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- Herausgeber: Carl Hanser Verlag GmbH & Co. KG
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
- Veröffentlichungsjahr: 2021
“Plastics Industry 4.0” provides a sophisticated insight into the development of the plastics industry in terms of digitalization and Industry 4.0, i.e. the Fourth Industrial Revolution. The background to these increasingly important topics is discussed along with provision of the prerequisite knowledge regarding process complexity and modeling as well as data acquisition to build the foundation of data driven digital processes. Furthermore, the facets of so-called cyber-physical systems including their key components and interfaces are discussed and illustrated using industrial application as well as scientific use cases.
Aimed at decision makers in the plastics industry, engineers in industry, including those in R&D and process and product development, as well as researchers and students in universities, this book provides the inspiration to connect with Plastics Industry 4.0, and thereby stimulate innovation in companies, processes, products, and research.
Contents:
- Introduction: Potentials, Benefits, and Challenges for Successful Implementation of Industry 4.0
- Data Acquisition and Process Monitoring as Enabler for Industry 4.0
- Cyber-Physical Systems
- Models and Artificial Intelligence
- Global Connectivity
- Digital Engineering
- Complex Value Chain
- Assistant Systems
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Christian HopmannMauritius Schmitz
Plastics Industry 4.0
Potentials and Applications in Plastics Technology
The Authors:
Prof. Dr.-Ing Christian HopmannHead of the Institute for Plastics Processing (IKV) at RWTH Aachen University, Aachen, Germany
Dipl.-Ing. Mauritius SchmitzScientific Director – Digitalisation and Industry 4.0, Institute of Plastics Processing (IKV) at RWTH Aachen University, Aachen, Germany
Distributed in the Americas by:Hanser Publications414 Walnut Street, Cincinnati, OH 45202 USAPhone: (800) 950-8977www.hanserpublications.com
Distributed in all other countries by:Carl Hanser VerlagPostfach 86 04 20, 81631 Munich, GermanyFax: +49 (89) 98 48 09www.hanser-fachbuch.de
The use of general descriptive names, trademarks, etc., in this publication, even if the former are not especially identified, is not to be taken as a sign that such names, as understood by the Trade Marks and Merchandise Marks Act, may accordingly be used freely by anyone. While the advice and information in this book are believed to be true and accurate at the date of going to press, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.
The final determination of the suitability of any information for the use contemplated for a given application remains the sole responsibility of the user.
Library of Congress Control Number: 2020946088
All rights reserved. No part of this book may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying or by any information storage and retrieval system, without permission in writing from the publisher.
© Carl Hanser Verlag, Munich 2021Editor: Dr. Mark SmithProduction Management: Jörg StrohbachCoverconcept: Marc Müller-Bremer, www.rebranding.de, MunichCoverdesign: Max Kostopoulos
ISBN: 978-1-56990-796-2E-Book ISBN: 978-1-56990-797-9ePub-ISBN: 978-1-56990-798-6
The digital age has just begun. Computer science is penetrating production and processing technology, digitization and 3D printing are disruptively changing the methodology of product development, and digital materials are attracting scientific interest. Biologically inspired computing supports humans in discovering patterns, relationships, and correlation in a vast amount of data, and new algorithms lead to improved and entirely new models of complex systems. Quantum computing will soon provide a substantial increase in computing power. It is inevitable and obvious that these technologies will affect plastics technology tremendously – and challenge engineers to discover opportunities that come along with those technologies in plastics technology and processing. The breakthrough of those technologies is just a matter of time – the race for innovation is yet open.
Digitization and Plastics Industry 4.0 are not ready-made solutions or products which may be chosen out of a catalogue. It is rather a sort of a mindset or a philosophy to entirely think plastics technology from a digital perspective to stimulate innovation. In this sense, this book provides stimuli and impulses to connect with Plastics Industry 4.0 and to develop strategies to tackle Plastics Industry 4.0 in a specific environment rather than just offering a list of tools that may be used in a plastics processing company. The reader may enjoy the diversity and versatility and take it as a source for inspiration.
The authors would like to thank Dr. Mark Smith from Hanser for persistent encouragement to write this book and for great support on this exciting journey. Great thanks go to the DFG – German Research Foundation, which provides considerable funding for the cluster of excellence “Internet of Production” at RWTH Aachen University and thus enables us to do the research on which this book is mainly built. We also thank all the colleagues contributing to and cooperating in this cluster of excellence. The cross-domain collaboration with excellent researchers is a rich source of inspiration and has been of invaluable importance for our research and this book. We also greatly thank the scientists at the IKV – Institute for Plastics Processing at RWTH Aachen University for excellent research in digitization and Plastics Industry 4.0 and for contributing to this book.
Aachen, October 2020Christian Hopmann
Mauritius Schmitz
Head of the Institute for Plastics Processing (IKV) at RWTH Aachen University, Aachen, Germany
University Professor Dr.-Ing. Christian Hopmann, born 1968, studied Mechanical Engineering with particular focus on Plastics Processing at RWTH Aachen University, Germany. Following his diploma in 1996, he received his doctoral degree (Dr.-Ing.) with a thesis on injection molding in 2000. From 2001 to 2004 he was Chief Engineer and Senior Vice Director of the institute.
After several years in the plastics processing industry, since 2011 he is Head of the IKV – Institute for Plastics Processing in Industry and Craft at RWTH Aachen University and Managing Director of the IKV’s Association of Sponsors. He also holds the Chair of Plastics Processing at the Faculty of Mechanical Engineering at RWTH Aachen University. In 2014 he received the innovation award of the federal state of North Rhine-Westfalia (NRW). Hopmann is Fellow of the Society of Plastics Engineers (SPE).
One of his core research interests is digitalization in plastics processing and Industry 4.0. Since 2019 he is one of the Principal Investigators in the Federal Cluster of Excellence “Internet of Production”, funded by the DFG. Under his lead, the IKV is building up a fully interconnected production environment according to the principles of a smart factory. The so called “Plastics Innovation Center 4.0” is funded by the federal state of North Rhine-Westphalia and the European Regional Development Fund (EFRE).
Dipl.-Ing. Mauritius SchmitzScientific Director – Digitalisation and Industry 4.0, Institute for Plastics Processing (IKV) at RWTH Aachen University, Aachen, Germany
Mauritius Schmitz, born in 1985, studied Computational Engineering Science with particular focus on structural mechanics and fluid dynamics at RWTH Aachen University. Following his diploma, in 2014 he joined the IKV as a project engineer for process simulation in the Department of Injection Moulding. In 2015 he became a doctoral student and head of the working group for Process Simulation in the Department of Injection Moulding, focusing on applications and further development of injection molding simulation. Furthermore, he focused on model predictive control strategies for novel mold tempering techniques, considering the local part properties during the injection molding process.
In 2019 Mauritius Schmitz joined the institute management as Scientific Director at the Institute for Plastics Processing. In this position he is responsible for the research and development in the field of digitalization and Industry 4.0 as well as the internationalization strategy of the institute. Since 2019, he is a Cluster Research Domain Coordinator in the research domain “Production Technology” of the Cluster of Excellence “Internet of Production”, funded by the DFG.
The ContributorsChapter 1
Dipl.-Ing. Mauritius Schmitz
Chapter 2
Pascal Bibow, M.Sc.; Katharina Hornberg, M.Sc.; Tobias Hohlweck, M.Sc.; Cemi E. Kahve, M.Sc.
Chapter 3
Pascal Bibow, M.Sc.
Chapter 4
Yannik Lockner, M.Sc.
Chapter 5
Dipl.-Ing. Mauritius Schmitz; Pascal Bibow, M.Sc.; Patrick Sapel, M.Sc.
Chapter 6
Jens Wipperfürth, M.Sc.; Maiko Ersch, M.Sc.; Dipl.-Ing. Thomas M. J. Gebhart; Simon Koch, M.Sc.; Jonathan Alms, M.Sc.; Christoph Zimmermann, M.Sc.; Steffen Verwaayen, M.Sc.; Jakob Onken, M.Sc.; Fabio Di Battista, M.Sc.; Yannik Lockner, M.Sc.; Dipl.-Ing. Mauritius Schmitz
Chapter 7
Simon Wurzbacher, M.Sc.; Patrick Sapel, M.Sc.; Thilo Köbel, M.Sc.
Chapter 8
Pascal Bibow, M.Sc.; Patrick Sapel, M.Sc.; Hanna Dornebusch, M.Sc.; Katharina Hornberg, M.Sc.
Contents
Title Page
Copyright
Contents
Preface
About the Authors
1 Introduction
1.1 Potentials and Benefits of Industry 4.0
1.2 Challenges for Successful Implementation of Industry 4.0
2 Data Acquisition and Process Monitoring as Enabler for Industry 4.0
2.1 The Necessity of Data Acquisition
2.1.1 Quality Control in the 1990s
2.1.2 Exemplary Fields of Application
2.2 Gaining Insights into the Process
2.2.1 Differentiation of Injection Molding Process Data
2.2.2 Economic Evaluation of the Injection Molding Process Based on Measurable Values
2.2.3 Process Data for Setup of a New Process
2.2.4 Process Control
2.2.4.1 Online Process Control
2.2.4.2 Process Control Concepts
2.3 Data Acquisition Methods
2.3.1 Material Properties for Digital Engineering
2.3.1.1 Thermal Properties of Plastic Melts
2.3.1.2 pvT-Behavior
2.3.1.3 Rheological Properties
2.3.1.4 Mechanical Properties
2.3.1.5 Applications of Data in Digital Engineering
2.3.2 Data Acquisition and Process Monitoring Methods
2.3.2.1 Temperature Measurement
2.3.2.2 Pressure Measurement
2.3.2.3 Electrical Pressure Measurement
2.3.2.4 Position Measurement
2.3.3 Humidity Measurement
2.3.4 Part Measurement
2.3.4.1 Part Measurement (Post-Mortem)
2.3.4.2 Optical Measurement
2.3.4.3 Tactile Measurement
2.3.5 Combination of Tactile and Optical Measurements
2.4 The Different Types of Quality Control
2.4.1 Offline Quality Control
2.4.2 Inline Quality Control
2.4.3 Online Quality Control
3 Cyber-Physical Systems
3.1 Computer Integrated Manufacturing as Conceptual Foundation for Cyber-Physical Production Systems
3.2 CPPS in Plastics Processing
3.3 Communication Capability of CPPS Components in Injection Molding
3.4 Planning and Realizing a CPPS in Plastics Processing
4 Models and Artificial Intelligence
4.1 Model Quality
4.2 Three Different Categories of Models
4.2.1 Physical Models
4.2.2 Knowledge-Based Systems
4.2.3 Artificial Intelligence
4.2.3.1 AI Modeling Methods
4.2.3.2 Artificial Neural Networks (ANNs)
4.2.3.3 AI Modeling Examples in the Plastics Industry
5 Global Connectivity
5.1 Data Availability
5.2 Data Management
5.3 IT Infrastructure
5.3.1 Cloud Computing
5.3.2 Edge Computing
5.3.3 Hybrid System in Plastics Processing
5.4 Machine and Data Interfaces
5.4.1 Digital I/O
5.4.2 Analog I/O
5.4.3 Serial Interfaces
5.5 Data Systems
5.5.1 Introduction
5.5.2 Need for Data Processing
5.5.3 Development of Data Systems
5.5.4 Enterprise Resource Planning
5.5.5 Manufacturing Execution System
5.5.6 ERP/MES in the Plastics Processing Industry
5.5.7 Requirements for ERP/MES in the Context of Industry 4.0
5.5.8 Developed Systems in Research
5.5.9 Used Systems in the Industry
5.5.9.1 SAP ERP
5.5.9.2 FEKOR MES
5.5.9.3 authenTIG
6 Digital Engineering
6.1 Introduction
6.1.1 Digital Materials
6.1.2 Material Modeling on the Nanoscopic Scale
6.1.3 Material Modeling on the Microscopic and Mesoscopic Scale
6.1.4 Material Models on the Macroscopic Scale
6.1.4.1 Isotropic Linear-Elastic Behavior
6.1.4.2 Orthotropic Linear-Elastic Behavior
6.1.4.3 Hyperelastic Behavior
6.1.4.4 Anisotropic Hyperelastic Behavior
6.1.4.5 Plastic Material Models
6.1.4.6 Viscoelasticity
6.1.4.7 Damage Model for Dynamic Load
6.2 Process Simulation
6.2.1 Setting up Injection Molding Simulation
6.2.2 Design and Optimization Using Injection Molding Simulation
6.3 Result Analysis and Mapping
6.3.1 Calculation of Mechanical Properties Based on Local Microstructure
6.3.2 Weld Lines
6.3.3 Elastomers: Considering Crosslinking Level in Structural Simulation
6.3.4 Thermoplastic Elastomers: Determination of Elastomer Particle Size
6.4 Part Simulation
6.5 Artificial Neural Networks in Virtual Process Development
7 Complex Value Chain
7.1 Introduction to Complex Value Chains
7.2 Shop Floor Management
7.2.1 Lean Management
7.2.2 Key Figures for Plastics Processing
7.2.3 Shop Floor Management in the Context of Industry 4.0
7.2.4 Asset Identification
7.2.4.1 Identification, Tracking, and Tracing of Assets
7.2.4.2 Technical Solutions of Asset Identification
7.2.4.3 Plastic-Related RFID Research Projects
7.2.5 Warehouse Management
7.2.6 Logistics 4.0
7.2.7 Equipment Management
7.3 Examples of Complex Value Chains in Plastics Processing
7.3.1 Model-Based Setup of Injection Molding Processes
7.3.2 Producing Multiple Variants in a Production Cell
8 Assistant Systems
8.1 Requirements and Functionalities Regarding Assistant Systems
8.2 Simulation-Based Assistance for Process Setup
8.3 Predictive Maintenance
8.3.1 Maintenance Routines
8.3.2 Predictive Maintenance in Injection Molding
8.3.2.1 Predictive Maintenance for Injection Molding Machines
8.3.2.2 Predictive Maintenance for Injection Molds
8.4 Augmented Reality and Virtual Reality as Visual Support
8.4.1 Definition and Demarcation of Terms
8.4.2 State of the Art
8.4.3 Industry 4.0 and Augmented Reality
8.5 Commercially Available Tools
8.5.1 Engel iQ Control Systems for Process Support in Injection Molding
8.5.2 ARBURG Continuous Quality Control with the CQC System
8.5.3 KraussMaffei Adaptive Process Control to Deal with Material Fluctuations
8.5.4 Sumitomo Enhanced Machine Efficiency with ActivePlus
8.5.5 Process Optimization with STASA QC
Figure 1.1First to Fourth Industrial Revolutions
The term “Industry 4.0” describes a holistic digitalization of industrial production to achieve improvement in terms of quality, efficiency, robustness, and flexibility. This shall be achieved using modern information infrastructure and communication techniques with the aim of creating intelligent self-organizing production systems. It is not only related to a single production machine or device, but aims for connection of all essential parts of the value chain and therefore enables a holistic optimization. Additionally, the interaction between humans, machines, production systems, logistics, and products is an essential component of Industry 4.0.
For Industry 4.0, four major design principles have been defined [1]:
Interconnection: People, machines, devices, and sensors are connected and communicate with each other.
Information transparency: Data, which is obtained by the interconnected devices throughout the production and along the value chain, is made available, where it delivers added value.
Technical assistance: Modern systems, which support humans either by assisted decision making using processed information or by taking over non-creative or unsafe tasks, are employed.
Decentralized decisions: Systems are enabled to fulfill tasks autonomously and only rely on centralized decision making if conflicting goals are present.
The term “Industry 4.0” originates from a report of an advisory committee to the German government, presented in 2013 [1]. Furthermore, it originates from a project within the high-tech strategy of the German government in the year 2014. It is also referred to as the “Fourth Industrial Revolution”, a term first introduced in 2015 by Klaus Schwab, chairman of the World Economic Forum. It refers to the consequent evolution of industrialization [2]. Thereby, the four stages of industrialization are stated as follows:
The First Industrial Revolution refers to the transition of hand production to machine-based production using steam power and water power between 1760 and 1840.
The Second Industrial Revolution was characterized by the introduction of logistic infrastructure like railroad and telegraph networks between 1871 and 1914. Being able to provide resources and information more efficiently and using electricity in production allowed a drastic economic growth and increase in productivity.
The Third Industrial Revolution, which occurred in the late 20th century, was mainly characterized by the introduction of electronic systems and micro-controllers into machine control systems, which extended the effects of the Second Industrial Revolution and allowed more efficient and well-controlled production.
Therefore, the Fourth Industrial Revolution defines the even further development of industrial production, extending the capabilities of production systems by enabling interconnection and developing more complex and flexible systems, which results in a smartification of the production. The benefit is higher quality and efficiency. This is to a great degree achieved by digitization, meaning that formerly analog information is transformed into digital information, enabling it to be reproducible, available anywhere, and automatically interpretable. Nevertheless, to a large extent it is achieved by digitalization, which extends digitization by the aspect of added value and transforming businesses or processes by innovative and creative new approaches instead of transferring traditional methods into a digital format. It is also referred to as “digital transformation”. Due to the rising complexity of production systems in the interconnected context, the direct benefits, which lead to increased efficiency, are manifold.
This book aims, on the one hand, to deliver basic knowledge about the acquisition and dependencies of data, industrial devices, processes, and digital infrastructures to provide a general understanding how various systems in the context of Industry 4.0 interact and how new systems can be designed and implemented. On the other hand, established as well as new applications in the field of plastics processing are illustrated to present benefits and potential new applications.
Since all applications rely on inputs in form of information, in Chapter 2 “Data Acquisition and Process Monitoring as Enabler for Industry 4.0”, the basic knowledge regarding data acquisition for industrial processes including knowledge about sensor systems is presented. Along with the understanding of the specific process, this data can be refined and used to implement beneficial systems such as, for example, process monitoring or optimization systems. In the following Chapter 3 “Cyber-Physical Systems”, the view is extended towards complex manufacturing environments and their digital representation. This chapter especially aims to convey methods for data clustering and defining efficient subsystems including internal and external communication. Hereafter, one of the most important aspects of Industry 4.0 is addressed in Chapter 4 “Models and Artificial Intelligence”. To be able to generate benefit from available data, a digital representation of the considered system is necessary to determine the correct response to arising problems or challenges. In this chapter, physical and analytical models as well as data-driven modeling methods are presented. In Chapter 5 “Global Connectivity”, the requirements and recommendations regarding a digital infrastructure are addressed considering data availability for various systems, starting at infrastructure concepts like centralized and decentralized production environments, going through industrial machine interfaces, and ending with manufacturing execution (MES) and warehouse systems.
In Chapter 6 “Digital Engineering”, concepts and methods for virtual representations of a system or its behavior are presented. These can range from dedicated simulation-based representations of a specific process to complex interconnected simulations or data-driven representations of processes or production systems using artificial intelligence. Since virtual representations rely on various pieces of information and additional benefit can be generated when including information from multiple stages of the value chain, in Chapter 7 “Complex Value Chain”, concepts of data acquisition and data management from adjacent areas of the shop floor are covered. Additionally, exemplary projects illustrating Industry 4.0 applications for increased efficiency and process flexibility are described.
Finally, in Chapter 8 “Assistance Systems”, I4.0 applications are presented that support humans in various tasks by providing reworked information directly at the work space.
1.1Potentials and Benefits of Industry 4.0Figure 1.2Potentials and benefits of using Industry 4.0 applications
Industry 4.0, as the “Fourth Industrial Revolution”, aims to increase efficiency and productivity and thus shift todayʼs production to one that is a more economic and of superior competitiveness. Several direct or indirect benefits have been mentioned in articles and the media and companies are putting enormous efforts in enhancing their knowledge of digitalization and Industry 4.0 applications. Studies have shown that around 80% of producing companies believe that Industry 4.0 is relevant for their business. Furthermore, almost 90% of these companies believe that I4.0 will gain even more relevance [3], and they are prepared to invest up to 5% of their budget into new I4.0 projects [4]. Some studies even predict an increase in productivity of up to 40% as a result of Industry 4.0 related technologies [5].
Considering the fields of interest when dealing and especially implementing Industry 4.0 concepts, data acquisition is the first step to consider. Acquired data delivers benefits directly when prepared or processed appropriately. Well-prepared key figures (see Chapter 7) help to quantify the current situation, provide feedback for necessary manual manipulation, or help to identify operations or processes which provide most potential for optimization. Even very simple digitization approaches like measuring the current status of a machine (for example by measuring the power consumption) enable real-time productivity measurement and help to quantify utilization or reveal optimization potential. Meanwhile, modern IoT (Internet of Things) devices, established databases like SQL [6, 7] databases, and streaming platforms like MQTT [8] have evolved to be extremely user-friendly and inexpensive. Using established data visualization platforms like Tableau (Tableau Software, CA, USA) or Grafana (Grafana Labs, Inc., New York), data can be visualized with minimal effort and IT knowledge [9, 10].
Data can also be understood in terms of knowledge; conserving data and making knowledge-based data available can for example improve failure analysis or error handling by providing reactions and results performed in previous cases, or improve a worker’s understanding by providing cross-department information like design data on the shop floor. This is nowadays achieved by using infrastructures and techniques which have been developed for server- and internet-based services to provide and interact with data, such that data can be made available almost anywhere without the need of specialized software.
Especially in the field of collaboration, modern digitization technologies can increase efficiency by enabling consistent work data and automation of standard workflows. This can be used in almost any operation along the value chain, such as automated material ordering when material stocks are running low, automated cost calculations for customer requests, or automated data provision from the sales department to the design department and production. Depending on the degree of connectivity, this concept can also be extended towards automated manufacturing or production setup, as already available in the 3D printing industry or illustrated in various different projects (see Chapter 7). This leads to another benefit of Industry 4.0: flexibilization of the production. By providing the necessary information along with the knowledge of the production system as quantifiable models (see Chapters 4 and 6), the setup process and determination of the suitable process parameters can be accelerated rapidly or even be automated (see Chapter 7). This leads to an increased ability of changing products or even whole processes more efficiently, leading to reduced costs and higher competitiveness.
As illustrated in Chapters 4 and 6, the used models can be extended using combined methods and modeling approaches, such that the quality of the model can be increased to such a level that it can be used to optimize processes or process chains very accurately. This enables closed-loop optimization systems, which allow process and part quality as well as process efficiency to be better controlled even in complex, multi-step production systems.
These possibilities furthermore allow a reduced need for physical work and presence of the employees on the shop floor, reducing the risk of injuries caused by heavy work or accidents. It also allows concepts like mobile office to be introduced to many other fields of manufacturing, which was not possible in the past and can enhance work-life balance. Employees that have performed monotonous or tedious tasks in the past can then perform additional different tasks utilizing knowledge and flexibility, and stimulating creativity of the individual person. Thereby, a deeper understanding of the manufacturing process and the dependencies for adjacent tasks can be generated.
Next to the aforementioned methods and benefits, which are all related to internal processes inside a specific facility or company, most of the concepts can be extended to communication and collaboration with other companies like suppliers, customers, or contractors. Using targeted information exchange, processes that have not been transparent in the past can nowadays be quantified reliably. Internal processes can thus be structured to optimally fit the given external conditions. Concepts like “just-in-time delivery”, which have been proven in the past, can be extended to multiple further applications.
Especially for rising complexity in products and their use as well as reuse, it is not sufficient to use information that is generated inside one facility or company to optimize the process chain efficiently. In rising fields of interest like recycling and circular economy, the most important and impactful measures can be implemented using cross-company information along with information from the product use. Digitization and Industry 4.0 can therefore be seen as necessary toolbox for many challenges that have to be addressed in the future. In the field of recycling, for example, one of the hardest challenges is to determine the type of the used material, including fillers and additives, to make it available in sufficient quality for reuse. This can either be achieved by elaborate and expensive sorting machines and effort or by tracking the content of the product throughout its life cycle. To enable this efficiency, reliable acquisition of data and tracking as well as delivering suitable access to this data are necessary.
As illustrated before, most of the benefit inside and across companies is generated by the right utilization of data. This illustrates that the actual value of data gets more and more important. Additionally, especially when exchanging information outside the company, the most important question is: How should the value of data be assessed?
Next to the benefit generated internally, data can also be provided to other companies or customers to generate benefit for them. This can be inside the value chain in which a company is participating, but can also be used for other companies or processes. Modern approaches for example allow the use of process data to accelerate the setup of new similar processes (see Chapter 6), such that providing data to even unknown companies is conceivable. To enable this, novel and reliable methods for data exchange and data anonymization are necessary, which enable new services and business models. This is an ongoing focus of research.
1.2Challenges for Successful Implementation of Industry 4.0In the previous section, the potential benefits were described, raising the question of why Industry 4.0 applications are not yet commonly used in current production environments.
While there are many digital applications available on the market in specialized areas like automated warehouse systems, MES systems and automation systems (see Figure 1.3), most of these systems are focused on specialized or separated tasks, while not offering easy integration in existing environments or other systems suitable for collaboration. This is a major drawback, since most benefit can be achieved by considering the whole production or even the entire value chain.
Figure 1.3Available Industry 4.0 applications along the value chain
There are two major reasons for this. One the one hand, system providers are aiming to establish their platform as a standard, enabling the development and sale of further proprietary products or the offering of additional services in terms of data integration or interfaces. One the other hand, in the field of production systems there are no suitable industry-wide standardized specifications and protocols available that can easily be integrated with a variety of different data sources and database systems. Fortunately, many standards and specifications are currently in development, such that interoperability will improve drastically in the future. This will allow a much easier and faster integration of these systems and therefore reduce the efforts necessary for implementation. Thereby, data-driven systems will also become more interchangeable in the future and, therefore, the system providers will have to compete even more, resulting in lower prices. One of the already established standards for machine communication in the field of Industry 4.0 is OPC-UA. One important aspect of OPC-UA is the separation of the data layer (which is accessible by external systems) and the bus-level of the machine control, allowing a data exchange without the risk of interfering with the machine control. It furthermore implements security and encryption mechanisms allowing a secure data access. The most compelling aspect of OPC-UA is the ability to browse the namespace, allowing the identification of all available information. Therefore, an increased level of data transparency and reliability is given, making it easy and affordable to access the data. Also on the higher level of data structuring and data interchange new standards are developed to unify interfaces and increasing efficiency for implementation, such that efforts and costs for implementation will be reduced [11].
Another challenge for Industry 4.0 implementation is the missing awareness about both possible applications and the necessity for implementation. The awareness of possible applications can either be generated by exemplary projects (as illustrated in Chapter 7) or by gaining a general knowledge about the possibilities of Industry 4.0 systems and therefore the transfer to new applications. The necessity for such solutions finally arises due to the identification of inefficient processes that cannot be addressed individually or by the need to stay competitive in the individual industry.
The aforementioned challenges are closely correlated to the knowledge available about the existing solutions as well as the background knowledge which is necessary to estimate the possibilities of integration and the amount of effort and thus costs of the implementation. Since Industry 4.0 strongly relies on interdisciplinary knowledge in the fields of informatics, modeling/mathematics, electrical engineering, and production engineering, it is a difficult task to coordinate these skills or to gain this knowledge. While the development of new methods is progressing, it can be expected that the necessity of a deep understanding in each of these fields decreases. This can be achieved by increasing the degree of automation and the automated handling of errors within each system. Nevertheless, an interdisciplinary skillset which allows an understanding of the overall dependencies is becoming more and more important. Furthermore, the necessary skillset varies, depending on the field of application, whether it be in the strategic decision making, in the project management, or at the operational level. This is a challenge, which has to be approached by education sector.
Furthermore, developing new concepts to interact with Industry 4.0 based systems is necessary, since one cannot rely on the above-mentioned skillset for operating such systems and granting acceptance of these modern approaches. A great challenge therefore is to design Human Machine Interfaces (HMI) that are capable of delivering highly complex content in a simplified und understandable manner, while allowing transparency. Next to modern approaches like augmented reality, there is still a huge potential and need for new innovative concepts that enable a more natural interaction with complex digital systems.
The following chapters are designed to cover most of important aspects of Industry 4.0 and deliver information for enabling a general understanding of the overall dependencies and structures necessary for successful acquisition, development, and implementation of Industry 4.0 applications. Thereby, the reader should be enabled to identify individual challenges and correspond to these using the right methods.
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The most challenging tasks for an implementation of the self-optimization systems are on the one hand the identification of appropriate model-based optimization strategies and on the other hand the provision of required data from the process provided by the used sensors. [1]
Industry 4.0 claims to enable smartification of production systems through digitization and an enhanced introduction of information technology to traditional manufacturing processes. As the possibilities in production automation emerge, data becomes a valuable resource of the digital era. Therefore, as stated by Klocke et al., not only developing new ways to analyze data but also provision of the right data at the right time reflect major challenges of today’s research activities. However, advances in sensor technology and utilization of sensor data for process control have been in focus for a long time and can be expected to be the basis for current developments in process automation and digitization.
The origin of the data and the purpose of its acquisition determine whether it serves as valuable information to model e. g. an injection molding process in order to optimize the cycle time or to just monitor a continuous production with calculated key performance indicators. On the one hand, quality data like dimensions or part weight can be gathered to get information about the final part quality. On the other hand, machine data describes the dynamic machine behavior, such as screw movement or hydraulic pressure. The link between both, from machine data to the final part quality, is made through process data that describes the filling process inside the mold, e. g. by the cavity pressure.
In the Cluster of Excellence Integrative Production Technology for High-Wage Countries, for example, a model-based self-optimization algorithm has been developed that optimizes the operating point of an injection molding production based on the correlation of pressure, temperature, and specific volume (pvT-behavior) of the plastic melt (Figure 2.1). The ideal operating point therefore is determined by ensuring a constant specific volume during cooling in the holding pressure phase [2]. The model-based self-optimization utilizes the pvT-behavior as quality model to link cavity pressure and melt temperature as process data to the resulting quality data, a constant specific volume. For subsequent pvT-optimization, a voltage Ucontrol enables the control of the servo-inverter and the hydraulic valve to adjust the screw movement during the injection and holding pressure phase with respect to the demanded reference course of the cavity pressure pmold,ref. An appropriate process model therefore needs to correlate the machine parameters to the process data. However, disturbances like temperature variations of plastic melt and mold surface or viscosity fluctuations of the material affect the process behavior and impede a stable process. The model-based self-optimization compensates for these influences, for example by applying a sensor-actuator system as a model-predictive process control [2].
Figure 2.1Data types in injection molding and how they interact in a self-optimization algorithm [2]
A quality model like the pvT-behavior links process and quality data. Subsequently, to optimize part quality, process data needs to be gathered and correlated to specific quality characteristics in injection molding. Whereas the filling process right inside the mold including the holding-pressure and cooling phase provides the most significant information to model the injection molding process with respect to a demanded part quality, the mold itself does not always offer the possibility to communicate this information. The mold therefore needs to have sensors to gather and transfer data about cavity pressure and temperature. For a single-cavity mold, for example, two combined piezoelectric pressure-temperature sensors, one near and one far from the gate, enable the monitoring of the flow path proceeding at each increment in time.
A process model like an Artificial Neural Network (ANN) or a simple model based on fluid mechanics with respect to the rheological behavior of the plastic melt correlates machine data and process data. Everything that happens inside the mold and that is measured as physical parameters describing the melt front velocity or a temperature distribution over the flow path is the result of mechanical movements that initiate a volume flow. Therefore, data about screw movements, screw torque, or the hydraulic pressure has to be gathered with respect to the resulting process data. Enhancing production automation and using digitization for realizing fully automated self-optimization algorithms as shown in Figure 2.1 requires a comprehensive view on the production system and a profound understanding of it as only machine parameters can be controlled directly. However, even with the possibility to calculate the control signal Ucontrol to realize a cavity pressure pmold that equals the cavity pressure reference course pmold,ref as described above, environmental fluctuations cannot be compensated completely and will always affect the production process.
Data from production management and logistics open up another different perspective. Considering more than one machine or production cell raises complexity to a completely new level as not only interactions between machine, process, and quality data have to be respected. In a Complex Value Chain, often several production cells interact with each other and human operators to fulfill a production order in a dynamic and efficient manner. Moreover, collaboration between different domains working on the same product or production order impedes optimization of production structures along the stages in order processing with respect to resource efficiency, profitability, or productivity.
To track the activities in a Complex Value Chain, the production order often is the only common element in a cross-domain and multi-process perspective. Information systems like an Enterprise Resource Planning (ERP) or a Manufacturing Execution System (MES) act as central data and information handling systems. Therefore, data gets centralized so that several domains get access with respect to their individual viewpoint such as in accounting, order processing, or material supply. The production order thereby acts as the one element to link every piece of information from production, assembly, or quality assurance. However, due to the necessity of making data and information accessible by a central information system, a consistent and persistent connectivity to field devices becomes a fundamental requirement in a production environment.
Standardized interfaces are expected to serve as a solution to realize a consistent interconnection between several devices and information systems. Especially due to various meta-standards in automation across different industries and upcoming new efficient communication protocols, choosing the right infrastructure becomes an increasingly complex task. Thereby, setting up the right infrastructure for individual Industry 4.0 applications is the source of great efforts and many discussions in the plastics processing industry.
Conclusively, data acquisition presents itself as an important and encompassing but necessary task when it comes to digitization of production processes. Therefore, it is not only the question of where to gather data (part, process, or machine) that is in focus, but also which technology is used as communication interface or in which information system it should be made accessible. Engineers as well as basic operators need to comprehensively understand the process and its physical boundary conditions to utilize accessible sensor technology for measuring the desired data and getting purposeful information about how to optimize and control the process. Data acquisition is furthermore a primary precondition of all data-driven applications and cybernetic process models. However, production technology tends to be so complex in structuring its assets and modeling, for example, the physical background of casting processes, that data-driven models alone will not provide sufficient solutions. The former Cluster of Excellence Integrated Production Technology for High-Wage Countries at RWTH Aachen University concludes that in complex, socio-technical production systems a combined approach of cybernetic models of data scientists and deterministic models of engineers enable an integrative comprehension (Figure 2.2). Therefore, cybernetic models support handling complexity and uncovering unknown phenomena and structures, whereas deterministic models reduce the complexity and identify subsystems that can be described by physical laws or physically motivated models [3].
Figure 2.2Deterministic and cybernetic models combined enable integrative comprehension and learning based on production data [3]
Data acquisition is important to produce high-quality plastic parts. The use of data enables an improvement in the following areas:
Control of process stability enables a fast reaction to process disturbances
Optimization of the process: analyzing process data to achieve a higher part quality
The injection molding process is always influenced by disturbances as stated above. They can arise unpredictably and are influenced by many different factors. These include the machine and its wear and tear, the material characteristics, the environmental conditions like temperature and humidity of the air, and the practical knowledge of the operating staff. Varying melt and mold temperatures, for example, might result from an unstable thermal state during ramp-up or after (unplanned) interruptions in production. However, also control fluctuations and disturbances in the heating system can cause varying temperature values. Moreover, different material batches or the use of recycled material induce viscosity fluctuations that influence the cavity pressure course [2]. The process disturbances can often be compensated by an adjustment of the machine parameters. With the acquisition of data, the machine operator can react much faster and a lower rejection rate can result due to more information about the actual process and better process understanding. Without the process data, the effect of the process disturbances might be seen only during final inspection, so the recognition of a process adaption is noticed too late. This results in cost and time deficits.
Process data can also be used for the optimization of the process. Therefore, the data from machine and sensors has to be analyzed during the process. If irregularities are detected, the process should be adapted to avoid parts that do not meet the predefined quality, which leads to a higher process stability. The data can also support the machine operator in sampling a mold or starting a process (see Chapter 8), so start-of-production can be accelerated significantly.
The data can also be used for an offline optimization to achieve a higher part quality and precision during production. Process data has to be analyzed offline during series production, so required changes of the machine parameters can be made afterwards to achieve a higher efficiency than with trial-and-error process setup. Besides the part quality, there are many other aspects that can be optimized by data analytics, such as energy consumption of the machine and the process productivity or the Overall Equipment Efficiency (OEE). The use of data almost always results in a higher quality of the manufactured parts. However, the process can only be adapted after a time delay with offline optimization approaches.
Despite the mentioned application possibilities of offline optimization, online optimization enables the adaption of the process settings as soon as deviations in part quality occur. In the following, some examples will show the importance of process data for an improvement of the injection molding process.
2.1.1Quality Control in the 1990sSince the 1990s, extensive research in the field of usage and benefits of data has been carried out. This has included machine data, process data, and logistic data. Since then, the requirements have changed because the necessity of process data has been recognized, and the acquisition of process data has becomes increasingly more a subject of great significance. Due to the enhancement of connectivity of the entire production process and use of intelligent assistance systems, the usability of process data will become more sophisticated.
The statistical and precise evaluation of quality parameters such as weight and geometry has increased in importance since the 1990s [4]. The testing methods are mostly manual tests with calipers, scales, and individually constructed testing devices. Inspection of incoming goods is usually carried out visually. Computer-aided quality control has only rarely been employed in injection molding facilities.
There are diverse reasons for the persisting manual quality control at the time. The quality management did not recognize the potential of digital control methods as well as the automated analysis of measurement data. Furthermore, the quality costs could not be calculated correctly due to a lack of comprehensive and objective calculations, so the partial costs of quality control did not correspond to the reality. The most significant aspect is that the cost advantages of a quality control during production were not recognized. Quality control costs are between 5% and 25% of sales, but the costs of rejected parts due to inadmissible batch fluctuations or a delayed readjustment of the process can be even higher. If the output control is not sufficient, it will cause high expenses, since recalls may have to be performed.
The main requirements for the efficient use of quality control in injection molding are:
data acquisition, processing, and evaluation with the challenge of machines and control units of different manufacturers
online integration of the test devices
integration of material processing (regranulating, mixing, drying)
lot tracking
The mentioned aspects of digitization were not implemented in 1990. Many functionalities of computer-aided quality control were just demands for the future, which are state-of-the-art nowadays. In 1990, the Institute for Plastics Processing in Industry and Craft (IKV) at RWTH Aachen University executed a survey about the industrial requirements for computer-aided quality control (Table 2.1) [4]:
Table 2.1 Industrial Requirements for a Computer-Aided Quality Control in 1990 [4]
Degree of necessity
Requirement
Must be fulfilled
CAQ software
Statistical process control
Statistical quality monitoring
Support for random sampling
Control chart management
Supplier evaluation
Support with measurement data acquisition
Acquisition of measurement costs
Communication to other control systems
Administration of check tables according to DIN 40090/ ISO 3951
Administration of a defect-(key)-catalog
Recording and processing of quality costs
Cost division by error correction costs, prevention costs, and inspection costs
Cost division by individual cost centers
Management of different experimental designs
CAQ hardware: running capability on PC networks for cross-linking of the whole company
CAQ operation areas
Receiving area of material and goods
Production area
Shipping area
Supplier evaluation
Should be fulfilled
Interfaces
V 24/RS 232
Ethernet
EUROMAP 15
Support of variable and attributive measurements
CAQ software
100% Quality control
Statistical process control (e. g. Quality Control Chart)
Statistical quality monitoring
Automatic creation of a quality control chart
Easy creation of test results and test reports
Fulfillment desirable
CAQ software
Interface to other CAx systems
Differentiation between types of experimental designs
3D presentation of test results
Usability
Good user guidance
Data processing and data transfer of test results and historical quality data
Selection tests (1–100%)
Fulfillment uninteresting
CAQ operation areas
Measuring laboratory
Physical laboratory
Future demands
Test equipment monitoring
Cataloging of resources, errors and measures
Information exchange of CAQ with CAD and PPS systems
Administration of a machine-(key)-catalog via CAQ system
Rarely requested
Running capability on supervised host systems
The requirements from Table 2.1 could only be fulfilled insufficiently in 1990. However, most of them are state-of-the-art for CAQ systems nowadays. The consideration of previous requirements demonstrates the massive development of CAQ systems and the soft- and hardware solutions.
2.1.2Exemplary Fields of ApplicationOne of the fields where quality control has been established is in the field of optical online quality control to achieve a high quality of produced parts. This online control technology can operate in a contactless, objectively consistent, and reproducible manner, and should be integrated into fast-running serial processes. The application areas were seen in control of printed design and testing of sealing components via image processing with a high-speed camera system. The idea could not be implemented due to a limitation of the maximum recording time of the camera system and processing capacity in image recognition [5].
Today’s developments in camera technology allow the usage of online optical quality control in a variety of areas. Cameras that work in the visible light spectrum fulfill tasks like color and pattern recognition and measurement control. Furthermore, new applications in identification and traceability of parts and components are being exploited [6, 7]. Nowadays, specifically developed infrared-camera systems can detect errors in parts by the use of active thermography [8].
Besides camera-based quality control, different sensor types were evaluated for application in injection molding processes in 1990. For example, dielectrometry measurement is used regularly in processing of thermoset molding materials for process control. It was already investigated in 1992 to display the benefits of the use of dielectric sensors. The curing of the thermoset can be controlled online to determine the correct demolding time. This is made possible by the measurement of the changing electrical characteristics of the polymer. The material is cured when the conductivity measured by the sensor reaches a constant value. Characteristics of the conductance curve also reflect the cross-linking process [9].
Furthermore, the temperature curve of the molded part can be reduced to key indicators, like an average or gradient. These key factors can be used to create process models to determine the part quality and process control [10]. The use of thermocouples and infrared sensors has been examined in various projects to determine the polymer temperature in the mold during the injection molding process.
Another tool that is beneficial for part quality improvement is process simulation. The benefit of using simulation data was recognized in 1996. Using simulation data along with measured data enables a qualitative evaluation of the process and part quality. The comparison of experimental results with thermocouples and simulation results shows a deviation of the measured heat flux. The experimental values are lower because the thermocouple reacts sluggishly on temperature changes due to the sensor mass. The detected deviations of the simulation results can be used for an improvement of the accuracy of simulative analysis. This demonstrates the limited usability of both: simulation data and sensor data [11]. Simulation results are subject to the accuracy of material data and validation of the results. For more accurate temperature data in the mold, the use of infrared sensors in the mold was examined [10]. Infrared sensors have been examined in other continuing projects [12].
Online process control is also used for gas assisted injection molding via ultrasonic examination. The rheological characteristics of the polymer are highly important for the part quality in this manufacturing process. The runtime from the ultrasonic signal is influenced by the polymer melt properties, so the temporal change of the polymer melt properties can be monitored with ultrasonic sensors. This data can be used to evaluate quality characteristics such as the degree of melt stagnation at the switchover point. Furthermore, echoes are created at phase boundaries, so air gaps between the cured part and the cavity are detectable. For example, the sensors are placed under the cavity surface to produce parts without any marks [13].
The use of ultrasonic signals was investigated in further projects regarding to the regular injection molding process. The aim was to find a process model to describe the correlation between the ultrasonic signals and sink marks of the manufactured part. Significant characteristics of the ultrasonic signal are the time of part separation from the mold and the time of glass transition. The temperature and pressure curves should also be derived via characteristics of the ultrasonic signal. The results show a good correlation between the calculated and the measured values of the sink mark geometry [14].
A control of shrinkage and the part weight was investigated by Schreiber and Reßmann [15, 16]. For this approach the pvT-behavior of the polymer was deployed as quality model. The aim was to achieve a reproducible specific volume at the end of the injection molding process as soon as ambient pressure is reached in the cavity. The mold temperature was measured by a thermocouple and the part temperature was calculated using analytical formulation of the heat equation. The optimal course of the cavity pressure was derived from this measurement and used for a cavity pressure control strategy in the packing phase. Thereby, a reproducible shrinkage can be achieved. For more detailed information, see Section 2.2.4.2.
2.2Gaining Insights into the ProcessThe behavior of the injection molding process is complex, so the process is difficult to retrace without comprehensive process knowledge, which has been collected over several years. The process adaption regarding occurring disturbances and the analysis of the process values without any recording of data rarely leads to the desired process stability and part quality. The appropriate analysis of process data to gain inside into process behavior requires inter alia a differentiation between the types of process data.
The different types of process data can be categorized into machine values, process values and quality values. A description of the respective value type is given in Section 2.2.1. The values also describe the efficiency of the injection molding process by analyzing the measurement data. The data, which can be used to describe the effectiveness is presented in Section 2.2.2. But there are further application areas for process data. It can also be used for process setup to achieve a sufficient part quality in a short time period (Section 2.2.3). The highest benefit of process data can be achieved by an online process control. Therefore, the process quality and traceability of quality parameters play an important role. The advantages and possibilities of this concept are described in Section 2.2.4.
2.2.1Differentiation of Injection Molding Process DataThere are three main types of parameters:
Machine parameters
Process parameters
Quality parameters
The correlation of these values to the part quality can be direct or indirect, depending on the value type. Examples of the different data types are given in Figure 2.3.
Figure 2.3The different types of data
Machine data has the weakest correlation to the molded parts’ quality. Disturbance variables influence the process behavior to such an extent that only qualitative correlations can be made. Machine parameters are directly displayed at the machine’s control unit. The connection between process parameters and the part quality is much stronger. Some of the disturbances are eliminated by a consideration of the process data, especially sensor data from the mold. The other disturbances can be taken into consideration by using a quality model. Quality parameters enable a direct and quantitative description of the part quality. Their relevance varies according to the customer’s requirements.
Process fluctuations and other disturbances can occur at anytime in such a severity that the part quality changes even though the machine parameters are constant. Therefore, the correlation between machine data and the part quality is often not very distinctively.
The injection molding process is adapted by hundreds of machine parameters, which can be set individually. Nevertheless, the highest impact on the process is given by just a few key variables which lead to huge variations of the part quality. These parameters are often used for an adaption of the process if process changes are measured and for design of experiments to determine a good setting of the machine parameters. The importance of each variable can be analyzed by a sIgnificance analysis via regression models or neural networks. The most important machine parameters on part quality are:
Injection velocity
Switchover point
Packing pressure
Packing time
Cooling time
Barrel temperature
Mold temperature
The injection velocity strongly influences the part quality, especially the mechanical and the optical properties [17]. When determining the injection velocity profiles, it should be ensured that the speed of the flow front is kept constant during injection for a constant shear rate. The switchover point between injection phase and packing phase can lead to quality losses and rejects [18]. A too early switchover between the two phases leads to a brief standstill of the flow front. This can result in anisotropic orientations on the part surface, switchover marks and sink marks [19]. A late switchover results in residual stresses [20], burrs, and floating sinks, which can lead to mold damage if overspraying occurs for a longer period of time. Burrs additionally lead to demolding problems in case of too low ejection forces [19, 21]. The right switchover time changes during production, so an adaption of machine parameters is unavoidable and should be performed frequently [22].
The packing pressure and time are also a key variable because they influence the amount of polymer melt in the cavity to compensate shrinkage of the polymer melt. Therefore, it has a high impact on part weight and dimensions. Polymer melt is continuously pressed in the cavity until the sealing point or packing time is reached.
The cooling time starts with the injection phase and should enable an acceptable demolding temperature for dimensional stability of the molded part. A residual cooling time is usually needed after the packing phase, so a minimal cooling time is a frequent optimization target [19].
About one-third of the contributed heat for plasticizing of the polymer is provided by the barrel heating system. The needed barrel temperature has to be chosen according to the processed material. It also has to be adapted to the material output because it influences the thermal history and viscosity of the melt which has an impact on the whole part quality [17]. A higher material output desires higher barrel temperatures as well as a low material output requires lower barrel temperatures.
An inhomogeneous tempered mold leads to residual stress and distortion. Furthermore, the part surface can be affected negative, which becomes visible by gloss differences and matte areas [19]. Surface quality and mechanical properties are increased by a higher mold temperature but high mold temperature also lead to long cycle times [23].
The transmission behavior from machine data to process data varies due to changing fluctuations like the environment temperature and humidity, which affect the process and lead to a changing part quality. Therefore, the process is insufficiently described by the machine data, so the process parameters should be considered. Process data also has a strong correlation to the part quality and can be used inter alia for process control and the automatic selection of rejected parts. Exemplary process parameters are described in the following, such as:
Melt temperature in the cavity
Cavity pressure
Shear velocity
The melt temperature is affected by the barrel temperature, the inner friction of the material and the shear heating of the polymer. It greatly affects the polymer viscosity and has to be adjusted for a correct filling of the mold. If the melt temperature is too high, the material degrades. On the other hand, the mold cannot be filled completely if the melt temperature is too low because of an increasing viscosity of the polymer [19].
Cavity pressure is one of the most important process parameters and often used for process and quality control. Inner and outer quality characteristics are highly dependent of the cavity pressure, which enables a deliberately affection of quality parameters. Defect parts can be detected during the injection molding cycle for an automatic part separation [24]. The optimal cavity pressure curve is dependent on machine, material and mold and has to be analyzed individually. Important key figures are the integral of the pressure curve in injection and packing phase as well as the maximum cavity pressure. For instance, the maximum cavity pressure has an increasing significance for the part quality with a decreasing part thickness, so an exact process control, especially at the switchover point, is essential [25].
The local shear velocity is an important value for the injection phase. It influences the formation of surface layer orientations, which influences the mechanical part properties [26].
The quality parameters are divided in the main categories mechanical, inner, optical, electrical and economic properties as well as geometry. These are the target values for quality assurance. A detailed listing is given in Table 2.2. A detailed description is given in related literature [19, 27].
Table 2.2 Quality Parameters for an Evaluation of the Part Quality
Category
Properties
Geometry
dimensionsshrinkage and delaysurface texture
Mechanical properties
tensile strengthflexural strengthelasticity modulusnotch impact strength
Inner properties
specific volumemolecular mass distributionsorientations
Optical properties
shinecolortransparencyhomogeneity
Electrical properties
specific resistancedielectric constant
Economic properties
cycle timeenergy consumption
The importance of the different quality parameters depends on the application area of the molded part. Major fields of application are medical, construction, automotive or packaging industry but there are also large requirement differences within those industries. In addition, some of the quality features are dependent of one another, which often necessitates a compromise for an optimal combination of the characteristics. The best fulfillment of the specific set of quality parameters is always the aim of a process optimization.
2.2.2Economic Evaluation of the Injection Molding Process Based on Measurable ValuesMany relevant values exist for an evaluation of the economic efficiency of the injection molding process. The composition of the costs of an injection molded part is shown in Figure 2.4.
Figure 2.4Production costs for a polyamide injection molded part (180 g; 180,000 p. a.)
The material costs of 55% cannot be influenced a lot because the polymer type is already defined by the product development but one considerable aspect of material costs is the use of recycled polymer. If the product quality meets the requirements when using recycled material, the production waste of sprues and rejected parts can be used directly for the production of new parts (internal recycling). The costs of recycled granulate are often higher than new material e. g. for PET caused by high recycling costs. Recycling material, which is sorted and purified by material recovery facilities, is only profitable if the sales price of the molded parts is increased. This is often acceptable because the environmental awareness of customers has increased. Nowadays, the material price is highly influenced by the oil price. The correlation between e. g. PET price and oil price is nearly linear, so the profitability of recycled polymer changes over time [28].
Besides the costs for injection molding machine acquisition and maintenance, the energy consumption is important for production costs. These aspects have a collective amount of 13%. When considering the energy consumption of the machine, the driving concept (electric, hybrid or hydraulic) plays an important role. For energy efficiency, an injection molding machine has to operate with the needed power only when required. The energy efficiency of an electrical injection molding machine is higher than the efficiency of a hydraulic injection molding machine but the machine costs are higher. The development of hydraulic injection molding machines already reduces the energy consumption of the machine but it is still high in comparison with electric and hybrid machines [29, 30].
