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Procedia Manufacturing 00 (2018) 000–000 Procedia Manufacturing 24 (2018) 40–47
ProcediaConference Manufacturing 00on (2017) 000–000 4th International System-Integrated Intelligence
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Application Scenarios ofSystem-Integrated Artificial Intelligence 4th International Conference on Intelligence in Electric Drives Production Application Scenarios of Artificial Intelligence a, a a Conferencea2017, MESIC a Manufacturing 28-30 June A. MayrEngineering *, M. Weigelt , M.International Masuch , M.Production Meiners , F. Hüttela, J.2017, Franke inSociety Electric Drives 2017, Vigo (Pontevedra), Spain a
Institute for Factory Automation and Production Systems (FAPS), Friedrich-Alexander University Erlangen-Nuremberg (FAU), a, a a Nuremberg, Germany a a a Fuerther Str. 246b, 90429
A. Mayr *, M. Weigelt , M. Masuch , M. Meiners , F. Hüttel , J. Franke
Costing models for capacity optimization in Industry 4.0: Trade-off Institute for Factory Automation and Production Systems (FAPS), Friedrich-Alexander University Erlangen-Nuremberg (FAU), Str. 246b, 90429 Germany between usedFuerther capacity andNuremberg, operational efficiency a
Abstract
Santana Afonso used , A. Zanin , R. Wernke Artificial intelligence (AI) is A. the overall term ,forP.technologies to build intelligent systems, no matter whether utilized in an Abstract industrial or private environment. However, hardly any AI-based approaches have been proposed for the increasingly important a University of Minho, 4800-058 Guimarães, Portugal electric drives production yet. By identifyingbUnochapecó, and presenting exemplary application scenarios for knowledge-based systems (KBS) 89809-000 Chapecó, SC, Brazil Artificial intelligence is the paper overallserves term for used build research intelligentinsystems, no matter whether utilized an and machine learning (AI) (ML), as atechnologies starting point fortofurther the respective fields. Among others,in the industrial private environment. However, hardly anysuited AI-based approachesthe have been proposed fordrives the increasingly systematicoroverview reveals that KBS are especially for supporting planning of electric productionimportant systems, electric production yet. By identifying and presenting exemplary for knowledge-based systems (KBS) whereasdrives ML-based approaches have great potential for optimizing singleapplication productionscenarios processes. and machine learning (ML), the paper serves as a starting point for further research in the respective fields. Among others, the Abstract systematic overview reveals that KBS are especially suited for supporting the planning of electric drives production systems, whereas ML-based approaches have great for optimizing single production processes. © 2018 the The Authors. Published by Elsevier Ltd. Under concept of "Industry 4.0",potential production processes will be pushed to be increasingly interconnected, This is an open access article under the CC BY-NC-ND license ( https://creativecommons.org/licenses/by-nc-nd/4.0/ ) information based on a real time basis and, necessarily, much more efficient. In this context, capacity optimization © 2018 The Authors. Published by Elsevier B.V. Selection and peer-review under responsibility of the scientific committee of the 4th International Conference on System-Integrated goes beyond traditional aim ofthecapacity maximization, contributing also for organization’s profitability and value. This is an openthe access article under CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) © 2018 The Authors. Published by Elsevier Ltd. Intelligence. Peer-review under responsibilityand of the scientific committee of the 4th Internationalsuggest Conference on System-Integrated Indeed, lean management continuous improvement approaches capacity optimization Intelligence. instead of This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) maximization. The study of capacity optimization and costing models is an important research topic that deserves Selection and peer-review under responsibilitysystem; of the scientific committee of the systems; 4th International Conference on System-Integrated Keywords: artificial intelligence; knowledge-based machine learning; intelligent electric drives production contributions from both the practical and theoretical perspectives. This paper presents and discusses a mathematical Intelligence. a
a,*
b
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model for capacity management based on different costing models (ABC and TDABC). A generic model has been developed and it intelligence; was used to analyze idle capacity and tolearning; designintelligent strategies towards the drives maximization Keywords: artificial knowledge-based system; machine systems; electric production of organization’s 1. Introduction value. The trade-off capacity maximization vs operational efficiency is highlighted and it is shown that capacity optimization might hide operational inefficiency. Artificial intelligence (AI) has long been considered as a key technology in data driven industries and is increasingly 1. Introduction © 2017 The Authors. Published by Elsevier B.V. personal assistants or autonomous driving. With the megatrend Industry being used in applications such as intelligent Peer-review under responsibility of the scientific the manufacturing Manufacturing Engineering Society International 4.0 at the latest, AI technologies also find committee their way ofinto industry. However, since AI Conference draws upon 2017. Artificial intelligence (AI) has long been considered as a key technology in data driven industries and is increasingly being used in applications such as intelligent personal assistants or autonomous driving. With the megatrend Industry Keywords: Models; TDABC; Capacity Management; Idleinto Capacity; Operational Efficiency 4.0 at theCost latest, AIABC; technologies also find their way manufacturing industry. However, since AI draws upon * Corresponding author. Tel.: +49 911 5302-9064; fax: +49 911 5302-9070. address:
[email protected] 1.E-mail Introduction 2351-9789 © 2018 Thecapacity Authors. Published by Elsevier Ltd. * The Corresponding author. Tel.: +49 5302-9064; fax: +49 911 5302-9070. cost of idle is911 a fundamental information for companies and their management of extreme importance ThisE-mail is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) address:
[email protected] in modern production systems. In general, it is defined as unused capacity or production potential and can be measured Selection and peer-review under responsibility of the scientific committee of the 4th International Conference on System-Integrated Intelligence. in several ways: tons of production, available hours of manufacturing, etc. The management of the idle capacity 2351-9789 © 2018 The Authors. Published by Elsevier Ltd. * Paulo Tel.:article +351 253 510 +351 253license 604 741 This is an Afonso. open access under the761; CC fax: BY-NC-ND (https://creativecommons.org/licenses/by-nc-nd/4.0/) E-mail address:
[email protected] Selection and peer-review under responsibility of the scientific committee of the 4th International Conference on System-Integrated Intelligence.
2351-9789 © 2017 The Authors. Published by Elsevier B.V. Peer-review under of the scientificbycommittee the Manufacturing Engineering Society International Conference 2017. 2351-9789 © 2018responsibility The Authors. Published Elsevier of B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 4th International Conference on System-Integrated Intelligence. 10.1016/j.promfg.2018.06.006
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various fields of computer science, production engineers are facing significant challenges in making use of such advanced theories. Therefore, applied research must close the gap between theory and practice by providing concrete solutions for concrete problems. Among manufacturing industry, especially the electric drives production is becoming increasingly important. Megatrends such as the electric mobility or process automation are all built upon energy efficient and cost-effective electric drives. Besides advancing ideas on the design of electric drives, the organization of the manufacturing processes and systems is of great importance. [1, 2, 3] This is where AI technologies come into play: On the one hand, AI technologies could assist the production planner in choosing the optimal production processes for a given electric drive design. On the other hand, such technologies could be used for building intelligent systems, which autonomously adapt their process parameters to changing conditions. This not only reduces costs, but also increases the robustness and resource efficiency of the required processes. However, hardly any AI-based approaches have been proposed in the field of electric drives production yet. Therefore, this paper focuses on transferring the potential of relevant AI technologies to the production of electric drives. After this introduction, the basics of relevant AI technologies, namely knowledge-based systems (KBS) and machine learning (ML), are explained, followed by a short overview about the electric drives production processes. By summarizing the few existing AI-based approaches, the need for action is addressed. Section 3 then gives a structured overview about various application scenarios within the electric drives production. In addition to existing concepts, a large number of approaches from related industrial sectors, which can be transferred to the present case of application, are shown. By identifying and disclosing application scenarios for KBS and ML in the production of electric drives, this paper serves as a starting point for future research in the respective fields. 2. Theoretical background 2.1. Fundamentals of relevant AI technologies It is vital to have a clear definition of AI when discussing its potentials. AI represents the generic term for a diverse field of research. On the one hand, it includes general concepts such as learning and perception. On the other hand, AI addresses specific tasks like playing chess, proving mathematical theorems, driving a car or diagnosing diseases. Due to the variety and dynamics in the field of AI, finding a universal definition is rather difficult. Existing definitions are either focused on human performance or on rationality, i.e. ideal performance. [4] Rich et al. define AI as the “study of how to make computers do things which, at the moment, people do better” [5]. In order to act humanly, a system requires capabilities of natural language processing, knowledge representation, automated reasoning, machine learning, computer vision and robotics [4]. As one can see, AI comprises a wide range of technologies, depending on the problem definition [6]. Therefore, this paper only focuses on those AI technologies that hold great potential for improving the production of electric drives, namely KBS and ML. 2.1.1. Knowledge-based systems and knowledge engineering A KBS is an intelligent, computer-based program or software system which arose from the research of the just mentioned AI. The main task of a KBS is to store knowledge as well as to provide the human user with information to support the problem solving and decision-making process. [7, 8] As shown in Fig. 1, a KBS consists of a knowledge base, an inference engine and a user interface [6]. The knowledge base contains formalized knowledge, whereas the inference engine implements the solution algorithm. The separation between knowledge representation and knowledge processing brings multiple benefits: The underlying models can be adjusted or exchanged without changing the algorithm. Furthermore, different algorithms can be applied to one knowledge base, facilitating the use of existing knowledge for different tasks. [9] Knowledge can be acquired by means of knowledge engineering, using available resources such as human experts, knowledge engineers or data bases. Furthermore, self-learning systems actively discover knowledge from databases and their environment. [6] The process of knowledge discovery is described in the following paragraph.
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Knowledge acquisition
Knowledge processing
Knowledge
3
User
Knowledge engineer Expert
Knowledge-based system
Knowledge engineering
Knowledge base
Data bases
User interface
Inference engine
Knowledge discovery
Environment
Knowledge
Data
Selection
Preprocessing
Transformation
Interpretation Data mining /Evaluation
Classical statistics
Machine learning
Unsupervised learning Supervised learning
Reinforcement learning
Clustering Classification Regression
Fig. 1. Connection between a knowledge-based system, the knowledge discovery process and machine learning
2.1.2. Knowledge discovery, data mining and machine learning Knowledge discovery in databases (KDD) can be regarded as a multidisciplinary approach describing the entire process of gaining useful information from large data sets. The general aim is to transform low-level data into more compact, more abstract or more useful one. As shown in Fig. 1, data mining represents one specific step of this process which is applied for extracting patterns after data has been selected, preprocessed and transformed. By interpreting and evaluating the derived patterns, valuable knowledge can be obtained. [10] Since data mining commonly uses techniques evolving from ML to analyze gathered data, these fields are closely related and to some extent overlapping [10, 11]. However, ML also addresses fields which are not relevant for KDD or rather data mining [11, 12]. Samuel describes ML as a domain that enables computers to learn, eliminating the necessity for explicit programming [13]. Mitchell specifies that a computer program learns if it enhances its performance regarding a specific task by gaining experience. ML algorithms are particularly useful in complex domains where humans lack the required understanding and knowledge to develop efficient algorithms, as well as in domains where the adaptation to altering conditions is essential. [12] Within ML, different learning methods can be distinguished, namely supervised, unsupervised and reinforcement learning. For ML problems, manifold algorithms can be utilized, depending on the application. Frequently used are artificial neural network (ANN) algorithms to which backpropagation networks, selforganizing maps, autoencoders or convolutional neural networks (CNN) belong, just to name a few. Further algorithms include, for example, support vector machines (SVM), decision trees, random forests, k-nearest neighbor, k-means or temporal difference learning. [14] The term deep learning refers to ML techniques which apply multiple data transformation steps to be particularly effective in extracting information from large data sets [15]. 2.2. Short overview about the production processes of electric drives After having explained the relevant AI technologies, the application domain, in this case the electric drives production, has to be described briefly. The main components of an electric drive are the stator and the rotor, surrounded by a housing. The movement of the rotor results from an electromagnetic force in the magnetic field of the stator. The production process can be divided into several sub-processes whose exact sequence varies depending on the motor type. The housing is commonly formed in a pressure die-casting process. The laminated core, a component required both for the stator and the rotor, consists of electrical sheets which are cut out by punching or laser cutting. The individual sheets are joined using common techniques such as riveting, welding or adhesive bonding.
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Subsequently, insulations and windings are mounted to the laminated core. For joining the insulated copper wires, various contacting techniques can be applied, namely crimping, welding or soldering. In addition, the rotor shaft has to be formed, machined and subsequently joined with the laminated core of the rotor. In case of an asynchronous motor, a rotor cage is formed in a pressure die-casting process, whereas in case of a permanent magnet synchronous motor, magnets are assembled. Finally, all components are assembled and tested. More information on the production of electric drives can be found in the referenced literature. [1, 2, 3] 3. Application scenarios of AI technologies within the electric drives production 3.1. State of the art and classification of the approaches found in the literature review Although AI technologies are already used in several industrial sectors, hardly any AI-based approaches have been proposed for the increasingly important electric drives production yet. Only recently, some basic concepts for the use of KBS and ML were presented in [16, 17]. While KBS seem to be eminently suitable for supporting the decision making during the planning and design phase of the production system, ML-based approaches show great potential for optimizing single production processes. In this context, process control, quality management (QM) and predictive maintenance represent the three major applications as described in [17]. Applying ML in process control is expected to result in a higher adaptability to changing conditions, stabilizing output quality while simultaneously reducing reject rates. In QM, ML-based models can be used to monitor or predict the quality of the product, whereby quality measures like checking random samples become unnecessary. In contrast to process control, QM focuses on the final product and therefore does not involve any process adjustments. When it comes to predictive maintenance, ML algorithms can estimate the condition of machines or tools and predict the optimal time for maintenance or tool changes. However, all three applications depend on measurement systems which record relevant process parameters and quality features of the product. [17, 18] In addition to the aforementioned approaches, various AI applications in related manufacturing processes exist from which analogies to the electric drives production can be drawn. Table 1 provides an overview of application scenarios which are examined in detail in the following paragraphs. Underlined approaches directly refer to electric drives production, whereas non-underlined applications are derived from related fields. Table 1. Overview about various application scenarios of AI technologies in electric drives production Electric drives production
AI technologies
Process optimization Knowledge-based system (KBS)
Overall production system Housing production
Machine Learning (ML)
[16], [19], [20], [21], [22] [23]
Process planning and design
Process control
Quality management (QM)
Predictive maintenance
[16], [19], [20], [21], [22] [23], [24], [25], [39], [40]
[23], [25], [39]
[24], [39], [40]
[39]
[26], [27], [28], [29], [30], [31], [32], [33], [34]
[28], [33]
[26], [27], [29], [30], [31], [32], [34]
[27]
[17], [35], [30], [31], [32], [33], [34], [36], [37], [38]
[17], [33]
[17], [35], [30], [31], [32], [34], [36], [37], [38]
[17], [35]
[23], [24], [25], [39], [40]
[23], [25], [39]
[24], [39], [40]
[39]
Magnet assembly
[17], [41], [42]
[17], [41], [42]
Final assembly
[43], [44]
Laminated core production Single process steps
Applications
Winding and insulation Contacting Shaft and rotor cage production
[23]
[43], [44]
Key: Underlined references directly address electric drives production, non-underlined references are derived from related fields
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3.2. Knowledge-based planning and design of the electric drives production system The development and production of electric drives as mechatronic products involve several engineering domains. In this context, an intelligent information system would support in mastering the complexity and managing the knowledge of all engineering disciplines involved, especially in developing the suitable production system. Therefore, in [16] a concept for a KBS supporting the product and process development of electric drives is presented. As a kind of production configurator, a KBS could draw conclusions from the electric drive design and derive the optimal production system. After presenting the principle architecture of the aspired KBS, Mayr et al. focus on the key component of such a system, the knowledge base. In order to formalize the experts’ knowledge, a proper knowledge representation method, in this case a semantic network, is chosen. For the implementation of a first prototype a graph database, especially the platform Neo4j, has proven the best applicability and will be used for further development within the underlying research project. An exemplary extract of the graph-based knowledge representation is shown in Fig. 2a, whereas more details are given in [16]. In addition to this concrete concept, analogies can be drawn from knowledge-based approaches in related fields. Since most approaches are related to the development of the product, there are few approaches for knowledge-based planning and design of production systems. To enable the documentation of expertise and know-how about dependencies within the development of products and production systems, Gausemeier et al. make use of semantic technologies, to which the KBS also belongs [19]. Similarly, Bauer’s knowledge-based planning tool builds upon the basic structure of a KBS and provides targeted support for the planning of a production system with CONSENS. The specification method CONSENS allows a semi-formal graphical modeling of the system and serves as a coordination and communication medium. [20] Further notable approaches are described in [21] or [22]. a)
b)
Component
Process
Attribute
Features • Visual appeareance of connection • Acoustic emissions
ML algorithm • Convolutional Neural Network • Support Vector Machine
Label • Electrical resistance • Withdrawal force
Relation …
Fig. 2. (a) Extract of a graph-based knowledge representation of alternative process chains; (b) ML-based prediction of the crimping quality
3.3. ML-based optimization of single production processes In contrast to KBS concepts, which mostly affect the overall production system, ML approaches rather focus on the optimization of individual process steps. In the following paragraph, several ML-based concepts suitable for the electric drives production are presented. For forming the housing as well as the rotor cage, analogies can be found in related casting processes. Saleem et al. develop a comprehensive system for the control of these processes. This includes ML techniques, a relational database and a KBS. By evaluating process data with intelligent tools and detecting implied correlations, process parameters can be optimized dynamically. [23] In addition, Rössle and Kübler present a ML approach for real-time quality prediction in die-casting based on process data acquired by high-resolution sensors [24]. Patel et al. describe a ML approach based on ANN which facilitates an automated process control system for squeeze casting. They develop a process model which utilizes forward mapping to predict the casting quality. Moreover, reverse mapping can be used to determine the appropriate input parameters which are necessary for meeting certain quality requirements. This model can be adapted for pressure die-casting since the respective processes show high similarities. [25] The production process of laminated cores offers numerous opportunities for the application of ML techniques. Rebouças Filho et al. present a supervised learning approach for the production of electrical steel sheets. The microstructures of non-grain oriented electrical steels serve as input data to predict the electromagnetic performance
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and thus to classify the material. [26] In production processes like sheet metal processing, large amounts of data are generated, offering high potential for the application of data mining and ML techniques. Accordingly, Bauer et al. describe an approach for error identification and predictive maintenance in laser cutting processes. Additionally, they develop a supervised learning model which can predict the feed rate of a punch laser machine based on audio signals. Thus, machine faults can be diagnosed without altering the production process. [27] Slomp and Klingenberg propose an autonomous process control system for punching operations, utilizing ANN to derive quality-indicating characteristics from the force-displacement graph [28]. Rahman et al. address the identification of defects in metalstamping processes. In their supervised learning model, ANN serve as a pattern recognizer, using statistical features from the manufacturing process as input data. [29] After being cut out, the individual electrical sheets are joined. From the field of welding, different approaches can be transferred to electric drives production. Petković presents a supervised learning approach for quality prediction in welding processes, using support vector regression. Therefore, various process parameters are utilized as input data, while weld strength and weld dimensions represent quality-relevant output parameters. [30] Similarly, Chen et al. use SVM for quality modeling in gas tungsten arc welding [31]. Sumesh et al. consider acoustic features for quality monitoring in welding processes. In their approach supervised learning algorithms are used for the classification of welds based on arc sound signals, distinguishing between defect-free welds, lack of fusion and burn through. [32] Günther et al. introduce a self-improving laser welding system. Significant features are extracted by an deep autoencoder. Subsequently, two reinforcement algorithms are applied to acquire process knowledge and control the process. [33] Khumaidi et al. present a visual inspection system for the classification of welding defects based on CNN [34]. In general, most of the ML-based approaches which are directly related to electric drives production refer to contacting technologies. Fleischmann et al. propose a self-adapting monitoring system for thermo-crimping processes in the production of electric drives, allowing for quality monitoring and predictive maintenance. Variations in energy consumption and process temperature are used to determine electrode wear and joint quality. In their model, ANN are applied for classification and prediction tasks. [35] Mayr et al. expand this approach and consider not only thermocrimping but also the innovative ultrasonic crimping process. In doing so, they examine the potential of ML algorithms for each of the three major applications mentioned in Tab. 1. In terms of QM, a ML model can predict the quality of a joint based on process parameters or its visual appearance, eliminating the need for QM measures such as random checks. The input parameters vary between ultrasonic and thermo-crimping, while contact resistance and withdrawal force serve as quality indicators for both types. With regard to predictive maintenance, ML algorithms can estimate the tool condition in-situ without requiring expensive measurement devices. For this purpose, the functional relations presented in [35] are proposed as a foundation. Another potential application is process control: A ML-based model can predict the process behavior, allowing for the adjustment of process parameters. Besides listing these potentials, a practical example is presented to validate the application of ML in ultrasonic crimping. Different regression models are considered for estimating the withdrawal force of a crimped connection based on input parameters. All three regressors, namely SVM, random forest and AdaBoost, show satisfactory results. Moreover, a SVM and a CNN are used to classify the quality of connections based on visual features. Another concept uses acoustic features for classification. The last two examples are illustrated in Fig. 2b, whereas more details are given in [17]. In addition to these ML-based models for crimping, application scenarios from other joining processes can be transferred to electric drives production. As described above, several approaches can be found in welding processes. Further applicable concepts can be derived from soldering processes. Wu proposes a method which obtains images of solder joints and uses an ANN to identify solder joint defects [36]. Similarly, Hao et al. and Cai et al. focus on the optical inspection of solder joints. In Hao et al.’s approach, a backpropagation network is used for the classification of solder joint defects [37]. Cai et al. present a deep learning approach using CNN [38]. Concerning the production of the shaft as well as the post-processing of the housing and the rotor cage, multiple approaches can be transferred from machining processes. Al-Zubaidi et al. provide a literature overview addressing the application of ANN in milling processes. Accordingly, ANN can be used for process control, QM and predictive maintenance. The presented models focus on the prediction of surface roughness and cutting force, as well as the estimation of tool life and wear. [39] Satorres Martínez et al. describe a method for the visual inspection of machined metal parts. They introduce a supervised learning model for the automated quality inspection of metallic surfaces based on ANN. [40]
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Besides, ML can also be applied in magnet assembly processes. Coupek et al. introduce a cloud-based control for selective magnet assembly. Their concept includes a ML approach to compensate magnetic deviations. Self-organizing maps are used for clustering the parts based on their magnetic profile. With these clusters, the assembly process can be optimized to achieve a uniform magnetic field. [41] Similarly, Mayr et al. present an application scenario for ML algorithms in selective magnet assembly processes, including the selection of magnets from a storage system, the rearrangement of magnets on the rotor and the shifting and rotation of individual magnets. Magnetic and geometric properties as well as assembly characteristics are considered as input parameters in their model. [17] Another concept is introduced by Murakami, who utilizes a ML algorithm to optimize the arrangement of magnets [42]. Further ML applications can be found in end-of-line testing. Sun et al. present a deep learning approach for quality testing of motors. They solely use vibration signals of defect-free motors as training data for an autoencoder, which is subsequently able to reconstruct such signals. In this way, potential deviations between the reconstructed and the recorded signal from the test motor serve as an indicator for defects. [43] Besides, Nan describes a methodology for fault diagnosis which can be transferred to end-of-line testing. A SVM in combination with an optimization algorithm is applied for the classification of mechanic faults such as shaft crack, bearing fault or permanent bending. [44] 4. Conclusion and outlook In addition to the described application scenarios, several other approaches in related fields exist. However, the limited scope of this paper does not allow to describe all of them. Therefore, it focuses on a small selection of the presumably most relevant approaches. The resulting overview reveals that KBS are especially suited for supporting the planning and design of the electric drives production system, whereas ML-based approaches have great potential for optimizing single production steps. It seems as if the most achievable potentials of ML techniques lie primarily in joining processes, i.e. the contacting and laminated core production. Other promising approaches can be found in casting and machining processes, as used for the production of housings and rotor cages. In addition, the example of the selective magnet assembly shows how optimized production processes can even positively influence the running characteristics of electric drives. For handling tasks like winding, other AI technologies such as robotics and computer vision should be considered. By identifying and disclosing application potentials for KBS and ML in the electric drives production, this paper serves as a starting point for further research in the respective fields. Thereby, the research focus of the authors is primarily on the aforementioned knowledge-based planning and design of electric drives production systems as well as the ML-based optimization of contacting processes, such as the innovative ultrasonic crimping. Acknowledgements The authors would like to express their sincere thanks to the Bavarian State Ministry for Science, Research and Culture for funding this research in the framework of the research association “Green Factory Bavaria”. References J. Franke and F. Risch, “Flexible Automatisierungstechnologien für die Produktion elektrischer Traktionsantriebe,“ in Elektrische Antriebstechnologie für Hybrid- und Elektrofahrzeuge, H. Schäfer, Ed. Renningen, Germany: Expert Verlag, 2014, pp. 377-388. [2] A. Kampker, Elektromobilproduktion, Berlin/Heidelberg, Germany: Springer Vieweg, 2014. [3] J. Hagedorn. F. S.-L. Blanc and J. Fleischer, Handbuch der Wickeltechnik für hocheffiziente Spulen und Motoren: Ein Beitrag zur Energieeffizienz, Berlin/Heidelberg, Germany: Springer Vieweg, 2016. [4] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 3rd ed. Upper Saddle River, NJ, USA: Prentice Hall, 2010. [5] E. Rich, K. Knight and S. B. Nair, Artificial Intelligence, 3rd ed. New Delhi, India: Tata McGraw-Hill, 2009. [6] W. Ertel, Grundkurs Künstliche Intelligenz: Eine praxisorientierte Einführung, 4th ed. Wiesbaden, Germany: Springer Vieweg, 2016. [7] C. Beierle and G. Kern-Isberner, Methoden wissensbasierter Systeme, 5th ed. Wiesbaden, Germany: Springer Vieweg, 2014. [8] R. Akerkar and P. Sajja, Knowledge-based systems, 1st ed. New York, NY, USA: Jones & Bartlett Publ Inc, 2009. [9] J. Lunze, Künstliche Intelligenz für Ingenieure: Methoden zur Lösung ingenieurtechnischer Probleme mit Hilfe von Regeln, logischen Formeln und Bayes-Netzen, 3rd ed. Oldenbourg, Germany: DeGruyter, 2016. [10] U. Fayyad, G. Piatetsky-Shapiro and P. Smyth, “From Data Mining to Knowledge Discovery in Databases,” AI Mag., vol. 17, no. 3, pp. 3754, 1996. [11] S. Wrobel, T. Joachims and K. Morik, “Maschinelles Lernen und Data Mining,“ in Handbuch der Künstlichen Intelligenz, 5th ed., G. Görz, J. Schneeberger and U. Schmid, Eds. Munich, Germany: Oldenbourg Verlag, 2014, pp. 405-471. [1]
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