Pub Date : 2019-12-01DOI: 10.1109/EDPC48408.2019.9011889
M. Halwas, Florian Sell-Le Blanc, B. Jux, M. Doppelbauer, F. Wirth, L. Hausmann, J. Hofmann, J. Fleischer
Coherences between production technology and performance of electric traction drives are published or based on experiential knowledge. The content of this paper shall represent an essential basis for intentions of improving future research and development purposes of production technologies for traction drives, but also of electric machine designs in general. The basic ambition of engineering a new manufacturing technology is to improve the performance of a product, taking several boundary conditions into account, like costs or cycle times. It has to be considered that the conflict area of production and performance are connected by physical characteristics, which are determined by the geometric and material compositions of the electric machine in this context. It is evident that the physical characteristics have a direct impact on the performance of electric machines. However, the production technology has a straight and unavoidable influence on the physical characteristic. An example for this is the slot fill factor, which is determined by the winding technology, but influences the performance of the machine significantly. First, known coherences between physical characteristics and performance of electric machines are considered. Therefore, an extensive summary of technical literature and publications at the current state of the art in science applications is used as a starting point. To give the best possible overview, a summary and visualization dependency matrix is created, in which the various elements of physical characteristic and the resulting performance of the electric machine are compared against each other. Next, the main influences of the different manufacturing processes on the characteristics of electric machines are presented, especially focusing on the winding technology. These contents are also transferred into the dependency matrix.
{"title":"Coherences Between Production Technology and Performance of Electric Traction Drives","authors":"M. Halwas, Florian Sell-Le Blanc, B. Jux, M. Doppelbauer, F. Wirth, L. Hausmann, J. Hofmann, J. Fleischer","doi":"10.1109/EDPC48408.2019.9011889","DOIUrl":"https://doi.org/10.1109/EDPC48408.2019.9011889","url":null,"abstract":"Coherences between production technology and performance of electric traction drives are published or based on experiential knowledge. The content of this paper shall represent an essential basis for intentions of improving future research and development purposes of production technologies for traction drives, but also of electric machine designs in general. The basic ambition of engineering a new manufacturing technology is to improve the performance of a product, taking several boundary conditions into account, like costs or cycle times. It has to be considered that the conflict area of production and performance are connected by physical characteristics, which are determined by the geometric and material compositions of the electric machine in this context. It is evident that the physical characteristics have a direct impact on the performance of electric machines. However, the production technology has a straight and unavoidable influence on the physical characteristic. An example for this is the slot fill factor, which is determined by the winding technology, but influences the performance of the machine significantly. First, known coherences between physical characteristics and performance of electric machines are considered. Therefore, an extensive summary of technical literature and publications at the current state of the art in science applications is used as a starting point. To give the best possible overview, a summary and visualization dependency matrix is created, in which the various elements of physical characteristic and the resulting performance of the electric machine are compared against each other. Next, the main influences of the different manufacturing processes on the characteristics of electric machines are presented, especially focusing on the winding technology. These contents are also transferred into the dependency matrix.","PeriodicalId":119895,"journal":{"name":"2019 9th International Electric Drives Production Conference (EDPC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123737543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/EDPC48408.2019.9011884
Moritz Kilper, Sandra Fickel, Hristian Naumoski, K. Hameyer
The use of SiC MOSFETs in modern power electronic causes transient voltage overshoot effects in the winding system of an electrical machine due to the fast switching operation of the semi conductors. This overshoot leads in combination with an increased voltage level to higher electrical stress within the insulation system. Based on the requirements for conventional winding insulation systems, the emerging electrical field can generate partial discharges (PD). Since the PD depends on various quantities, a study of the basic relationships is performed here. This contribution discusses the necessity of a high frequency (HF) model for the electrical machine A hybrid, coupled approach of analytical and numerical simulations determine the parameter of this HF - model. In order to verify the HF - model, the predicted transient voltage distribution along the lap of a winding is compared to experimental data obtained from a motorette. The HF - model predicts the transient voltage with respect to the traveling time and location. In our contribution an approach will be discussed, in which the locally simulated voltage distribution and the occurrence of PD is predicted. 2D Finite Element simulations of the electric field are employed. To proof the approachs reliability to accurately predict PD for different topologies of the insulation system, different motorettes are studied. The different motorettes are developed using results of the method of design of experiments (DoE) to cover a wide variety of possible insulation system combinations. The model factors include a variation of the wire grade, the geometry and the winding scheme. The results obtained show, that PD prediction is within the standard deviation of the experimental data. Besides the validation of the simulation approach, the DoE demonstrated the influence of the different quantities on PD.
SiC mosfet在现代电力电子中的应用，由于半导体的快速开关操作，导致电机绕组系统中的瞬态电压超调效应。这种超调与增加的电压水平相结合，导致绝缘系统内更高的电应力。基于传统绕组绝缘系统的要求，新兴电场可产生局部放电(PD)。由于PD依赖于各种量，因此在这里进行基本关系的研究。本文讨论了建立电机高频模型的必要性。分析和数值模拟的混合耦合方法确定了高频模型的参数。为了验证高频模型，将预测的暂态电压沿绕组搭接的分布与从电机上获得的实验数据进行了比较。高频模型预测暂态电压与行进时间和位置的关系。在我们的贡献中，将讨论一种方法，其中局部模拟电压分布和PD的发生进行预测。采用二维有限元法对电场进行了模拟。为了证明该方法能够准确预测不同拓扑结构绝缘系统的局部放电，对不同的电机进行了研究。使用实验设计方法(DoE)的结果开发了不同的电动机，以涵盖各种可能的绝缘系统组合。模型因素包括线材等级、几何形状和绕线方案的变化。结果表明，PD预测值在实验数据的标准差范围内。除了验证了仿真方法外，DoE还验证了不同量对PD的影响。
{"title":"Effects of Fast Switching Semiconductors Operating Variable Speed Low Voltage Machines","authors":"Moritz Kilper, Sandra Fickel, Hristian Naumoski, K. Hameyer","doi":"10.1109/EDPC48408.2019.9011884","DOIUrl":"https://doi.org/10.1109/EDPC48408.2019.9011884","url":null,"abstract":"The use of SiC MOSFETs in modern power electronic causes transient voltage overshoot effects in the winding system of an electrical machine due to the fast switching operation of the semi conductors. This overshoot leads in combination with an increased voltage level to higher electrical stress within the insulation system. Based on the requirements for conventional winding insulation systems, the emerging electrical field can generate partial discharges (PD). Since the PD depends on various quantities, a study of the basic relationships is performed here. This contribution discusses the necessity of a high frequency (HF) model for the electrical machine A hybrid, coupled approach of analytical and numerical simulations determine the parameter of this HF - model. In order to verify the HF - model, the predicted transient voltage distribution along the lap of a winding is compared to experimental data obtained from a motorette. The HF - model predicts the transient voltage with respect to the traveling time and location. In our contribution an approach will be discussed, in which the locally simulated voltage distribution and the occurrence of PD is predicted. 2D Finite Element simulations of the electric field are employed. To proof the approachs reliability to accurately predict PD for different topologies of the insulation system, different motorettes are studied. The different motorettes are developed using results of the method of design of experiments (DoE) to cover a wide variety of possible insulation system combinations. The model factors include a variation of the wire grade, the geometry and the winding scheme. The results obtained show, that PD prediction is within the standard deviation of the experimental data. Besides the validation of the simulation approach, the DoE demonstrated the influence of the different quantities on PD.","PeriodicalId":119895,"journal":{"name":"2019 9th International Electric Drives Production Conference (EDPC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114278694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/EDPC48408.2019.9012029
Daniel Keller, Akif Karayel, N. Parspour
This study presents investigations on different winding designs for dual three-phase permanent magnet synchronous machines. Dual three-phase machine design can enable a reduction of the maximum IGBT current by increasing the number of phase belts from three to six. Dual three-phase machines have important advantages compared to conventional three-phase machines such as decreased torque pulsation and increased fault tolerance. In this contribution a short pitch dual three-phase winding where the winding sets are placed in seperate halves of the stator with no-phase shift between the two subsystems is compared to a dual three-phase winding with thirty degree phase shift between the two winding subsystems. This paper presents 2D finite-element analysis of the different winding designs and provides acoustic measurements to verify the conclusions.
{"title":"Comparison of Two Different Winding Sets for Dual Three-Phase Permanent Magnet Machines","authors":"Daniel Keller, Akif Karayel, N. Parspour","doi":"10.1109/EDPC48408.2019.9012029","DOIUrl":"https://doi.org/10.1109/EDPC48408.2019.9012029","url":null,"abstract":"This study presents investigations on different winding designs for dual three-phase permanent magnet synchronous machines. Dual three-phase machine design can enable a reduction of the maximum IGBT current by increasing the number of phase belts from three to six. Dual three-phase machines have important advantages compared to conventional three-phase machines such as decreased torque pulsation and increased fault tolerance. In this contribution a short pitch dual three-phase winding where the winding sets are placed in seperate halves of the stator with no-phase shift between the two subsystems is compared to a dual three-phase winding with thirty degree phase shift between the two winding subsystems. This paper presents 2D finite-element analysis of the different winding designs and provides acoustic measurements to verify the conclusions.","PeriodicalId":119895,"journal":{"name":"2019 9th International Electric Drives Production Conference (EDPC)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116726953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/EDPC48408.2019.9011872
Tobias Glaessel, Daniel Bachinski Pinhal, M. Masuch, D. Gerling, J. Franke
To face the challenges posed by the electrification of the automotive drivetrain, rotating electrical machines are getting an impetus to innovation. Thus, machines of high power density are required to be manufactured cost-effectively in a large-scale production. To meet these requirements, the application of stators with hairpin windings is focused in industry and research institutions, as they show potentials to realize improved copper fill ratios in the armature slots. In addition, this technology also is seen as one possibility to substitute the elaborate winding technologies that often feature a lack of reproducibility, by bending, assembling and joining processes. During the design of electric drives, the influences of deviations of manufacturing processes on its performance usually are not taken into account. This particularly applies to the technology of hairpin windings, whose manufacturing requires a large number of bending and contacting operations. These process steps may influence the electrical properties of the winding, changing the behavior of the electrical machine. For this reason, the objective of this paper is to identify deviations in the manufacturing processes of hairpin windings and to validate their influence on the performance of the drive. For this purpose, measuring systems are set up which make it possible to quantify the influence of bending and contacting processes on the electrical properties of the winding by means of highly accurate resistance measurements. As next step, a simulation is set up to map the production influences on the hairpin winding to the machine's efficiency. Based on simulation studies, the influences of the manufacturing imperfections on the engine performance become visible.
{"title":"Manufacturing Influences on the Motor Performance of Traction Drives with Hairpin Winding","authors":"Tobias Glaessel, Daniel Bachinski Pinhal, M. Masuch, D. Gerling, J. Franke","doi":"10.1109/EDPC48408.2019.9011872","DOIUrl":"https://doi.org/10.1109/EDPC48408.2019.9011872","url":null,"abstract":"To face the challenges posed by the electrification of the automotive drivetrain, rotating electrical machines are getting an impetus to innovation. Thus, machines of high power density are required to be manufactured cost-effectively in a large-scale production. To meet these requirements, the application of stators with hairpin windings is focused in industry and research institutions, as they show potentials to realize improved copper fill ratios in the armature slots. In addition, this technology also is seen as one possibility to substitute the elaborate winding technologies that often feature a lack of reproducibility, by bending, assembling and joining processes. During the design of electric drives, the influences of deviations of manufacturing processes on its performance usually are not taken into account. This particularly applies to the technology of hairpin windings, whose manufacturing requires a large number of bending and contacting operations. These process steps may influence the electrical properties of the winding, changing the behavior of the electrical machine. For this reason, the objective of this paper is to identify deviations in the manufacturing processes of hairpin windings and to validate their influence on the performance of the drive. For this purpose, measuring systems are set up which make it possible to quantify the influence of bending and contacting processes on the electrical properties of the winding by means of highly accurate resistance measurements. As next step, a simulation is set up to map the production influences on the hairpin winding to the machine's efficiency. Based on simulation studies, the influences of the manufacturing imperfections on the engine performance become visible.","PeriodicalId":119895,"journal":{"name":"2019 9th International Electric Drives Production Conference (EDPC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114627497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/EDPC48408.2019.9012044
Johannes Vater, Peter Schamberger, Alois Knoll, D. Winkle
The automotive industry is facing a change from combustion engine-powered to electrified vehicles. Besides the traction battery, the electric engine is one of the most important components of the electrified powertrain. In order to increase the energy efficiency of the electric motor, wound copper wires are replaced by enameled rectangular copper wires, known as hairpins. In order to produce a conductive connection between hairpins, it is necessary to weld them together. Currently, the automated laser welding of copper is a poorly understood process. Such new production processes are still unknown in comparison to classic engine production and there is only little expert knowledge available. The integration of Industry 4.0 techniques and advanced data analytics provides the opportunity to understand the process of copper welding more thoroughly. A common understanding of advanced data analytics differentiates between predictive and prescriptive analytics. One of the most promising developments in advanced analytics is Machine Learning (ML). There is a wide range of different types of algorithms, theories and methods. An example of these are Convolutional Neural Networks (CNN). They have been designed for learning multidimensional data, such as images or even videos. This paper presents such a CNN to detect welding defects of hairpins. Depending on the classified defect, a rework concept is given (prescriptive analytics). The input parameters are the visual information are derived from of a 3D camera. Using the welding process as an example, the paper illustrates a newly developed method based on the CRoss Industry Standard Process for Data Mining (CRISP-DM) for the development of the CNN. In this context, the paper deals in detail with data preprocessing, modeling and evaluation. The newly developed methodology and architecture of the CNN achieves an accuracy of over 99 percent to predict the defect class.
{"title":"Fault Classification and Correction based on Convolutional Neural Networks exemplified by laser welding of hairpin windings","authors":"Johannes Vater, Peter Schamberger, Alois Knoll, D. Winkle","doi":"10.1109/EDPC48408.2019.9012044","DOIUrl":"https://doi.org/10.1109/EDPC48408.2019.9012044","url":null,"abstract":"The automotive industry is facing a change from combustion engine-powered to electrified vehicles. Besides the traction battery, the electric engine is one of the most important components of the electrified powertrain. In order to increase the energy efficiency of the electric motor, wound copper wires are replaced by enameled rectangular copper wires, known as hairpins. In order to produce a conductive connection between hairpins, it is necessary to weld them together. Currently, the automated laser welding of copper is a poorly understood process. Such new production processes are still unknown in comparison to classic engine production and there is only little expert knowledge available. The integration of Industry 4.0 techniques and advanced data analytics provides the opportunity to understand the process of copper welding more thoroughly. A common understanding of advanced data analytics differentiates between predictive and prescriptive analytics. One of the most promising developments in advanced analytics is Machine Learning (ML). There is a wide range of different types of algorithms, theories and methods. An example of these are Convolutional Neural Networks (CNN). They have been designed for learning multidimensional data, such as images or even videos. This paper presents such a CNN to detect welding defects of hairpins. Depending on the classified defect, a rework concept is given (prescriptive analytics). The input parameters are the visual information are derived from of a 3D camera. Using the welding process as an example, the paper illustrates a newly developed method based on the CRoss Industry Standard Process for Data Mining (CRISP-DM) for the development of the CNN. In this context, the paper deals in detail with data preprocessing, modeling and evaluation. The newly developed methodology and architecture of the CNN achieves an accuracy of over 99 percent to predict the defect class.","PeriodicalId":119895,"journal":{"name":"2019 9th International Electric Drives Production Conference (EDPC)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114736247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/EDPC48408.2019.9011839
J. Hofmann, J. Fleischer, F. Blanc, Wilken Wößner, Eyathunandhan Angiyan-Vishnuram, H. Köhn, Alexander Lepold, T. Weber, Thomas Schüttler, Paul Busch
Due to the electrification of the automotive powertrain new challenges in production technology must be faced. For direct winding processes of non-circular bobbins, the control of the wire tensile force is crucial for the winding quality and displays one of the greatest challenges. Within this paper a new approach to control the tensile force along the winding trajectory will be introduced. As a first use case, the application of this approach for linear winding technology is presented. The potential of the model based servo-based wire tensile force control is demonstrated through systematic winding experiments. Since the control strategy does not solely rely on a measurement of the wire tensile force but also on the modeled behavior of the wire length, precision winding schemes can be achieved at significantly increased winding speeds. Secondly the transfer of this technology to the more complex multi axis needle winding kinematics will be presented. Finally the implementation of the control strategy in the needle winding machine will be shown and a validation with winding experiments is provided.
{"title":"Development of a new model based servo-controlled wire tensile force control for stator winding applications","authors":"J. Hofmann, J. Fleischer, F. Blanc, Wilken Wößner, Eyathunandhan Angiyan-Vishnuram, H. Köhn, Alexander Lepold, T. Weber, Thomas Schüttler, Paul Busch","doi":"10.1109/EDPC48408.2019.9011839","DOIUrl":"https://doi.org/10.1109/EDPC48408.2019.9011839","url":null,"abstract":"Due to the electrification of the automotive powertrain new challenges in production technology must be faced. For direct winding processes of non-circular bobbins, the control of the wire tensile force is crucial for the winding quality and displays one of the greatest challenges. Within this paper a new approach to control the tensile force along the winding trajectory will be introduced. As a first use case, the application of this approach for linear winding technology is presented. The potential of the model based servo-based wire tensile force control is demonstrated through systematic winding experiments. Since the control strategy does not solely rely on a measurement of the wire tensile force but also on the modeled behavior of the wire length, precision winding schemes can be achieved at significantly increased winding speeds. Secondly the transfer of this technology to the more complex multi axis needle winding kinematics will be presented. Finally the implementation of the control strategy in the needle winding machine will be shown and a validation with winding experiments is provided.","PeriodicalId":119895,"journal":{"name":"2019 9th International Electric Drives Production Conference (EDPC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132078446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/EDPC48408.2019.9012063
M. Linnemann, M. Bach, V. Psyk, M. Werner, M. Gerlach, N. Schubert
Due to new requirements regarding the efficiency of electrical machines it is necessary to increase the slot filling factor of such machines. Forming technology offers an excellent opportunity to achieve this aim. The paper therefore presents a method for flexibly adapting the cross-sectional shape of the coil to the geometry of the slot. First, the potentials and limits of forming technologies are presented. Furthermore, the forming tool required for the cross-sectional adaptation is described. Advantages and limits of the method are analyzed and evaluated. In particular, factors such as elongation and burr formation play a decisive role here. Finally, an outlook is given and possible research approaches are discussed.
{"title":"Resource-efficient, innovative coil production for increased filling factor","authors":"M. Linnemann, M. Bach, V. Psyk, M. Werner, M. Gerlach, N. Schubert","doi":"10.1109/EDPC48408.2019.9012063","DOIUrl":"https://doi.org/10.1109/EDPC48408.2019.9012063","url":null,"abstract":"Due to new requirements regarding the efficiency of electrical machines it is necessary to increase the slot filling factor of such machines. Forming technology offers an excellent opportunity to achieve this aim. The paper therefore presents a method for flexibly adapting the cross-sectional shape of the coil to the geometry of the slot. First, the potentials and limits of forming technologies are presented. Furthermore, the forming tool required for the cross-sectional adaptation is described. Advantages and limits of the method are analyzed and evaluated. In particular, factors such as elongation and burr formation play a decisive role here. Finally, an outlook is given and possible research approaches are discussed.","PeriodicalId":119895,"journal":{"name":"2019 9th International Electric Drives Production Conference (EDPC)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133985225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/EDPC48408.2019.9012036
M. Masuch, Johannes von Lindenfels, J. Seefried, A. Mayr, Michael Schneider, M. Weigelt, A. Kühl, J. Franke, Christian Hemmer
In the ongoing development of electric traction motors with increasing volumetric power density, concepts are being developed for rotational speeds of more than 30,000 rpm. These motors require an especially high balancing quality. Due to the unavoidable material and production fluctuations, a tuning of the mass distribution regarding the rotation axis is necessary. This balancing can be achieved by subtractive or additive process alternatives. In this paper, an overview of current balancing approaches is given first. Based on this, the potentials and challenges of additive balancing methods, which directly apply material to the lamination stack, are explained. In this context, two joining processes, namely ultrasonic welding and laser beam welding, are examined in more detail on the basis of practical experiments. In particular, influences on the quality of the lamination stack are considered.
{"title":"Comparison of Additive Balancing Processes for Rotors in the Context of High Speed Electric Traction Motors","authors":"M. Masuch, Johannes von Lindenfels, J. Seefried, A. Mayr, Michael Schneider, M. Weigelt, A. Kühl, J. Franke, Christian Hemmer","doi":"10.1109/EDPC48408.2019.9012036","DOIUrl":"https://doi.org/10.1109/EDPC48408.2019.9012036","url":null,"abstract":"In the ongoing development of electric traction motors with increasing volumetric power density, concepts are being developed for rotational speeds of more than 30,000 rpm. These motors require an especially high balancing quality. Due to the unavoidable material and production fluctuations, a tuning of the mass distribution regarding the rotation axis is necessary. This balancing can be achieved by subtractive or additive process alternatives. In this paper, an overview of current balancing approaches is given first. Based on this, the potentials and challenges of additive balancing methods, which directly apply material to the lamination stack, are explained. In this context, two joining processes, namely ultrasonic welding and laser beam welding, are examined in more detail on the basis of practical experiments. In particular, influences on the quality of the lamination stack are considered.","PeriodicalId":119895,"journal":{"name":"2019 9th International Electric Drives Production Conference (EDPC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132459089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/EDPC48408.2019.9011820
M. Metzner, Daniel Fiebag, A. Mayr, J. Franke
Automatic optical inspection (AOI) of solder joints is a common testing process in electronics production. Especially in power electronics production for electric drive systems, such inspection systems are employed for quality control of selective soldering processes for through-hole devices. Up to now, commercial systems rely on rule-based programming for the determination of soldering quality. However, this approach demands expert knowledge for setup and is very susceptible to changes in input data. To avoid error slip, thresholds are often defined very strictly, resulting in a high pseudo-error rate. Improvement is only possible through extensive expert input. As power electronics production is often characterized by a high variant and only medium quantities, this manual effort is critical. In this contribution, we benchmark a commercial AOI system with an adaptive approach utilizing convolutional neural networks based on a pre-trained VGG-16 algorithm with custom fully connected layers. Supervised learning is employed for each static region of interest with refined labeled data from the existing AOI system. To overcome the extremely unbalanced dataset, we employ data augmentation and data filtering. Our results show significant improvement in precision over the commercial system regarding the total recall. In addition, the adaptive system is also able to learn from pseudo-error classifications. We also show that our approach can not only output a binary classification but also identify process deviations that may still yield acceptable quality. Hence, this output might be used for an online control of process parameters in further research.
{"title":"Automated Optical Inspection of Soldering Connections in Power Electronics Production Using Convolutional Neural Networks","authors":"M. Metzner, Daniel Fiebag, A. Mayr, J. Franke","doi":"10.1109/EDPC48408.2019.9011820","DOIUrl":"https://doi.org/10.1109/EDPC48408.2019.9011820","url":null,"abstract":"Automatic optical inspection (AOI) of solder joints is a common testing process in electronics production. Especially in power electronics production for electric drive systems, such inspection systems are employed for quality control of selective soldering processes for through-hole devices. Up to now, commercial systems rely on rule-based programming for the determination of soldering quality. However, this approach demands expert knowledge for setup and is very susceptible to changes in input data. To avoid error slip, thresholds are often defined very strictly, resulting in a high pseudo-error rate. Improvement is only possible through extensive expert input. As power electronics production is often characterized by a high variant and only medium quantities, this manual effort is critical. In this contribution, we benchmark a commercial AOI system with an adaptive approach utilizing convolutional neural networks based on a pre-trained VGG-16 algorithm with custom fully connected layers. Supervised learning is employed for each static region of interest with refined labeled data from the existing AOI system. To overcome the extremely unbalanced dataset, we employ data augmentation and data filtering. Our results show significant improvement in precision over the commercial system regarding the total recall. In addition, the adaptive system is also able to learn from pseudo-error classifications. We also show that our approach can not only output a binary classification but also identify process deviations that may still yield acceptable quality. Hence, this output might be used for an online control of process parameters in further research.","PeriodicalId":119895,"journal":{"name":"2019 9th International Electric Drives Production Conference (EDPC)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126645515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/EDPC48408.2019.9011861
A. Mayr, J. Franke, J. Seefried, M. Ziegler, M. Masuch, A. Mahr, J. V. Lindenfels, Moritz Meiners, Dominik Kißkalt, M. Metzner
Artificial intelligence entails a wide range of technologies, which provide great potential for tomorrow's electric motor production. Above all, data-driven techniques such as machine learning (ML) are increasingly moving into focus. ML provides systems the ability to automatically learn and improve from data without being explicitly programmed. However, the potential of ML has not yet been tapped by most electric motor manufacturers. Therefore, this paper aims to summarize potential applications of ML along the whole process chain. To do so, basic methods, potentials and challenges of ML are discussed first. Secondly, special characteristics of the application domain are outlined. Building on this, various ML approaches directly relating to electric motor production are presented. In addition, a selection of transferable approaches from related sectors is included, as many ML approaches can be used across industries. In conclusion, the given overview of different ML approaches helps practitioners to better assess the possibilities and limitations of ML. Moreover, it encourages the identification and exploitation of further ML use cases in electric motor production.
{"title":"Machine Learning in Electric Motor Production - Potentials, Challenges and Exemplary Applications","authors":"A. Mayr, J. Franke, J. Seefried, M. Ziegler, M. Masuch, A. Mahr, J. V. Lindenfels, Moritz Meiners, Dominik Kißkalt, M. Metzner","doi":"10.1109/EDPC48408.2019.9011861","DOIUrl":"https://doi.org/10.1109/EDPC48408.2019.9011861","url":null,"abstract":"Artificial intelligence entails a wide range of technologies, which provide great potential for tomorrow's electric motor production. Above all, data-driven techniques such as machine learning (ML) are increasingly moving into focus. ML provides systems the ability to automatically learn and improve from data without being explicitly programmed. However, the potential of ML has not yet been tapped by most electric motor manufacturers. Therefore, this paper aims to summarize potential applications of ML along the whole process chain. To do so, basic methods, potentials and challenges of ML are discussed first. Secondly, special characteristics of the application domain are outlined. Building on this, various ML approaches directly relating to electric motor production are presented. In addition, a selection of transferable approaches from related sectors is included, as many ML approaches can be used across industries. In conclusion, the given overview of different ML approaches helps practitioners to better assess the possibilities and limitations of ML. Moreover, it encourages the identification and exploitation of further ML use cases in electric motor production.","PeriodicalId":119895,"journal":{"name":"2019 9th International Electric Drives Production Conference (EDPC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123239662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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