Application of the Deep CNN-Based Method in Industrial System for Wire Marking Identification

Andrzej Szajna, Mariusz Kostrzewski, Krzysztof Ciebiera, Roman Stryjski and Waldemar Woźniak
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Andrzej Szajna: DTP Ltd., 66-002 Zielona Góra, Poland
Mariusz Kostrzewski: Faculty of Transport, Warsaw University of Technology, 00-662 Warsaw, Poland
Krzysztof Ciebiera: DTP Ltd., 66-002 Zielona Góra, Poland
Roman Stryjski: Faculty of Mechanical Engineering, University of Zielona Góra, 65-516 Zielona Gora, Poland
Waldemar Woźniak: Faculty of Mechanical Engineering, University of Zielona Góra, 65-516 Zielona Gora, Poland

Energies, 2021, vol. 14, issue 12, 1-35

Abstract: Industry 4.0, a term invented by Wolfgang Wahlster in Germany, is celebrating its 10th anniversary in 2021. Still, the digitalization of the production environment is one of the hottest topics in the computer science departments at universities and companies. Optimization of production processes or redefinition of the production concepts is meaningful in light of the current industrial and research agendas. Both the mentioned optimization and redefinition are considered in numerous subtopics and technologies. One of the most significant topics in these areas is the newest findings and applications of artificial intelligence (AI)—machine learning (ML) and deep convolutional neural networks (DCNNs). The authors invented a method and device that supports the wiring assembly in the control cabinet production process, namely, the Wire Label Reader (WLR) industrial system. The implementation of this device was a big technical challenge. It required very advanced IT technologies, ML, image recognition, and DCNN as well. This paper focuses on an in-depth description of the underlying methodology of this device, its construction, and foremostly, the assembly industrial processes, through which this device is implemented. It was significant for the authors to validate the usability of the device within mentioned production processes and to express both advantages and challenges connected to such assembly process development. The authors noted that in-depth studies connected to the effects of AI applications in the presented area are sparse. Further, the idea of the WLR device is presented while also including results of DCNN training (with recognition results of 99.7% although challenging conditions), the device implementation in the wire assembly production process, and its users’ opinions. The authors have analyzed how the WLR affects assembly process time and energy consumption, and accordingly, the advantages and challenges of the device. Among the most impressive results of the WLR implementation in the assembly process one can be mentioned—the device ensures significant process time reduction regardless of the number of characters printed on a wire.

Keywords: wiring; assembly; production; control cabinet; wire label; wire marking; machine learning; CNN; DNN; DCNN; industry 4.0 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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