A contradiction solving method for complex product conceptual design based on deep learning and technological evolution patterns |
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| Affiliation: | 1. Zhejiang Provincial Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China;2. Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China;1. School of Reliability and Systems Engineering, Beijing University of Aeronautics and Astronautics, Beijing, PR China;2. Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, PR China;3. State Key Laboratory of Virtual Reality Technology and System, Beijing, PR China;1. State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;2. School of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China;3. National NC System Engineering Research Center, Huazhong University of Science and Technology, Wuhan 430074, China;1. ISAE-SUPMECA, Quartz Laboratory, Saint-Ouen, France;2. Roberval Laboratory, University of Technology of Compiègne, Compiègne, France;3. Laboratory of Mechanics of Sousse, National Engineering School of Sousse, University of Sousse, Sousse, Tunisia;1. School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China;2. Beijing Xinghang Mechanical-Electrical Eqiupment Co., Ltd., Beijing 100074, China;3. AVIC Manufacturing Technology Institute, Beijing 100024, China;4. School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China;1. School of Hydraulic Engineering, Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, PR China;2. College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, PR China |
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| Abstract: | Contradictions caused by the various design constraints present increasing challenges to efficiency and innovation in product development. TRIZ provides Inventive Principles (IPs) and Contradiction Matrix that are the most frequently applied in conflict resolution. However, the high-level abstraction and subjective selection of IPs inhibit achieving the transformation process from paradoxical states to physical structures. To fill this gap, a contradiction solving method by integrating deep learning and technological evolution patterns for product conceptual design is proposed, which illustrates the mechanism of contradiction transition from the perspective of system evolution and supplies a systematic and model-based design approach. Firstly, generic engineering parameters are extracted to define the underlying contradictions transformed from critical defects which are found out through function modeling and root-conflict analysis. Then, a fully-connected deep neural network with excellent performance is developed to uncover the non-linear relationships between engineering parameters and evolution patterns. Finally, an evolution tree based on the predicted patterns is constructed to visualize transformation potentials of a technical system and help designers generate innovative specific solutions. In addition, a case study concerning design conflict resolution for beat-up system of three-dimensional tubular weaving machine is used to validate the adaptability and reliability of the proposed approach. |
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| Keywords: | Contradiction solving Conceptual design TRIZ Deep learning Technological evolution patterns |
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