A contradiction solving method for complex product conceptual design based on deep learning and technological evolution patterns

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.