In Industry 5.0, human-centric smart manufacturing prioritizes the needs of technologists to help enterprises sustain competitive advantages. In this context, design for manufacturing (DFM) plays an essential role, as it ensures the manufacturability of digital designs to deliver high-quality products. Due to novice designers’ limited manufacturing knowledge, the implementation of DFM depends on repeated and passive design iterations, placing a heavy burden on designers. Existing research on improving DFM focuses on manufacturability analysis, which only provides analysis results but ignores novice designers’ manufacturability needs for design modifications. To bridge the gap, this paper proposes a novel human-centric proactive DFM approach that aims to address designers’ manufacturability needs throughout the design process to reduce passive iterations and meet evolving industry demands. Specifically, considering multiple design parameters, a deep learning network is trained for 3D model generation and similarity calculation. Next, the learned network can support human-centric proactive DFM, which includes two parts: automated manufacturability guidance for incomplete designs and manufacturability analysis for complete designs. Through 3D model generation, incomplete designs can be completed and unmanufacturable designs can be modified. Furthermore, similarity calculation facilitates historical manufacturable case recommendation to meet designers’ needs in their decision-making. Experimental results show the efficacy of the approach, achieving accuracy improvements of 4.17% on the impeller dataset and 4% on the manufacturing feature dataset in manufacturability analysis, compared with state-of-the-art approaches. Application examples demonstrate its effectiveness to assist novice designers to proactively improve product manufacturability.

Design is a complex process and multifaceted knowledge is required in a collaborative environment. Especially, knowledge modeling and application across design and manufacturing domain can reduce automotive development iteration. However, it still a challenge for designer-oriented manufacturing knowledge reuse due to the lack of relationship capture for design contexts and their manufacturing constraints. To solve this problem, the paper presents a rule-enhanced collaborative design method, which can infer manufacturing constraints required in the specific design context. First, a framework of collaborative design is proposed. Then the ontology is used for knowledge modeling and rule construction. Finally, a simple case is illustrated to demonstrate the effectiveness of the method.