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Design for Manufacturing (DfM) is becoming increasingly important in computer-aided design (CAD) due to the growing complexity of products. Automatically evaluating design rules for manufacturability from CAD dimensions and geometry is one effective approach for DfM. However, implementing automated design-rule-checks requires coding complex programs on CAD systems and the expertise to implement them, which make it difficult to adopt the approach, or implement product-specific design-rules. In this study, to mitigate this barrier, we developed a visual programming environment for the design-rule programs and a graph neural network (GNN) based recommender system that assists users to create the visual program. The proposed visual programming environment allows users to make design rules by placing and connecting shape recognition and measurement function nodes. This simplifies and improves the programming interface. Furthermore, the proposed recommender system predicts the subsequent function node that users will append, which reduces the user’s cognitive load of choosing the right function nodes. By employing proposed GNN architecture in the visual program recommender system, the input-output relationships between the function nodes and their input arguments are naturally taken into account to produce accurate recommendations. We evaluated the performance of the proposed recommender in the off-line experiment and the experiment in real-world use. The results in both experiments demonstrate that the proposed GNN based visual program recommender system can suggest the function nodes in sufficient accuracy, which contributes to improved productivity of implementing automatic CAD design-rule-checks.

We developed an “Insight CAD system” as one of the design support technologies. The purpose of the Insight CAD system development is to check design rules automatically on 3DCAD, and to provide information about the violation part or the violation list for designers. In this paper, we report the rule check on the evaluation of assemblability. First, we developed a weldability function and generated two digitized rules. One rule is related to the distance between the welding lines, and the other is related to the insertion space of the welding torch. Next, we generated two rules on the assemblability during bolt fastening. One rule is related to the diameter of the hole into which the bolt inserts, the other is related to the position of the hole. We evaluated the four newly developed rules using a simplified model and found that the violation parts can be identified automatically with respect to the four newly developed rules. This technology can help designers to shorten the check time, reduce rework from manufacturing to design, and improve product quality.