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A system that integrates design and planning for mechanical assemblies is presented. The system integrates neural network computing that captures designer's design concept and rule-based system to generate a task-level assembly plan automatically. The design concept is expressed by a standard pattern format representing qualitative assembly information. A neural network model together with feature-based model translates the input pattern into a preliminary boundary representation (B-rep). Based on a refinement B-rep assembly representation, assembly plans are generated for practical use in a single-robot assembly workcell. A feasible assembly plan that minimizes tool changes and subassembly reorientations is generated from the system. A robust part collision detection algorithm to generate the precedence relationships among the assembly's components is included in the system. By contrast with many assembly planning systems that used a prolonged question-and-answering session or required knowledge beyond what is typically available in the design database, an automated assembly planning system presented here draws input relationships directly from the conceptual design and the geometry of the assembly. The system developed under this study extracts all reasoning information from the product model and permits the components to be assembled in a multitude of directions. Several experiments illustrate the effectiveness of the designed assembly planning system.
Chen et al. (Mon,) studied this question.