A Conceptual Framework and Design Architectures for Neural Network-Based Adaptive and Dynamic Process Planning Proposal for A Dissertation by

Although feature-based process planning plays a vital role in automating and integrating design and manufacturing for efficient production, its off-line properties prohibit the shop floor controller from rapidly coping with dynamic shop floor status such as unexpected production errors and rush orders. The objective of the paper is to address a neural network-based adaptive and dynamic approach to process planning that can generate the needed process plans based on shop floor status. To this end, a conceptual framework of the approach is suggested and then the functional architectures of dynamic planning models are specified to demonstrate the applicability of the proposed approach. The dynamic planning models are constructed as neural network forms, and then embedded into each process feature in the process plan. The shop floor controller will execute them to determine required machines, cutting tools, process parameters, tool paths, NC codes just before the associated process feature is machined. Owing to the dynamic nature of process planning, the shop floor controller will increase flexibility and efficiency in unexpected situations occurring.