Detc Dfm Process Planner S Assistant an Interactive and Iterative Approach to Automating Process Planning

Most existing approaches to computer aided process plan ning aim for full automation by searching for a plan that is opti mal with respect to a pre speci ed objective function Such full automation is often infeasible in practice for three reasons i the search space of potential plans is huge ii optimality metrics are often context sensitive and can only be elicited through user interaction iii because of the importance of process planning organizations are more interested in process planning assistants that support human expert process planners rather than stand alone process planners In this paper we present a process planner s assistant which helps the human process planning experts in coming up with process plans In order to achieve this the process plan ner s assistant must have access to the full search space of pro cess plans and the ability to modify plans in response to human criticism The former is provided by basing our system on ASU Features Testbed a comprehensive and systematic framework for recognizing and reasoning with features in machinable parts To support the latter the system is equipped with an incre mental and interactive search mechanism We will discuss the operational details of the resultant system called ASUPPA corresponding author INTRODUCTION Computer aided process planning CAPP is a key part of bridging the link between design and manufacturing Process planning involves determining the sequence of op erations to perform to manufacture a part given its descrip tion and the speci cation of the resources in the workshop It should take into account both technological and economic considerations some of which are hard constraints and some preferences This knowledge often represents both the ex perience and the know how of engineers specialists which di er from one company to another Most existing approaches to CAPP Britanik Gupta Hayes Kambhampati aim for full automation by searching for a plan that is optimal with re spect to a pre speci ed objective function Such approaches su er from three important limitations The search space for process plans is too large to fa cilitate an e cient systematic search This often ne cessitates restricting focus to a single interpretation of current design and nding the best plan under this xed feature set which may not be the best plan globally Second and perhaps more important these approaches assume the availability of a pre speci ed objective func tion for evaluating process plans In reality the eval