Automated estimation of time and cost for determining optimal machining plans



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The process of taking a solid model and producing a machined part requires the time and skillset of a range of professionals, and several hours of part review, process planning, and production. Much of this time is spent creating a methodical step-by-step process plan for creating the part from stock. The work presented here is part of a software package that performs automated process planning for a solid model. This software is capable of not only greatly decreasing the planning time for part production, but also give valuable feedback about the part to the designer, as a time and cost associated with manufacturing the part. In order to generate these parameters, we must simulate all aspects of creating the part. Presented here are models that replicate these aspects. For milling, an automatic tool selection method is presented. Given this tooling, another model uses specific information about the part to generate a tool path length. A machining simulation model calculates relevant parameters, and estimates a time for machining given the tool and tool path determined previously. This time value, along with the machining parameters, is used to estimate the wear to the tooling used in the process. Using the machining time and the tool wear a cost for the process can be determined. Other models capture the time of non-machining production times, and all times are combined with billing rates of machines and operators to present an overall cost for machining a feature on a part. If several such features are required to create the part, these models are applied to each feature, until a complete process plan has been created. Further post processing of the process plan is required. Using a list of available machines, this work considers creating the part on all machines, or any combination of these machines. Candidates for creating the part on specific machines are generated and filtered based on time and cost to keep only the best candidates. These candidates can be returned to the user, who can evaluate, and choose, one candidate. Results are presented for several example parts.