ASSESSMENT OF MACHINING MODELS: PROGRESS REPORT

Progress in developing and assessing predictive modeling of machining processes has been hindered by the extremely localized nonlinear physical phenomena that occur in machining and the many different types of models ranging from theoretical to empirical. The difficulty in assessing models has been cited by industry as the major barrier to use of modern machining models. Current practice in industry is to machine and change tools conservatively, or to conduct costly empirical studies for a limited selection of tools and coolants. The Assessment of Machining Models project will assess the ability of modern machining models to predict the outputs of machining processes based upon a consistent, well measured calibration data set. The data set is nearly complete and is to be used in benchmarking the predictive capability of machining models in blind tests. This paper presents the project motivation, goals, and representative calibration data set results. The next steps in the effort include release of the calibration data, solicitation and collection of predictions, and evaluation and reporting of results.