Integration of Machining and Measuring Processes Using On-Machine Measurement Technology

作     者：Myeong Woo Cho Tae Il Seo Dong Sam Park 

作者机构：Department of Mechanical Engineering Inha UniversityTechnology Development & Assistance Center for SMIs Korea Institute of Industrial TechnologyDepartment of Mechanical Engineering University of Incheon Inchon KoreaBucheon KoreaIncheon Korea 

出 版 物：《厦门大学学报（自然科学版）》 (Journal of Xiamen University：Natural Science)

年 卷 期：2002年第41卷第S1期

页      面：102-103页

核心收录：

学科分类：08[工学] 0802[工学-机械工程] 080201[工学-机械制造及其自动化] 

主　　题：CAD/AM/CAI integration OMM (On-Machine Measurem ent) inspection planning error compensation neural network 

摘      要：This paper presents an integration methodology for ma chining and measuring processes using OMM (On-Machine Measurement) technology b ased on CAD/CAM/CAI integration concept. OMM uses a CNC machining center as a me asuring station by changing the tools into measuring probes such as touch-type, laser and vision. Although the measurement accuracy is not good compared to tha t of the CMM (Coordinate Measuring Machine), there are distinctive advantages us ing OMM in real situation. In this paper, two topics are handled to show the eff ectiveness of the machining and measuring process integration: (1) inspection pl anning strategy for sculptured surface machining and (2) tool path compensation for profile milling process. For the first topic, as a first step, effective mea suring point locations are determined to obtain optimum results for given sampli ng numbers. Two measuring point selection methods are suggested based on the CAD /CAM/CAI integration concept: (1) by the prediction of cutting errors and (2) by considering cutter contact points to avoid the measurement errors caused by cus ps. As a next step, the TSP (Traveling Salesman Problem) algorithm is applied to minimize the probe moving distance. Appropriate simulations and experiments are performed to verify the proposed inspection planning strategy, and the results are analyzed. For the second topic, a methodology for profile milling error comp ensation is presented by using an ANN (Artificial Neural Network) model trained by the inspection database of OMM system. First, geometric and thermal errors of the machining center are compensated using a closed-loop configuration for the improvement of machining and inspection accuracy. The probing errors are also t aken into account. Then, a specimen workpiece is machined and then the machi ning surface error distribution is measured on the machine using touch-type pro be. In order to efficiently analyze the machining errors, two characteristic err or parameters (W er