An adaptive methodology for monitoring and controlling of precision machining and on-machine inspection

Jong-I Mou, Purdue University


In this research, an adaptive methodology for monitoring and controlling the accuracy of CNC machine tools in material removal and on-machine inspection was developed. A reference part based error modeling technique, robust search algorithms, and an adaptive thermal effect tracking method were proposed to extend the capability of a machine tool to sense and compensate for errors during a sequence of operations. The reference part based error modeling technique involves mathematical models and measurements closely related to real parts and therefore reduces the uncertainty in error estimation and compensation. Rigid body kinematics was used to derive the error models that correlate the resultant errors in the machine tool workspace to the imperfection and misalignment of position elements and workpieces. The derived error models are capable of decoupling the machine tool's position errors and workpiece misalignment.^ To improve the robustness and effectiveness of the derived error models, robust search algorithms were developed to identify the appropriate measuring points, with respect to the designated tolerance, for arbitrary shapes of reference parts. The identified measurement points were least sensitive to measurement uncertainty and variation in load induced errors. The search algorithms utilize computer simulation with limited information abstracted from previous measurements. Thus, the measurement effort involved in error modeling has been significantly reduced. Experimental results demonstrate the feasibility and the effectiveness of the algorithms with desired accuracy.^ An adaptive algorithm was developed to cope with the time-varying thermal effect on position accuracy of machine tools. A state observer was constructed to track and predict the thermal induced errors and continuously modify the coefficients of the error model. The state observer uses the information derived from a partial recalibration process as feedback to keep track of the machine tool error migration without interfering with the existing fixtures and parts. A full scale recalibration process can be conducted, after a part is finished, to fine tune the state estimation. As the error model coefficients are determined, the model can be used to correct the on-machine probing data or to generate the compensation signal for precision machining. Therefore, an adaptive environment was provided for error monitoring and controlling for precision machining and on-machine inspection. ^




Major Professor: C. R. Liu, Purdue University.

Subject Area

Engineering, Industrial|Engineering, Mechanical

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