To enhance the accuracy of CNC machines for the request of modern industry, an effective static/quasi-static error compensation system composing of an element-free interpolation algorithm based on the Galerkin method for error prediction, a recursive software compensation procedure, and an NC-code converting software, is developed. Through automatically analyzing the machining path, the new error prediction method taking into the consideration of the fact that machine structure is non-rigid, can efficiently on-line determine the position errors of the cutter for compensation without computing a complex error model. The predicted errors are then compensated based on a recursive compensation algorithm. Finally, a compensated NC program will be automatically generated by the NC-code converting software for the precision machining process. Because of the advantage of element-free theory, the error prediction method can flexibly and irregularly distributing nodal points for accurate error prediction for a machine with complex error distribution characteristics throughout the workspace. To verify the algorithm and the developed system, cutting experiments were conducted in this study, and the results have shown the success of the proposed error compensation system.

Technology development trends towards the ability to manufacture ever smaller parts and feature sizes with increased precision and decreased cost. Micro machining is one of the important manufacturing methods to fulfill the requirements from the industry. The objective of this paper is to develop an on-machine error measurement system that can identify the micro machining errors for error compensation so that the machining accuracy of a meso-scale machine tool (mMT) can be enhanced. Because of the difficulty in handling and repositioning the miniature workpiece, the error measurement system should be non-contact and on-machine executable. To meet this requirement, a vision-based error measurement system integrating image re-constructive technology, camera pixel correction, and model comparison algorithm error was developed in this study. The proposed measurement system consists of a CCD with CCTV lens, a precision 3-DOF platform, image re-construction sub-system, and contour error calculation sub-system. By adopting Canny Edge Detection algorithm and camera pixel calibration method, the contour of a machined workpiece can be identified and compared to the pixel-based theoretical contour model of the workpiece to determine the micro machining errors. Because the system does not have to remove the machined workpiece from the CNC machine tool, errors due to re-installing and re-positioning can be avoided. To prove the feasibility of the developed algorithm and system, measurement results obtained from the vision-based measurement system were compared with the measurements of CMM, and error compensation experiment conducted on a 3-DOF mMT was also conducted. The results have shown the good feasibility and effectiveness of the developed system.

Micro machining plays an important role in the manufacturing of miniature products which are made of various materials with complex 3D shapes and tight machining tolerance. To further improve the accuracy of a micro machining process without increasing the manufacturing cost of a micro machine tool, an effective machining error measurement method and a software-based compensation method are essential. To avoid introducing additional errors caused by the re-installment of the workpiece, the measurement and compensation method should be on-machine conducted. In addition, because the contour of a miniature workpiece machined with a micro machining process is very tiny, the measurement method should be non-contact. By integrating the image re-constructive method, camera pixel correction, coordinate transformation, the error identification algorithm, and trajectory auto-correction method, a vision-based error measurement and compensation method that can on-machine inspect the micro machining errors and automatically generate an error-corrected numerical control (NC) program for error compensation was developed in this study. With the use of the Canny edge detection algorithm and camera pixel calibration, the edges of the contour of a machined workpiece were identified and used to re-construct the actual contour of the work piece. The actual contour was then mapped to the theoretical contour to identify the actual cutting points and compute the machining errors. With the use of a moving matching window and calculation of the similarity between the actual and theoretical contour, the errors between the actual cutting points and theoretical cutting points were calculated and used to correct the NC program. With the use of the error-corrected NC program, the accuracy of a micro machining process can be effectively improved. To prove the feasibility and effectiveness of the proposed methods, micro-milling experiments on a micro machine tool were conducted, and the results have demonstrated the success of the micro-machining accuracy enhancement for micro machine tools.

To enhance the accuracy of CNC machines for the request of modern industry, an effective static/quasi-static error compensation system composed of an element-free interpolation algorithm based on the Galerkin method for error prediction, a recursive software compensation procedure, and an NC-code converting software, is developed. Through automatically analyzing the machining path, the new error prediction method takes into consideration the fact that the machine structure is non-rigid, and can efficiently determine the position errors of the cutter for compensation without computing a complex error model on-line. The predicted errors are then compensated based on a recursive compensation algorithm. Finally, a compensated NC program will be automatically generated by the NC-code converting software for the precision machining process. Because of the advantage of the element-free theory, the error prediction method can flexibly and irregularly distribute nodal points for accurate error prediction for a machine with complex error distribution characteristics throughout the workspace. To verify the algorithm and the developed system, cutting experiments were conducted in this study, and the results have shown the success of the proposed error compensation system.

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