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After grinding, the machine tool spindle with high surface integrity has a significant impact on its subsequent service life. Therefore, it is necessary to create a reasonable process plan for the grinding process of the machine tool spindle. A hybrid method based on case-based reasoning (CBR) and process reasoning (PR) was proposed in this paper. The subjective and objective weights of feature attributes are determined in the CBR by the AHP method and the CRITIC method, respectively, and the similarities between the latest case and the retrieved case are calculated on the basis of the nearest neighbour algorithm. The most suitable process solution can be selected from the case database by means of CBR. PR is applied to solve the problem that the case cannot be retrieved through CBR or is unsatisfactory. In the case of the grinding machine spindle, the applicability of this technology was demonstrated. Consequently, the results revealed that the surface consistency of the spindle after grinding was substantially increased. A decision-making system based on the proposed approach was developed by using Qt 4.8.7 and SQLite 3. The results demonstrate the viability and efficacy of the hybrid CBR-PR method to rapidly generate process planning for specific parts. [ABSTRACT FROM AUTHOR]

Uneven surface quality usually occurs when grinding welds offline, which results non-uniform stress and then would damage the workpiece. In this paper, the robotic welding seam online grinding system based on laser vision sensor was proposed and built. A weld seam tracking software was developed and the data online interaction method of grinding system based on XML (Extensible Markup Language) file was applied. Firstly, hand-eye calibration model was built to convert data in the robot coordinate system. Then the weld profile information was extracted and stored in the data buffer area, and the coordinates of the robotic grinding point were transmitted through the self-developed weld grinding software. Finally, the vision system and the self-made grinding system were integrated at the end of the robot. The experiments were conducted to verify the reliability and practicality of this system and the proposed data interaction online method. [ABSTRACT FROM AUTHOR]

[Display omitted] • The local mean decomposition (LMD) algorithm is improved by the mirror extension method, moving average algorithm, and adaptive soft screening stopping criterion. • Considering the influence of the curvature change of the non-circular contour grinding surface on the chatter features, the signal features are automatically extracted according to the contour curve characteristics. • Considering the shortcomings of a single intelligent optimization algorithm, a new hybrid swarm intelligent optimization algorithm GAPSO-ABC is constructed. • The slight chatter in the incubation period of grinding chatter is effectively identified to avoid the influence on the workpiece. • In the high-speed grinding of camshaft non-circular contour, the detection and recognition method based on improved LMD and GAPSO-ABC-SVM can achieve an accuracy of 97.917 % for chatter recognition. The camshaft is a crucial part of the engine. However, its non-circular contour surface is prone to chatter in high-speed grinding, seriously affecting the processing quality and efficiency. Therefore, an online detection and recognition method for camshaft non-circular contour high-speed grinding chatter based on improved LMD and GAPSO-ABC-SVM is proposed. Firstly, the local mean decomposition (LMD) algorithm is improved by the mirror extension method, moving average algorithm, and adaptive soft screening stopping criterion. Its ability to deal with unsteady vibration signals is verified by simulation signals and experiments. Then, considering the influence of the curvature change of the non-circular contour grinding surface on the chatter features, the signal features are automatically extracted according to the contour curve characteristics. Finally, a recognition algorithm based on GAPSO-ABC-SVM is proposed to improve the accuracy and robustness of high-speed grinding chatter recognition. A new hybrid swarm intelligent optimization algorithm is proposed through the intelligent fusion of Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC) algorithms. The support vector machine (SVM) optimization is implemented by the hybrid swarm intelligence algorithm. In the high-speed grinding chatter verification experiment of camshaft non-circular contour, the detection and recognition method based on improved LMD and GAPSO-ABC-SVM can achieve an accuracy of 97.917 % for chatter recognition. And it has good fault tolerance. [ABSTRACT FROM AUTHOR]