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Establishing a mathematical model to predict and compensate for the thermal error of CNC machine tools is a commonly used approach. Most existing methods, especially those based on deep learning algorithms, have complicated models that need huge amounts of training data and lack interpretability. Therefore, this paper proposes a regularized regression algorithm for thermal error modeling, which has a simple structure that can be easily implemented in practice and has good interpretability. In addition, automatic temperature-sensitive variable selection is realized. Specifically, the least absolute regression method combined with two regularization techniques is used to establish the thermal error prediction model. The prediction effects are compared with state-of-the-art algorithms, including deep-learning-based algorithms. Comparison of the results shows that the proposed method has the best prediction accuracy and robustness. Finally, compensation experiments with the established model are conducted and prove the effectiveness of the proposed modeling method.

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A sliding sensor based on a fiber Bragg grating (FBG) was proposed to enable mechanical fingers to softly grasp an object. FBG strain sensors are embedded in a polymeric material as a sensing element to obtain sliding information. This study expounded the structural design of the sliding sensor and the mechanism of sliding sensation, which were verified using the finite element simulation. The static and dynamic performances of the sliding sensor were studied experimentally. Finally, the sensing signals were processed using fuzzy logic. Results show that the FBG sliding sensor with a simple structure has high sensitivity and can reliably detect the contact state of the target object, thereby providing a design scheme for the study of the sliding sense of mechanical fingers.

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