Studying the cutting temperature is critical for unlocking the secrets of sawblade wear, lifespan, and the metallurgical alterations beneath the surface. This paper describes an investigation into the temperature of 45 steel during dry sawing, using a cemented carbide circular saw blade under various cutting conditions. A temperature acquisition system was developed, enabling the determination of the average temperature of the arc zone in the workpiece and the temperature of the sawtooth tip via a semi-automated thermocouple measurement and an embedded dynamic artificial thermocouple method, respectively. Results obtained from these two methods indicate a positive correlation between the sawing temperature and the saw blade speed and feed rate, with an optimal combination of cutting process parameters identified for maintaining stability within reasonable ranges. Finite element simulations reveal a cyclical fluctuation in temperature along the workpiece surface and sawtooth, with a gradual decrease after an increase in the intermittent step, and confirm the relationship between the sawing temperature and the saw blade and feed rates observed experimentally. Overall, this study presents valuable insights into the temperature changes occurring during the sawing process, with important implications for improving productivity and maintaining stability in industrial applications.

To optimize the rigidity and dynamic mechanical properties of a sawing machine and improve its processing quality and stability, a design method for the sawing machine’s gearbox was proposed. First, a lightweight design of the gearbox was realized by topology optimization. Second, the sensitivity of different design variables of the new gearbox was determined via sensitivity analysis of the objective function. Finally, multi-objective optimization was used to obtain the optimal solution for the gearbox. Considering the complexity of the internal structure of the gearbox assembly and the accuracy of the numerical calculation process, a modeling method with mass points was proposed. A comparison between the numerical calculation results and the operation mode analysis revealed that the former was accurate and can be applied to the verification of the optimized gearbox. By optimizing the vibration signals before and after, and the analysis of the end face quality of the workpiece, the results revealed that the optimized gearbox has a significantly reduced amplitude under various operating conditions. In addition, the vibration stability was improved, and the end face quality of the workpiece was significantly enhanced compared to that before optimization. This study serves as a theoretical reference for multi-body dynamics modeling and optimization of machine tools, and also outlines technical solutions for high-speed stable cutting with sawing machines.

Stainless steel pipes, as the main material for oil and chemical transportation systems, have essential surface quality, and the surface quality of steel pipes after pickling-cleaning is usually inspected manually. With the acceleration of production pace, improvement of product quality, and manpower shortages, manual inspection methods can no longer meet current demands. Therefore, a vision-based method for detecting surface defects on stainless steel pipes is proposed. First, a detection device for the spiral conveyance of steel pipes was designed through simulation. Then, a YOLO-DSDS object detection model was proposed, which incorporates a depth aware convolution (DAC) module into the Backbone network to improve feature extraction capability. During the feature extraction process, a downsampling module was designed that simultaneously adds Shuffle Attention (SA) and Spatial Pyramid Pooling Fast (SPPF) to enhance the contextual information in the feature maps. Finally, the Wise-IoU (WIoU) loss function was used to calculate the loss value, improving the overall performance of the detector. Experimental results show that this detection method achieved a mean Average Precision (mAP) of 90.37% on a self-made dataset, improving by 5.58% and 1.77% compared to YOLOv7 and YOLOv9 respectively. In the publicly available NEU-DET and GC10-DE datasets, the model achieved mAP values of 80.03% and 77.84%, respectively, which is superior to other models.

Seamless stainless steel pipes (SSSP) are widely used in aerospace, petrochemical, and other fields, making the quality assessment of their surfaces particularly important. However, defect detection on the inner surface of SSSP is challenging, such as the scarcity of samples and the blurring of defect boundaries. Therefore, this paper proposes a method for surface defect segmentation of steel pipes based on superpixel prior knowledge and builds a classification model to achieve the segmentation task. Firstly, the images are superpixel clustered by Simple Linear Iterative Clustering (SLIC) with Edge Constraints and each superpixel is created as a description vector incorporating edge information. Then, the local description vectors are input into a support vector machine (SVM) for classification, and the foreground-background graph is constructed using confidence as priori knowledge. Finally, the graph model is constructed by co-constructing the terminal weights from the Gaussian Mixture Model (GMM) estimates and the SVM regression confidence, while to improve the edge segmentation accuracy, the neighborhood difference matrix is co-constructed with the superpixel mean and Canny edge image as the neighborhood weights, and the defects are segmented by using the minimum graph cut algorithm. Experimental results show that this method still demonstrates excellent accuracy and robustness even with a limited number of samples, achieving an intersection over union (IoU) of 87.94% on the self-made dataset, which outperforms other methods.