Optimization of mechanical processing for sustainability

During the last few years, European manufacturing has encountered several challenges in obtaining Agile production and reducing environmental impact. The importance of joining the production with a Zero Defects and Zero Waste policy has been a difficult challenge. In a typical scenario, one or more defects could occur during the production of a manufacturing product, requiring the intervention of a specialized worker to detect the defects and decide whether the product should continue production or be wasted. The latter possibility increases material waste, and the presence of a specialized worker who needs to stop the production process from detecting the selected area could increase production consumption. The research objective was to study the possible implementation of an automated detective process that could increase the accuracy of detecting defects without increasing the consumption and time of application. Moreover, the possibility of identifying the defects present in a single piece allows one to direct the defective piece to a different production line or to be repaired without wasting the whole material. In this case, the detection time could increase the possibility of not wasting the material. The study investigates various information fusion techniques to enhance defect detection accuracy and efficiency. Several strategies are employed, including decision fusion based on Dempster-Shafer's theory with a new measure of uncertainty to improve reliability. The research also integrates Selective Kernel and Multi-Head Attention layers into defect detection and segmentation models to further boost their accuracy and effectiveness. During the testing phase, industrial datasets provided promising results and additional tests were performed across different domains to validate the effectiveness of the proposed approaches.