Search

The soldering process for aerospace applications follows stringent requirements and standards to ensure the reliability and safety of electronic connections in aerospace systems. For this reason, the quality control phase plays an important role to guarantee requirements compliance. This process often requires manual control since technicians' knowledge is fundamental to obtain effective quality check results. In this context, the authors have developed a new open source dataset (SolDef_AI) to implement an innovative methodology for printed circuit board (PCB) defect detection exploiting the Mask R-CNN algorithm. The presented open source dataset aims to overcome the challenges associated with the availability of datasets for model training in this specific research and electronics industrial field. The dataset is open source and available online. [ABSTRACT FROM AUTHOR]

In this paper, both numerical and analytical method were developed for computing, in strip or plate rolling, the distribution of roll pressure, rolling force, and rolling torque (from which also rolling power can be estimated), assuming an homogeneous deformation of the rolled material. Unlike other similar models present in the literature, which solve the resulting rolling differential equation for the roll pressure, the model presented in this work solves the problem for the horizontal force. In this way, it is possible to avoid the calculation of the derivative of material flow stress curve, which is not always analytically easy and possible (i.e., point material flow stress data). The proposed numerical model is based on the friction law proposed by Chen and Kobayashi while the analytical one is based on the simple shear friction model and brings to useful analytical formulas for a quick calculation of rolling torque and force. Moreover, a relationship between the shear friction factor and Coulomb friction coefficient in rolling was found. The developed models show good agreement with experimental measures, in terms of rolling force and torque, found in literature. [ABSTRACT FROM AUTHOR]

Tool wear is one of the main issues encountered in the manufacturing industry during machining operations. In traditional machining for chip removal, it is necessary to know the wear of the tool since the modification of the geometric characteristics of the cutting edge makes it unable to guarantee the quality required during machining. Knowing and measuring the wear of tools is possible through artificial intelligence (AI), a branch of information technology that, by interpreting the behaviour of the tool, predicts its wear through intelligent systems. AI systems include techniques such as machine learning, deep learning and neural networks, which allow for the study, construction and implementation of algorithms in order to understand, improve and optimize the wear process. The aim of this research work is to provide an overview of the recent years of development of tool wear monitoring through artificial intelligence in the general and essential requirements of offline and online methods. The last few years mainly refer to the last ten years, but with a few exceptions, for a better explanation of the topics covered. Therefore, the review identifies, in addition to the methods, the industrial sector to which the scientific article refers, the type of processing, the material processed, the tool used and the type of wear calculated. Publications are described in accordance with PRISMA-P (Preferred Reporting Items for Systematic review and Meta-Analysis Protocols). [ABSTRACT FROM AUTHOR]