Fault diagnosis is vital in manufacturing system, since early detections on the emerging problem can save invaluable time and cost. With the development of smart manufacturing, the data-driven fault diagnosis becomes a hot topic. However, the traditional data-driven fault diagnosis methods rely on the features extracted by experts. The feature extraction process is an exhausted work and greatly impacts the final result. Deep learning (DL) provides an effective way to extract the features of raw data automatically. Convolutional neural network (CNN) is an effective DL method. In this study, a new CNN based on LeNet-5 is proposed for fault diagnosis. Through a conversion method converting signals into two-dimensional (2-D) images, the proposed method can extract the features of the converted 2-D images and eliminate the effect of handcrafted features. The proposed method which is tested on three famous datasets, including motor bearing dataset, self-priming centrifugal pump dataset, and axial piston hydraulic pump dataset, has achieved prediction accuracy of 99.79%, 99.481%, and 100%, respectively. The results have been compared with other DL and traditional methods, including adaptive deep CNN, sparse filter, deep belief network, and support vector machine. The comparisons show that the proposed CNN-based data-driven fault diagnosis method has achieved significant improvements.

In recent years, infrared thermographic (IRT) technology has experienced notable advancements and found widespread applications in various fields, such as renewable industry, electronic industry, construction, aviation, and healthcare. IRT technology is used for defect detection due to its non-contact, efficient, and high-resolution methods, which enhance product quality and reliability. This review offers an overview of active IRT principles. It comprehensively examines four categories based on the type of heat sources employed: pulsed thermography (PT), lock-in thermography (LT), ultrasonically stimulated vibration thermography (UVT), and eddy current thermography (ECT). Furthermore, the review explores the application of IRT imaging in the renewable energy sector, with a specific focus on the photovoltaic (PV) industry. The integration of IRT imaging and deep learning techniques presents an efficient and highly accurate solution for detecting defects in PV panels, playing a critical role in monitoring and maintaining PV energy systems. In addition, the application of infrared thermal imaging technology in electronic industry is reviewed. In the development and manufacturing of electronic products, IRT imaging is used to assess the performance and thermal characteristics of circuit boards. It aids in detecting potential material and manufacturing defects, ensuring product quality. Furthermore, the research discusses algorithmic detection for PV panels, the excitation sources used in electronic industry inspections, and infrared wavelengths. Finally, the review analyzes the advantages and challenges of IRT imaging concerning excitation sources, the PV industry, the electronics industry, and artificial intelligence (AI). It provides insights into critical issues requiring attention in future research endeavors.

Electrical Discharge Machining (EDM) is a machining method commonly used to produce complex shapes and deep holes by eroding hard metals with an electric arc. There is a growing demand for process simulation using finite element models in order to improve the quality and efficiency of EDM, to reduce costs, to improve resource efficiency, and to facilitate its application in critical areas such as aerospace and mechanical engineering. Finite element models have greatly improved the prediction accuracy of EDM processes, simulated complex hybrid machining processes, and provided important guidance for the optimization of EDM processes. This paper systematically reviews the research progress of finite element modeling for EDM. Finite element method modeling is evaluated mainly in terms of four indicators: material removal rate, surface roughness, tool wear ratio, and recast layer thickness. Firstly, the importance and application of EDM are described, and the EDM finite element method modeling and its advantages are summarized. Then, the single-spark simulation model and the multi-spark simulation model of EDM are compared and discussed. Among the mainstream finite element models, the prediction error of the material removal rate for single-spark simulation ranges from 8.2% to 14.75%, while the prediction error of the recast layer thickness for multi-spark simulation can be as low as 1.98%. Finally, the applications of finite element modeling in EDM hybrid machining processes’ performance prediction and new material machining are summarized, and future research directions and trends in EDM finite element modeling are predicted.

Integrated process planning and scheduling (IPPS) is important for modern manufacturing companies to achieve manufacturing efficiency and improve resource utilization. Meanwhile, multiple objectives need to be considered in the realistic decision-making process for manufacturing systems. Based on the above realistic manufacturing system requirements, it becomes increasingly important to develop effective methods to deal with multi-objective IPPS problems. Therefore, an improved NSGA-II (INSGA-II) algorithm is proposed in this research, which uses the fast non-dominated ranking method for multiple optimization objectives as an assignment scheme for fitness. A multi-layer integrated coding method is adopted to address the characteristics of the integrated optimization model, which involves many optimization parameters and interactions. Elite and mutation strategies are employed during the evolutionary process to enhance population diversity and the quality of solutions. An external archive is also used to store and update the Pareto solution. The experimental results on the Kim test set demonstrate the effectiveness of the proposed INSGA-II algorithm.

Arc-welding robots are widely used in the production of automotive bracket parts. The large amounts of fumes and toxic gases generated during arc welding can affect the inspection results, as well as causing health problems, and the product needs to be sent to an additional checkpoint for manual inspection. In this work, the framework of a robotic-vision-based defect inspection system was proposed and developed in a cloud–edge computing environment, which can drastically reduce the manual labor required for visual inspection, minimizing the risks associated with human error and accidents. Firstly, a passive vision sensor was installed on the end joint of the arc-welding robot, the imaging module was designed to capture bracket weldments images after the arc-welding process, and datasets with qualified images were created in the production line for deep-learning-based research on steel surface defects. To enhance the detection precision, a redesigned lightweight inspection network was then employed, while a fast computation speed was ensured through the utilization of a cloud–edge-computing computational framework. Finally, virtual simulation and Internet of Things technologies were adopted to develop the inspection and control software in order to monitor the whole process remotely. The experimental results demonstrate that the proposed approach can realize the faster identification of quality issues, achieving higher steel production efficiency and economic profits.