Additive manufacturing (AM) can build up complex parts in a layer-by-layer manner, which is a kind of novel and flexible production technology. The special manufacturing capability of AM shows great application potential in various fields. However, an open-loop control method cannot guarantee the reliability and repeatability of an AM process. Defects often occur to deteriorate product quality and lead to material and time waste, which hinders the development of AM industry. In this regard, a lot of efforts have been made to make an AM process more controllable. This work proposes an AM control framework that divides the related studies into three feedback loops, including the in-situ monitoring of process defects, fault diagnosis of 3-D printers, and closed-loop control of an AM process. These three loops constitute the inspection and control of AM from the machine level to product level. Specifically, the measurement requirements for monitoring techniques, defect detection, fault diagnosis, and closed-loop control are summarized. The challenges and future trends in realizing a more reliable and repeatable AM process are discussed. Note to Practitioners —This survey is motivated by urgent need to solve product quality problems in additive manufacturing (AM) caused by open-loop control. Three feedback loops can be established to solve them. The first one is defect detection that inspects part quality during fabrication. The second one is the fault diagnosis of a 3-D printer that monitors the health and operation conditions of its actuators. The last one is closed-loop control that improves AM process reliability and repeatability by regulating process variables in real time. These three loops are all based on the feedback signals of in-situ monitoring systems. This paper reviews the related studies and provides guidance for establishing the monitoring systems, performing defect detection and fault diagnosis, and designing closed-loop control systems, which helps realize more reliable and repeatable AM.

Additive manufacturing (AM), commonly known as 3D printing, is a rapidly growing technology. Guaranteeing the quality and mechanical strength of printed parts is an active research area. Most of the existing methods adopt open-loop-like Machine Learning (ML) algorithms that can be used only for predicting properties of printed parts without any quality assuring mechanism. Some closed-loop approaches, on the other hand, consider a single adjustable processing parameter to monitor the properties of a printed part. This work proposes both open-loop and closed-loop ML models and integrates them to monitor the effects of processing parameters on the quality of printed parts. By using experimental 3D printing data, an open-loop classification model formulates the relationship between processing parameters and printed part properties. Then, a closed-loop control algorithm that combines open-loop ML models and a fuzzy inference system is constructed to generate optimized processing parameters for better printed part properties. The proposed system realizes the application of a closed-loop control system to AM.

Additive manufacturing (AM), also known as 3D printing, is a new manufacturing trend showing promising progress over time in the era of Industry 4.0. So far, various research has been done for increasing the reliability and productivity of a 3D printing process. In this regard, reviewing the existing concepts and forwarding novel research directions are important. This paper reviews and summarizes the process flow, technologies, configurations, and monitoring of AM. It started with the general AM process flow, followed by the definitions and the working principles of various AM technologies and the possible AM configurations, such as traditional and robot-assisted AM. Then, defect detection, fault diagnosis, and open-loop and closed-loop control systems in AM are discussed. It is noted that introducing robots into the assisting mechanism of AM increases the reliability and productivity of the manufacturing process. Moreover, integrating machine learning and conventional control algorithms ensures a closed-loop control in AM, a promising control strategy. Lastly, the paper addresses the challenges and future trends.

The widespread adoption of green energy resources worldwide, such as photovoltaic (PV) systems to generate green and renewable power, has prompted safety and reliability concerns. One of these concerns is fault diagnostics, which is needed to manage the reliability and output of PV systems. Severe PV faults make detecting faults challenging because of drastic weather circumstances. This research article presents a novel deep stack-based ensemble learning (DSEL) approach for diagnosing PV array faults. The DSEL approach compromises three deep-learning models, namely, deep neural network, long short-term memory, and Bi-directional long short-term memory, as base learners for diagnosing PV faults. To better analyze PV arrays, we use multinomial logistic regression as a meta-learner to combine the predictions of base learners. This study considers open circuits, short circuits, partial shading, bridge, degradation faults, and incorporation of the MPPT algorithm. The DSEL algorithm offers reliable, precise, and accurate PV-fault diagnostics for noiseless and noisy data. The proposed DSEL approach is quantitatively examined and compared to eight prior machine-learning and deep-learning-based PV-fault classification methodologies by using a simulated dataset. The findings show that the proposed approach outperforms other techniques, achieving 98.62% accuracy for fault detection with noiseless data and 94.87% accuracy with noisy data. The study revealed that the DSEL algorithm retains a strong generalization potential for detecting PV faults while enhancing prediction accuracy. Hence, the proposed DSEL algorithm detects and categorizes PV array faults more efficiently, reliably, and accurately.

The intelligent manufacturing is a complex engineering system, and the cyber–physical systems (CPSs) and the industrial Internet are the preliminary infrastructures. When cyber–physical–social systems (CPSSs) are formed by extending CPS into the social aspect, Societies 5.0 era is coming. In the Societies 5.0 era, social manufacturing (SM) is an innovative manufacturing solution for intelligent manufacturing. In this article, a survey on SM is introduced. It includes the definition and theory of SM, and comparison between SM and other manufacturing paradigms. Moreover, the key supporting technologies are presented, which can be used to realize SM, such as blockchain, 3-D printing, and big data. Then, the applications of SM to industries are illustrated. The SM has broad application prospects in the high-end customized, distributed manufacturing, and other intelligent manufacturing. Finally, the challenges and future trends are discussed.