The widespread adoption of laser powder bed fusion (LPBF) additive manufacturing is hampered by process unreliability problems. Modeling the melt pool behavior in LPBF is crucial to develop process control methods. While data-driven models linking melt pool dynamics to specific process parameters have shown appreciable advancements, existing models often oversimplify these relationships as deterministic, failing to account for the inherent instability of LPBF processes. Such simplifications can lead to overconfident and unreliable predictions, potentially resulting in erroneous process decisions. To address this critical issue, we propose a probabilistic data-driven approach to melt pool modeling that incorporates process noise and uncertainty. Our framework formulates a problem that includes distribution approximation and uncertainty quantification. Specifically, the Gaussian distribution with higher order priors, aided with variational inference and importance sampling, is used to approximate the probability distribution of melt pool characteristics. The uncertainty inherent in both LPBF process data and the modeling approach itself are then decomposed and approximated by using Monte Carlo sampling. The melt pool model is improved further by using a novel grid-based representation for the neighborhood of a fusion point, and a neural network architecture designed for effective feature fusion. This approach not only refines the accuracy of the model but also quantifies the uncertainty of the predictions, thereby enabling more informed decision-making with reduced risk. Two potential applications, including LPBF process planning and anomaly detection, are discussed. The implementation of our model is available athttps://github.com/qihangGH/probabilistic_melt_pool_model. Note to Practitioners—Modeling the melt pool behavior in laser powder bed fusion (LPBF) processes is pivotal for enhancing its quality control. However, a problem is that most existing data-driven melt pool models learn melt pool behavior with a deterministic function, which predicts the same outputs if its inputs are the same. This deviates from the reality and neglects the uncertainty in LPBF processes. As a consequence, the quality control methods based on such melt pool models lack required reliability. In response to these challenges, this work proposes to model melt pool behavior by using probability distributions with deep learning techniques, which can quantify the uncertainty in both LPBF process data and data-driven models. Aided with an elegantly designed representation for the neighborhood of a fusion point as model input, and a neural network architecture that fuses multi-modal data, the proposed model achieves accurate melt pool size prediction results. More importantly, this work quantifies and decomposes the prediction uncertainty. By accounting for noise and parameter variations, the probabilistic modeling models developed herein offer a more robust foundation for LPBF quality control than the existing ones. They can be readily applied by practitioners to perform improved process planning, defect prognosis, and real-time anomaly detection tasks.

3-D printing, or additive manufacturing (AM), leverages 3-D computer-aided design models and numerical control to produce objects layer-by-layer, playing a key role in Industry 4.0 and Industry 5.0. Despite its potential to revolutionize manufacturing by creating complex structures more efficiently and cost-effectively, 3-D printing still faces quality issues due to a lack of sufficient data, resulting in improper process parameter settings and poor analyzability. This work introduces a data-driven and physics-assisted machine learning (DP-ML) approach for a 3-D-printed BeltClip object, integrating finite element analysis (FEA) and physics-informed machine learning (PIML). The proposed DP-ML framework provides a cost-effective and time-efficient data collection method using Digimat-AM and a warpage classification algorithm. The data collection begins with obtaining the STereoLithography (STL) file of the BeltClip object from Thingiverse and slicing it in Ultimaker© Cura, considering process parameters such as infill amount, toolpath pattern, layer height, print speed, and extrusion temperature. The resulting G-code file is then input into Digimat-AM for further parameter setting and analysis. In Digimat-AM, glass fiber-filled and unfilled material types are set, undergoing the virtual 3-D printing process, followed by a warpage analysis of the printed BeltClip. The collected 3-D printing data is used to build ML models—deep neural network (DNN), decision tree (DT), support vector machine (SVM), logistic regression (LR), and random forest. The DNN contains three architectures—DNN-1, DNN-2, and DNN-3. Based on the metrics of precision, recall, F1-score, and accuracy, DNN-3 outperforms the others and is chosen for the warpage classification algorithm. The presented DP-ML approach is compared with the state-of-the-art methods and shows a promising capability to predicting warpage, optimizing process parameters, and improving the overall quality and efficiency of a 3-D-printed BeltClip.

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), also called 3-D printing, is a supporting technology in social manufacturing that has gained significant attention recently. As the AM industry grows, collecting and analyzing data are essential to ensure product quality, process efficiency, and cost-effectiveness. However, obtaining experimental data is challenging owing to cost and time constraints. Therefore, cost-effective and time-efficient strategies for collecting AM data are urgently required. This study proposes a novel data-collection approach that integrates the concept of finite element analysis (FEA) and physics-informed machine learning (PIML). We begin by discussing the importance of data collection in AM and the associated challenges. We then present various types of data that can be collected in AM, including the 3-D models and end-to-end data. End-to-end data comprise experimental data (i.e., sensors and images) and simulation data. Moreover, we present a case study that demonstrates the generation of simulation data and provides a detailed analysis of warpage. The STereoLithography (STL) file format of the BeltClip object from the Thingiverse possesses slicing through the Ultimaker© Cura software. The resulting G-code file is input to the Digimat-AM platform for virtual simulation of the BeltClip printing process. Digimat-AM, as a FEA simulation tool, then generates observational sample data. These data function as a roadmap for understanding the application of physical information for learning, which constitutes the observational bias aspect of PIML. The observational data obtained from the Digimat-AM is suggested for building a machine-learning model. Finally, we conclude with a discussion of inductive and learning biases in the prediction, control, and optimization aspects of AM.

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.