In recent decades, laser additive manufacturing has seen rapid development and has been applied to various fields, including the aerospace, automotive, and biomedical industries. However, the residual stresses that form during the manufacturing process can lead to defects in the printed parts, such as distortion and cracking. Therefore, accurately predicting residual stresses is crucial for preventing part failure and ensuring product quality. This critical review covers the fundamental aspects and formation mechanisms of residual stresses. It also extensively discusses the prediction of residual stresses utilizing experimental, computational, and machine learning methods. Finally, the review addresses the challenges and future directions in predicting residual stresses in laser additive manufacturing.

Coaxial wire-based laser metal deposition is a versatile and efficient additive process that can achieve a high deposition rate in the manufacturing of complex structures. In this paper, a three-beam coaxial wire system is studied, with particular attention to the effects of deposition height and laser defocusing on the resulting bead geometry. As the deposition standoff distance changes, so does the workpiece illumination proportion, which describes the ratio of energy going directly into the feedstock wire and into the substrate. Single titanium beads are deposited at varying defocus levels and deposition rates and the bead aspect ratio is measured and analyzed. Over the experimental settings, the defocusing level and deposition rate were found to have a significant effect on the resulting bead aspect ratio. As the defocusing level is increased away from the beam convergence plane, the spot size increases and the deposited track is wider and flatter. Process parameters can be used to tune the deposited material to a desired aspect ratio. In coaxial wire deposition, defocusing provides an adjustment mechanism to the distribution of heat between the wire and substrate and has an important impact on the resulting deposit.

Purpose: The melt pool created by a laser is one of the most important factors affecting the quality of the deposit in a laser metal deposition (LMD) process. The high‐intensity infrared (IR) radiation emitted by the melt pool saturates a conventional camera sensor preventing useful data acquisition. The purpose of this paper is to discuss the development of a low‐cost vision system to monitor the size of the melt pool for in‐process quality control of the deposit.Design/methodology/approach: According to the black body radiation theory, there is no radiation emitted in the ultraviolet (UV) region from the melt pool created in the LMD process. IR radiation and visible light are the only radiations inherent to the LMD process. UV illumination is utilized along with narrow band pass filters on a USB camera to achieve a clear image of the melt pool while IR radiation of the process is blocked out. The melt pool size and shape were closely monitored during the deposition process.Findings: A clear image of the melt pool was obtained using a relatively low‐cost imaging system during laser deposition process.Originality/value: Traditional approaches to vision systems in high‐intensity processes use a high‐speed video camera fitted with IR filters to prevent saturation of the camera sensor. Such systems are usually complex and expensive to run and maintain. This paper demonstrates an alternative and lower cost method to achieve in process monitoring in an LMD process.

Wire-based additive manufacturing (AM) is at the forefront of complex metal fabrication because of its scalability for large components, potential for high deposition rates, and ease of use. A common goal of wire directed energy deposition (DED) is preserving a stable process throughout deposition. If too little energy is put into the deposition, the wire will stub into the substrate and begin oscillating, creating turbulence within the meltpool. If too much energy exists, the wire will overheat, causing surface tension to take over and create liquid drips as opposed to a solid bead. This paper proposes a computer vision technique to work as both a state detection and event detection system for wire stability. The model utilizes intensity variations along with frame-to-frame difference calculations to determine process stability. Because the proposed model does not rely on machine learning techniques, it is possible for an individual to interpret and adjust as they see fit. The first part of this paper describes creation and implementation of the model. The model’s capability was then evaluated using a 1D laser power experiment, which generated a wide range of stability states across varying powers. The model’s accuracy was evaluated through 3D geometry data gathered from the experimentally deposited beads. The model proved to be both capable and accurate and has potential to be used as a real-time control system with future work.

Coaxial wire-based laser metal deposition is a versatile and efficient additive process that can achieve a high deposition rate in the manufacturing of complex structures. In this paper, a three-beam coaxial wire system is studied, with particular attention given to the effects of the deposition direction and laser beam orientation on the resulting bead geometry symmetry. With the three-beam laser delivery, the laser spot pattern is not always symmetric with respect to the deposition direction. Single titanium beads are deposited in different directions and at varying deposition rates, and the bead profile is quantitatively scored for multiple symmetry measures. Through an analysis of variance, the deposition direction and deposition rate were found to be insignificant with respect to the resulting bead symmetry for the developed measures. The bead symmetry and geometry are important factors in determining if a build is free of critical defects, and in this study, it is shown that the three-beam coaxial wire deposition setup is a directionally independent process.

Metal additive manufacturing (AM) processes are very complex and the process parameters required to fabricate quality parts can be very complicated and challenging to determine. For this reason, there is a continuous demand for AM simulations which can assist users to determine optimal process parameters in a timely manner. However, current commercial simulation packages are expensive and not designed with AM processes in mind, which makes it challenging to simulate an AM process. In addition, they are computationally intensive and usually require some high performance computing (HPC) system to run effectively. Besides, they take relatively more time, usually from several days to months, to simulate an AM process. This paper presents additive manufacturing simulator (AMS), a simulation tool built specifically with AM processes in mind, to address the challenges with current AM simulation tools. AMS is based on a three-tier client-server architecture, coupled with service oriented architecture's (SOA) publication and subscription model. It has the ability to scale to utilize available computational resources and automatically balances computational resources among multiple processors. Compared with current AM simulation systems, AMS improves performance of AM simulation considerably and can run on not only HPC systems, but also low-cost desktop computers.