Overview of Residual Stress in Metal Additive Manufacturing: Detection Techniques, Numerical Simulation, and Mitigation Strategies

Researchers from Shantou University have reported a review of residual stresses in metal additive manufacturing: detection techniques, numerical simulations, and mitigation strategies. The relevant paper titled "A comprehensive review of residual stress in metal additive manufacturing: detection techniques, numerical simulations, and mitigation strategies" was published in the Journal of the Brazilian Society of Mechanical Sciences and Engineering.

As an emerging manufacturing process, metal additive manufacturing (AM) technology has completely changed traditional design practices and achieved innovation in the field of materials engineering, including the production of metal parts with complex structures, improved design freedom, and significantly reduced production costs. However, the cyclic heating and cooling process can generate residual stresses inside the material, which may lead to material deformation and affect the dimensional accuracy and mechanical properties of the formed parts. Therefore, a comprehensive understanding of the basic mechanism of metal AM can provide insights for obtaining high-performance products. In this review, researchers first briefly introduce several common metal AM technologies and elucidate the mechanisms that lead to residual stress (RS) generation. Subsequently, the latest developments in commonly used and online detection methods for monitoring RS were outlined. In addition, thermodynamic coupling modeling methods for RS prediction were introduced, as well as several strategies aimed at improving simulation computational efficiency. These details can help scholars determine appropriate techniques. This article also outlines some methods for managing residual stress, with a particular emphasis on the effectiveness of energy field assisted methods in reducing RS and improving mechanical properties in metal AM processes. Finally, this article summarizes the prospects and provides specific directions for future research work.

Figure 1 Failure modes caused by RS in metal AM parts: a-b cracks, c-e deformation, f-g delamination.

Figure 2 Classification of Metal AM Processes.

Figure 3 Schematic diagram of temperature gradient mechanism of thermal stress.

Figure 4a shows a typical RS measurement experiment, and b shows the spatial resolution and penetration of RS measurement technology.

Figure 5 Thermal analysis flowchart.

Figure 6 Multi scale additive component modeling method.

Figure 7a shows the analysis workflow of MISM, and b compares the deformation of MISM and experimental measurements along two lines on Ti6Al4V standard parts.

Figure 8a shows a schematic diagram of L-PBF with auxiliary molten metal flow, while Figure 8b illustrates the effect of static molten metal flow on the flow pattern of the molten pool. The influence of magnetic field (MF) on the evolution of grain and microstructure morphology in AM: columnar growth in the absence of MF, MF acting on columnar grains, columnar to equiaxed transformation under the action of MF, and microstructure results under the action of MF. EBSD analysis of AlSi10Mg alloy made from L-PBF under different MF strengths: g 0T and h 0.2T; Microstructure of i WAAM Inconel 625 alloy deposition: without MF and with MF; Refining grain size in Ti6Al4V alloy made of L-ED.

Figure 9 Schematic diagram of deformation assisted AM: a. Rolling b SP、c LSP、d MHP。

Figure 10: The influence of scanning mode on RS and deformation: a schematic diagram of scanning strategy, b temperature, c von Mises stress, d residual stress, e deformation.

This review article provides an overview of RS in metal AM. Due to local heat input and rapid solidification, RS is inevitably generated during the metal AM process. Therefore, understanding the formation mechanism of RS is crucial. The temperature gradient mechanism, cooling phase mode mechanism, and structural changes during phase transition clearly explain the partial origin of RS at both macroscopic and microscopic scales. As discussed, the detection methods for RS were subsequently analyzed, each with its own advantages and disadvantages, different spatial resolutions, and capabilities related to part size. The experimental method for measuring residual stress is feasible, but it is expensive and time-consuming. Therefore, reliable deformation and RS prediction models are very useful for improving the process optimization of metal AM technology. The RS in metal AM can be controlled through methods during the process, such as preheating, energy field assistance, and parameter optimization methods. Some viewpoints can be summarized as follows:

In the AM process, once RS occurs, it is often too late for post process measurement to save the product. Most existing technologies rely on predictive simulation and post-processing analysis, but these methods cannot accurately reflect the stress evolution during the manufacturing process. The online detection of RS can explore the generation and evolution of stress, and track and monitor in-situ strain through special detection equipment, including strain and displacement sensors, visual monitoring technology, and thermodynamic monitoring mentioned in this article. The summary of these technologies not only provides technical references for real-time monitoring of residual stress, but also points out the direction for the development of residual stress monitoring technology. Future research requires fast and automatic decision-making based on online detection results, and precise control of multiple process parameters that induce high RS. Meanwhile, adopting multi-sensor technology will be an important way to achieve AM process control, improve monitoring accuracy, and achieve closed-loop control.

In the field of prediction and simulation, the numerical modeling of macroscopic RS mainly uses finite element models to establish thermodynamic models. At present, mainstream RS numerical models mainly focus on improving prediction accuracy in complex environments while minimizing computational time costs. However, existing RS prediction models still have limitations in terms of computational accuracy and efficiency. Ensuring high computational accuracy remains the most important; However, addressing the efficiency issue of model solving is a challenge that requires further exploration in future research. Multiscale numerical models have enormous potential in this regard. In the future, combining predictive capabilities with process monitoring, computer-aided simulation, and process optimization will help to make more effective predictions for RS.

During the AM process, excessive RS will have a negative impact on the fatigue strength of the material, thereby seriously affecting the stability and performance of the parts. Therefore, it is necessary to investigate potential risks related to RS and determine effective mitigation strategies to ensure that RS is controlled during the manufacturing process. Although optimizing process parameters is a method of controlling stress from the source, frequent experiments often result in resource waste. The energy field assisted method in AM provides an opportunity for effective control of RS. Different types of fields have different mechanisms of action on the solidification process of metals in the melt pool; However, most current research mainly focuses on a single field. Future research should explore the interaction laws of multi field synchronous assistance of various forming materials in metal additive manufacturing technology, in order to broaden the application prospects of metal additive manufacturing technology in different fields.

Source: Yangtze River Delta Laser Alliance