Session: 03-03-03: Annual Congress-Wide Symposium on Additive Manufacturing III

Paper Number: 166507

Critical Review of Contributing Parameters, Predictive Models and Sensing Techniques for Prospective In-Situ Monitoring and Control of Residual Stress in Metal Additive Manufacturing (MAM) 

Residual stress is one of the key challenges facing Metal Additive Manufacturing(MAM), and it influences component performance and reliability a lot. In dealing with increased applications across industries, especially in MAM technologies, understanding and addressing residual stress becomes important. These stresses arise from complex thermal cycles, rapid cooling rates, and localized heating appearing during the process, which affect geometrical distortions, fatigue failures, and degraded mechanical properties. This review will discuss the critical role of residual stress in MAM and investigate its implication for material integrity.

The discussion outlined various controllable parameters from the additive manufacturing process that are responsible for residual stress formation. Some of the key parameters involve laser power, layer thickness, Hatch space, and scan pattern, among other factors. These parameters all have a direct effect on thermal gradients and cooling rates experienced by the material, which is essential when computing the resultant stress distribution. In that way, by varying these variables, it is possible to optimize the manufacturing conditions in order to minimize undesirable residual stresses, enhancing mechanical properties and dimensional precision of the final components.

Various models that can be used for monitoring in situ and controlling residual stresses in MAM are explored in detail. These models vary from simple analytical frameworks to complex numerical simulations that are capable of representing many of the complex thermo-mechanical behaviors arising in manufacturing. This review evaluates the effectiveness of such models in predicting residual stress development and correlating results with experimental observations. Limitations of the existing modeling approaches are discussed, along with further refinement that could result in better predictive accuracy.

In addition, advanced sensing techniques-which make real-time residual stress monitoring possible, will be highlighted as having importance. Such technologies as thermography, x-ray diffraction, and acoustic emission could provide instantaneous information on developing stress states in the material. Integration of sensing technologies with control algorithms will provide an adaptive process through which manufacturers can make prompt adjustments in processing parameters based on detected levels of stress. This proactive approach has a double advantage: it reduces residual stress and generally improves the quality and reliability of MAM components.

It summarizes state-of-the-art knowledge regarding residual stress in MAM, its in situ monitoring, and control; therefore, this review is a strong reference for both the researcher and practitioner. It has outlined the important areas that will be of future concern to the researchers. Great emphasis has been placed on in-depth understanding of the interaction between process parameters, residual stress, and material behavior. Results obtained in this review will contribute to the elaboration of improved methodologies for control, hence fostering progress toward additive manufacturing of metals, which is key to various industries.

Presenting Author: Farshad Samadpour Purdue University

Presenting Author Biography: I'm a PhD student in Mechanical Engineering at Purdue University, specializing in additive manufacturing. My research focuses on residual stress modeling, microstructure control, and process optimization in metal additive manufacturing. I have experience in finite element modeling, analytical modeling, and machine learning applications for manufacturing. I'm actively involved in university-industry collaborations to advance metal AM technologies.

Authors:

Farshad Samadpour Purdue University
Hazim El-Mounayri Purdue University