ReviewMaterials (Basel, Switzerland)2024
Experimental, Computational, and Machine Learning Methods for Prediction of Residual Stresses in Laser Additive Manufacturing: A Critical Review.
Review in Materials (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Investigation of Microstructure, Hardness, and Wear Behavior of Hardfacing Produced by Wire Laser Additive Manufacturing.Materials (Basel, Switzerland) · 2026Article
- A Finite Element Simulation-Informed Machine Learning Framework for Screening Average Thermal Stress Responses in SLM-Fabricated 316L Stainless Steel.Materials (Basel, Switzerland) · 2026Article
- Post-Processing Optimization of MDLP-Fabricated 316L Stainless Steel: Microstructural Evolution and Mechanical Properties.Materials (Basel, Switzerland) · 2026Article
- Artificial Intelligence in Metal Additive Manufacturing: Applications in Design, Process Modeling, Monitoring, and Quality Optimization.Materials (Basel, Switzerland) · 2026Review
- Investigation of Crack Propagation in Locally Thermal-Treated Cast Iron.Materials (Basel, Switzerland) · 2025Article
- Article
- A Robust Recurrent Neural Networks-Based Surrogate Model for Thermal History and Melt Pool Characteristics in Directed Energy Deposition.Materials (Basel, Switzerland) · 2024Article
- Effect of Pre-Heating on Residual Stresses and Deformation in Laser-Based Directed Energy Deposition Repair: A Comparative Analysis.Materials (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.