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RISDesign De-Identification of Thermal History for Collaborative Process-Defect Modeling of Directed Energy Deposition Processes
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: There is an urgent need for developing collaborative process-defect modeling in metal-based additive manufacturing (AM). This mainly stems from the high volume of training data needed to develop reliable machine learning ...
Efficient Distortion Prediction of Additively Manufactured Parts Using Bayesian Model Transfer Between Material Systems
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Distortion in laser-based additive manufacturing (LBAM) is a critical issue that adversely affects the geometric integrity of additively manufactured parts and generally exhibits a complicated dependence on the underlying ...
Deep Learning-Based Data Fusion Method for In Situ Porosity Detection in Laser-Based Additive Manufacturing
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Laser-based additive manufacturing (LBAM) provides unrivalled design freedom with the ability to manufacture complicated parts for a wide range of engineering applications. Melt pool is one of the most important signatures ...
Understanding the Effects of Process Conditions on Thermal–Defect Relationship: A Transfer Machine Learning Approach
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: This study aims to develop an intelligent, rapid porosity prediction methodology for additive manufacturing (AM) processes under varying process conditions by leveraging knowledge transfer from the existing process conditions. ...
Adaptive Thermal History De-Identification for Privacy-Preserving Data Sharing of Directed Energy Deposition Processes
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: In collaborative additive manufacturing (AM), sharing process data across multiple users can provide small- to medium-sized manufacturers (SMMs) with enlarged training data for part certification, facilitating accelerated ...
Morphological Dynamics-Based Anomaly Detection Towards In Situ Layer-Wise Certification for Directed Energy Deposition Processes
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: The process uncertainty induced quality issue remains the major challenge that hinders the wider adoption of additive manufacturing (AM) technology. The defects occurred significantly compromise structural integrity and ...
Physics-Informed Approximation of Internal Thermal History for Surface Deformation Predictions in Wire Arc Directed Energy Deposition
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: This work presents a physics-informed fusion methodology for deformation detection using multi-sensor thermal data. A challenge with additive manufacturing (AM) is that abnormalities commonly occur due to rapid changes in ...
Quantifying Geometric Accuracy With Unsupervised Machine Learning: Using Self-Organizing Map on Fused Filament Fabrication Additive Manufacturing Parts
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Although complex geometries are attainable with additive manufacturing (AM), a major barrier preventing its use in mission-critical applications is the lack of geometric accuracy of AM parts. Existing geometric dimensioning ...
Layer-Wise Modeling and Anomaly Detection for Laser-Based Additive Manufacturing
Publisher: American Society of Mechanical Engineers (ASME)
Abstract: Additive manufacturing (AM) is a novel fabrication technique capable of producing highly complex parts. Nevertheless, a major challenge is the quality assurance of the AM fabricated parts. While there are several ways of ...
Multi-Objective Accelerated Process Optimization of Part Geometric Accuracy in Additive Manufacturing
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: The goal of this work is to minimize geometric inaccuracies in parts printed using a fused filament fabrication (FFF) additive manufacturing (AM) process by optimizing the process parameters settings. This is a challenging ...