Machine Learning Assisted Prediction of the Manufacturability of Laser-Based Powder Bed Fusion Process

2019 ◽  
Author(s):  
Ying Zhang ◽  
Guoying Dong ◽  
Sheng Yang ◽  
Yaoyao Fiona Zhao
Keyword(s):  
Machine Learning ◽  
Manufacturing Process ◽  
Fusion Process ◽  
Design Stage ◽  
High Threshold ◽  
Powder Bed Fusion ◽  
Powder Bed ◽  
Early Design Stage ◽  
Novel Approach ◽  
Shape Complexity

Abstract Laser-based powder bed fusion (LPBF) process is a type of additive manufacturing process which is able to produce complex metal geometries. The fast development of laser-based powder bed fusion process offers new opportunities to the industries. Comparing to the conventional manufacturing process, LPBF offers more freedom on the shape complexity and hierarchical complexity. Even though the LPBF process has many advantages, there are still many constraints on LPBF. At the current stage, LPBF process still has a very high threshold for industrial application. It requires designers to have extensive knowledge of LPBF process to make the design manufacturable. The need for the automatic manufacturability analysis in the early design stage is essential. In this paper, a novel approach on analyzing the manufacturability of LPBF process is introduced. The machine learning model is developed to predict the manufacturability of LPBF. The unique dataset is established as the training examples. The proposed method achieves very competitive accuracy on analyzing the manufacturability of LBPF. The limitation and future work will be discussed in the end.

scholarly journals In Situ Monitoring Systems of The SLM Process: On the Need to Develop Machine Learning Models for Data Processing

Crystals ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 524
Author(s):  
Pinku Yadav ◽  
Olivier Rigo ◽  
Corinne Arvieu ◽  
Emilie Le Guen ◽  
Eric Lacoste
Keyword(s):  
Machine Learning ◽  
Defect Detection ◽  
In Situ Monitoring ◽  
Fusion Process ◽  
Monitoring Systems ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
In Situ Sensing

In recent years, technological advancements have led to the industrialization of the laser powder bed fusion process. Despite all of the advancements, quality assurance, reliability, and lack of repeatability of the laser powder bed fusion process still hinder risk-averse industries from adopting it wholeheartedly. The process-induced defects or drifts can have a detrimental effect on the quality of the final part, which could lead to catastrophic failure of the finished part. It led to the development of in situ monitoring systems to effectively monitor the process signatures during printing. Nevertheless, post-processing of the in situ data and defect detection in an automated fashion are major challenges. Nowadays, many studies have been focused on incorporating machine learning approaches to solve this problem and develop a feedback control loop system to monitor the process in real-time. In our study, we review the types of process defects that can be monitored via process signatures captured by in situ sensing devices and recent advancements in the field of data analytics for easy and automated defect detection. We also discuss the working principles of the most common in situ sensing sensors to have a better understanding of the process. Commercially available in situ monitoring devices on laser powder bed fusion systems are also reviewed. This review is inspired by the work of Grasso and Colosimo, which presented an overall review of powder bed fusion technology.

scholarly journals Maturity Assessment of Laser Powder Bed Fusion Process Chain Modelling and Simulation

2021 ◽  
pp. 34-45
Author(s):  
Anas Yaghi ◽  
Shukri Afazov ◽  
Matteo Villa
Keyword(s):  
Case Studies ◽  
Manufacturing Process ◽  
Modelling And Simulation ◽  
Fusion Process ◽  
Design Solution ◽  
Powder Bed Fusion ◽  
Process Chain ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Process Chains

This paper presents case studies of additive manufacturing process chains including laser powder bed fusion and post-processes. The presented case studies are used to assess the maturity of the manufacturing process chains using a Modelling and Simulation Readiness Level Scale. The results from the assessment have shown that the maturity of the modelling and simulation of laser powder bed fusion process chains lies between the stage of applied research and development and the stage of being instrumental, with high reliance on modelling and simulation experts. This means that the laser powder bed fusion (L-PBF) process chain modelling and simulation can support low-risk development, with high reliance on modelling and simulation experts, making them suitable for qualitative assessment, alternative design/solution ranking, defining the design structure, constraining the design space and replacing some experimental trials. This shows that further maturation is required before the modelling and simulation methods and codes are well recognised as best practice in the industry and are part of operational process control at any stage of the supply chain.

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