Additive Manufacturing Processes Protocol Prediction by Artificial Intelligence using X-ray Computed Tomography data

• URL: http://arxiv.org/abs/2501.14306v1
• Date: Fri, 24 Jan 2025 08:05:49 GMT
• Title: Additive Manufacturing Processes Protocol Prediction by Artificial Intelligence using X-ray Computed Tomography data
• Authors: Sunita Khod, Akshay Dvivedi, Mayank Goswami,
• Abstract summary: This study includes three commercially available 3D printers for soft material printing based on the Material Extrusion (MEX) AM process. The samples are 3D printed for six different AM process parameters obtained by varying layer height and nozzle speed. The performance of the trained AI model is compared with the two software tools based on the classical thresholding method.
• Score: 4.943054375935879
• License:
• Abstract: The quality of the part fabricated from the Additive Manufacturing (AM) process depends upon the process parameters used, and therefore, optimization is required for apt quality. A methodology is proposed to set these parameters non-iteratively without human intervention. It utilizes Artificial Intelligence (AI) to fully automate the process, with the capability to self-train any apt AI model by further assimilating the training data.This study includes three commercially available 3D printers for soft material printing based on the Material Extrusion (MEX) AM process. The samples are 3D printed for six different AM process parameters obtained by varying layer height and nozzle speed. The novelty part of the methodology is incorporating an AI-based image segmentation step in the decision-making stage that uses quality inspected training data from the Non-Destructive Testing (NDT) method. The performance of the trained AI model is compared with the two software tools based on the classical thresholding method. The AI-based Artificial Neural Network (ANN) model is trained from NDT-assessed and AI-segmented data to automate the selection of optimized process parameters. The AI-based model is 99.3 % accurate, while the best available commercial classical image method is 83.44 % accurate. The best value of overall R for training ANN is 0.82. The MEX process gives a 22.06 % porosity error relative to the design. The NDT-data trained two AI models integrated into a series pipeline for optimal process parameters are proposed and verified by classical optimization and mechanical testing methods.

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