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

TL;DR

This paper presents an AI-driven methodology for optimizing additive manufacturing process parameters using X-ray CT data, achieving high accuracy in parameter selection and improving part quality without human intervention.

Contribution

The study introduces an innovative AI-based image segmentation step integrated into AM process optimization, utilizing NDT data for fully automated parameter prediction.

Findings

AI model accuracy of 99.3% in parameter prediction

Compared to classical methods, AI improves segmentation accuracy from 83.44% to 99.3%

The methodology reduces porosity error by 22.06% in printed parts

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.