Research on Automated Defect Classification Based on Visual Sensing and Convolutional Neural Network-Support Vector Machine for GTA-Assisted Droplet Deposition Manufacturing Process

Chen Ma,Haifei Dang,Jun Du,Zhengying Wei,Pengfei He,Minbo Jiang

doi:10.3390/met11040639

Abstract

This paper proposes a novel metal additive manufacturing process, which is a composition of gas tungsten arc (GTA) and droplet deposition manufacturing (DDM). Due to complex physical metallurgical processes involved, such as droplet impact, spreading, surface pre-melting, etc., defects, including lack of fusion, overflow and discontinuity of deposited layers always occur. To assure the quality of GTA-assisted DDM-ed parts, online monitoring based on visual sensing has been implemented. The current study also focuses on automated defect classification to avoid low efficiency and bias of manual recognition by the way of convolutional neural network-support vector machine (CNN-SVM). The best accuracy of 98.9%, with an execution time of about 12 milliseconds to handle an image, proved our model can be enough to use in real-time feedback control of the process.

Highlights

Additive manufacturing (AM) is revolutionary compared to traditional processing methods in creating complex 3D-shaped components
Among the different additive manufacturing techniques, wire and arc additive manufacturing (WAAM), which combines an electric arc as heat source and wire as the feedstock material, is suitable to produce large metallic parts owing to the high deposition rates being significantly larger than powder as the feedstock [1]
Comparing with wires as feedstock, we developed a new metal additive manufacturing process in this paper, which uses fused droplets as feedstock combined with variable-polarity gas tungsten arc (GTA) to form aluminum alloy

Summary

Introduction

Additive manufacturing (AM) is revolutionary compared to traditional processing methods in creating complex 3D-shaped components. Among the different additive manufacturing techniques, wire and arc additive manufacturing (WAAM), which combines an electric arc as heat source and wire as the feedstock material, is suitable to produce large metallic parts owing to the high deposition rates being significantly larger than powder as the feedstock [1]. Gas metal arc (GMA), gas tungsten arc (GTA) and plasma arc (PA) are the most used processes in WAAM. They all need external filler materials and a high energy-density arc heat source under an inert shielding gas [2]. Comparing with wires as feedstock, we developed a new metal additive manufacturing process in this paper, which uses fused droplets as feedstock combined with variable-polarity GTA to form aluminum alloy.

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