Abstract

Melt pool temperature contains abundant information on metallurgical and mechanical aspects of products produced by additive manufacturing. Forecasting melt pool temperature profile during a process can help in reducing microstructural porosity and residual stresses. Although analytical and numerical models were reported, the performance of these are questionable when applied in real-time. Hence, we developed data-driven models to address this challenge, for continuous forecasting layer-wise melt pool temperature using a hybrid deep learning technique. The melt pool temperature forecasting by the proposed CNN-LSTM model is found to be better than other benchmark models in terms of accuracy and efficiency. The model results have shown that combining CNN and LSTM networks can extract the spatial and temporal information from the melt pool temperature data. Further, the proposed model results are compared with existing statistical and machine learning models. The performance measures of the proposed CNN-LSTM model indicate a greater potential for in-situ monitoring of additive manufacturing process.

Highlights

  • Metal additive manufacturing (AM) processes of directed energy deposition (DED) offer numerous possibilities for producing complex parts in aerospace and automotive industries without design constraints

  • The performance of the proposed model for forecasting melt pool temperature is evaluated from various metrics

  • The performance metrics of various statistical, machine learning and deep learning models are summarized in Tables 4, 6 and 7 respectively

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Summary

Introduction

Metal additive manufacturing (AM) processes of directed energy deposition (DED) offer numerous possibilities for producing complex parts in aerospace and automotive industries without design constraints. AM offers four key advantages that include design flexibility, sustainability, higher accuracy and efficiency, and faster production cycles. AM techniques have brought key transformations in aerospace and automotive industries. Boeing used more than 600 additively manufactured parts in its aircraft 777X. The BMW group has produced over 300,000 additively manufactured parts in just one year [1].

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