Parts design and process optimization

Artificial intelligence and additive manufacturing are primary drivers of Industry 4.0, which is reshaping the manufacturing industry. Based on the progressive layer-by-layer principle, additive manufacturing allows for the manufacturing of mechanical parts with a high degree of complexity. In this chapter, a deep learning neural network (DLNN) is introduced to rationalize the effect of cellular structure design factors as well as process variables on physical and mechanical properties utilizing laser powder bed fusion. The models developed were validated and utilized to create process maps. For both design and process optimization, the trained deep learning neural network model showed the highest accuracy. Deep learning neural networks were found to be an effective technique for predicting material properties from limited data sets, as per the findings.