Optimization of process parameters and predicting surface finish of PLA in additive manufacturing—a neural network approach

Additive manufacturing (AM), also known as 3D printing, has revolutionized the industrial sector by enabling the production of intricate geometries and specialized parts. However, achieving optimal surface finish and mechanical properties in AM poses challenges due to factors like material properties and machine characteristics. Accurately predicting surface finish is essential for process optimization and minimizing post-processing efforts. This abstract presents an innovative approach to predict surface finish and tensile strength simultaneously in AM. Leveraging advanced machine learning techniques, predictive models are developed using a comprehensive dataset of process parameters and corresponding measurements. The dataset is generated through systematic experimentation in the fused deposition modelling method, focusing on printing speed, layer thickness, and infill density. These models offer significant benefits to the industry, allowing manufacturers to optimize process parameters for desired surface finish and mechanical properties concurrently. By reducing reliance on trial-and-error approaches, they enhance efficiency, productivity, and part quality while lowering costs and accelerating product development cycles.