Process Parameter Optimization of Additively Manufactured Parts Using Intelligent Manufacturing

Additive manufacturing is the technique of combining materials layer by and process parameter optimization a method used popularly for achieving desired quality part. In this paper, four input parameters (layer height, infill density, pattern, number perimeter walls) along with their settings were chosen to maximize tensile strength given Taguchi DOE was generate an L27 orthogonal array which helped fabricate 27 parts on Ender 3 V2 fused deposition modeling (FDM) printer. The ultimate testing machine test all samples respective values. Next, Microsoft Azure ML database predict values various using data obtained from as input. Linear regression applied dataset web service deployed through API key generated find optimal both output parameters. optimum value 22.69 MPa at height 0.28 mm, density 100%, pattern honeycomb, walls 4. paper ends conclusions drawn future research directions.