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research-article

Prescriptive Data-Analytical Modeling of Selective Laser Melting Processes for Accuracy Improvement

[+] Author and Article Information
He Luan

Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA, 90089, USA
hluan@usc.edu

Marco Grasso

Department of Mechanical Engineering, Politecnico di Milano, 20156, Milano, Italy
marcoluigi.grasso@polimi.it

Bianca M. Colosimo

Department of Mechanical Engineering, Politecnico di Milano, 20156, Milano, Italy
biancamaria.colosimo@polimi.it

Qiang Huang

Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA, 90089, USA
qiang.huang@usc.edu

1Corresponding author.

ASME doi:10.1115/1.4041709 History: Received March 20, 2018; Revised October 01, 2018

Abstract

Selective laser melting (SLM) has the ability to produce 3D lightweight metal parts with complex shapes. To ensure build quality for freeform shapes, extensive experimental and investigations have been conducted to tackle shape complexity issues. Due to higher accuracy requirement for metal parts, surface roughness, laser beam positioning error, and part location effect can all be coupled with shape accuracy of SLM build products. This study develops a data-driven modeling approach as a promising solution for shape accuracy improvement in SLM processes. To address the shape complexity issue, a prescriptive modeling approach is adopted to minimize shape deviations in SLM process through compensating CAD models, as opposed to changing process parameters. It allows us to predict and control a wide range of shapes starting from a limited set of measurements on basic benchmark geometries. An error decomposition and compensation scheme is developed to decouple the influence from different error components and to reduce the shape deviations caused by materials shrinkage, laser beam positioning error and other location effects simultaneously via an integrated modeling and compensation framework. Experimentation and data collection are conducted to investigate error sources and to validate the developed modeling and accuracy control methods.

Copyright (c) 2018 by ASME
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