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Keywords:

Smart, Selective Laser Melting

Abstract:

The main objective of this dissertation is to improve the quality and robustness and to be able to estimate the quality of the Selective Laser Melting (SLM) process. This is realised by carefully examining the complete SLM process chain and by improving the intelligence of the system by predicting the quality and improving the robustness. To improve the robustness and quality, the job preparation is implemented in an in-house developed software to create job files for an SLM machine based on a 3D CAD model. Combining the recognition of critical zones with optimised parameters for each zone results in a higher local and more repeatable quality level for the production of these features. This software is tested for overhanging structures and thin wall structures. The methodology however is generic and can in future be implemented for any possible feature. This improved intelligence is combined with the developed monitoring system to make a first step in the field of automated quality estimation. By transferring logged data into maps and comparing them with CT images of the produced parts some correlations between the two images could be detected. These correlations are however influenced by a lot of different phenomena which make that the automated quality control needs further research to be applicable in industry.