Optimization of the machining of metallic additive manufacturing supports: first methodological approach

Autor: Vincent BENOIST, Maher BAILI, Lionel ARNAUD
Rok vydání: 2023
Předmět:
Zdroj: The International Journal of Advanced Manufacturing Technology.
ISSN: 1433-3015
0268-3768
Popis: Metal additive manufacturing is an active field of innova- tion, but for Selective Laser Melting (SLM), supports removal is a major constraint. For this technology, supports are strongly welded to the part, to tightly maintain the part and avoid distortion and also to evacuate the thermal load. Supports are usually optimized for their manual removal but machining is often applied and need to be more often used in order to improve post-processing produc- tivity. This paper proposes a full methodological approach to optimize the selection of the cutting parameters, cutting tools and the type of supports itself. The aim is to help the additive manufacturer to find among the numerous wide supports designs, the ones that would reduce the cost of machining, in terms of machining time and cutting tools degradation. This approach can also be used for the optimization of the design of lattice structures for their structures. Our results show that among the 11 designs tested, the honey- comb and squared pattern grid supports are the most efficiently machined, using the 8 teeth tangential milling among the 3 tools tested, with a good post machined surface roughness and tool’s health. The method takes into account low magnification optical analysis and an accelerometer sensor, easy to use even for SME. This paper also proposes and analyzes with this method a new kind of porous support.
Databáze: OpenAIRE
Externí odkaz: