Hardness, grainsize and porosity formation prediction on the Laser Metal Deposition of AISI 304 stainless steel. (December 2018)

• Record Type: Journal Article
• Authors: Arrizubieta, Jon Iñaki; Lamikiz, Aitzol; Cortina, Magdalena; Ukar, Eneko; Alberdi, Amaia
• Is Part Of: International journal of machine tools & manufacture. Volume 135(2018)
• Page Start: 53
• Page End: 64
• Publication Date: 2018-12
• Subjects: Laser Metal Deposition -- Simulation -- Pore formation -- Grainsize -- Hardness; Machine-tools -- Periodicals; Manufacturing processes -- Periodicals; Machines-outils -- Périodiques; Fabrication -- Périodiques; Electronic journals; 621.902
• DOI: 10.1016/j.ijmachtools.2018.08.004
• Languages: English
• ISSNs: 0890-6955

Abstract

Abstract: The presented numerical model solves the heat and mass transfer equations in the Laser Metal Deposition process and based on the evolution of the thermal field predicts the grainsize, the resulting hardness and evaluates the pores formation probability in an AISI 304 stainless steel. For this purpose, in a first step, the model calculates the shape of the deposited material and the variations of the temperature field. In a second step, and based on the evolution of the thermal field, the model calculates the resulting hardness of the deposited material, the grainsize and the porosity formation probability after the deposition process. Numerical results are experimentally validated, and good agreement is obtained. Consequently, besides predicting the geometry of the resulting part and the evolution of the thermal field, the developed model enables to evaluate the quality of the deposited material. Therefore, the optimum process conditions and strategy when depositing AISI 304 stainless steel can be determined without initial trial-and-error tests. Highlights: A model that simulates the Laser Material Deposition (LMD) process has been developed and validated experimentally. The model is capable of determining the Dendrite Arm Spacing of the deposited material based on the cooling rate. The model predicts the hardness of the deposited material based on the Hall-Petch relation and the sensitization effect. The model predicts the pore formation based on the trapped gas due to a fast solidification and the material shrinkage. The model enables to optimize the deposition strategy and minimize defects such as pores.