Thermal cutting analysis on grain size distribution using probabilistic FEM

• Main Authors: Graf, M. (Author), Ismail M.H (Author), Manurung, Y.H.P (Author), Mohamad Syakir, A.G (Author), Muhd Faiz, M. (Author), Sulaiman, M.S (Author)
• Format: Article
• Language: English
• Published: Institute of Physics Publishing, 2020
• Online Access: View Fulltext in Publisher; View in Scopus

 This paper investigates the capability of probabilistic FEM to predict grain size distributions due to thermal cutting process. The application of transient heat source causes non-uniform temperature distribution across the parent metal that can lead to non-uniform expansion and contraction during heating and cooling cycle. This phenomenon will induce thermal stresses to the workpiece that can subsequently lead to unwanted cutting deformation. Therefore, prediction of temperature distribution is important in order to control the amount of heat required in the cutting process. The simulation was computed through probabilistic FEM using Monte Carlo method based on non-linear thermo-elastic-plastic numerical analysis. The probabilistic FEM was carried out by varying the input power. In this study, the simulation method had been executed by using FEM software MSC MARC. The simulation analysis was also executed using customized material input module in order to take the grain growth into consideration during the cutting process. The additional subroutine of grain growth was introduced and implemented in order to predict the grain size distribution. The material used for the simulation was stainless steel 316L with the thickness of 2 mm. Based on the results obtained, it was found out that slight differences of the results were achieved between deterministic and probabilistic methods. The small observable differences occurred due to the probabilistic method was only executed to fluctuate the input power, while the other process parameters were still unchanged. Nevertheless, the Monte Carlo method was successfully integrated into the normal simulation which then transforming it into probabilistic analysis. Thus, through probabilistic method, reliability on prediction could be increased in which the prediction would be closer to reality. © 2020 IOP Publishing Ltd. All rights reserved.