The effect of mesh parameters on computational cost and results in simulation of milling in Inconel 718

Felipe Chagas Rodrigues De Souza,Pedro Henrique Pires França,Felipe Dos Anjos Rodrigues Campos,Leonardo Rosa Ribeiro Da Silva

doi:10.4025/actascitechnol.v43i1.50363

Felipe Chagas Rodrigues De Souza, Pedro Henrique Pires França + Show 2 more

Open Access

https://doi.org/10.4025/actascitechnol.v43i1.50363

Copy DOI

Abstract

The Finite Element Method analysis of machining processes has become a ubiquitous feature to the area, however, there sometimes occur considerable deviations between experimental and simulated results due to the inherent complexity of the process. The basis for such may conceivably be related to imprecisions in the material and friction modelling, besides improper setup of mesh parameters. Elements should be small enough to allow for the proper representation of the chip formation, but taking into account that the computational time increases accordingly with mesh downsizing. Simulations of the milling process of Inconel 718 were conducted using the software Thirdwave AdvantEdge under different cutting conditions for three different meshes. Power and temperature output were compared to experimental results, most of which were measured via Hall-effect sensors and thermographic camera, respectively. The tool cutting edge radius was found to be an important factor and was estimated using Scanning Electron Microscope images. The influence of the finite element mesh size was higher for harsher cutting conditions, with effects felt on machining power only. In this case, finer mesh produced results that showed a higher agreement with experimental data, but at higher computational cost as shown by analysis of elapsed processing time. Although errors higher than 40% were observed, power and temperature trends from simulations were always in accordance with that found in experimental tests. Comparisons with experimental data from other studies showed the errors tend to grow for higher feed and cutting speed, which indicates the constitutive model of the material is more adequate for softer machining conditions. Simulation time seemed to be exponentially proportional to the inverse of minimum element size, and measured values might serve as a reference for other users.

Highlights

Milling is considered a complicated machining process, as the rotating tool and the intermittent cutting action produce varying chip loads and chip thicknesses (Man, Ren, Usui, Johnson, & Marusich, 2012)
From experimental tests, software given solutions only change if input variables are modified, and for this reason no standard deviation was incorporated in the analysis
An important consideration is that the experimental temperature was measured as the highest value captured by the thermographic camera, which was centered on an area of the tool that comprised of a few square millimeters and over 15 seconds of milling

Summary

Introduction

Milling is considered a complicated machining process, as the rotating tool and the intermittent cutting action produce varying chip loads and chip thicknesses (Man, Ren, Usui, Johnson, & Marusich, 2012). It enables the reduction in the number of experimental tests, saving time and reducing costs It allows for the optimization of process parameters such as cutting speed, feed and tool geometry, as a way of predicting forces and tool deflection, cutting edge temperature and wear, as well as other variables that are hard to obtain experimentally, as in residual stress, accumulated plastic strain and strain rate. This method becomes even more relevant for high cost or difficult to machine materials, among which nickel super alloys are important examples (Batista et al, 2015)

Objectives

Methods

Results

Conclusion