Surface Roughness In Machining Process Research Articles

A hybrid approach of NSGA-II and TOPSIS for minimising vibration and surface roughness in machining process

Increasing vibration amplitude during end milling process can seriously affect the life of end mills and reduces surface finish. Spindle and worktable vibration has a significant influence on surface quality of machined components. This paper confronts and investigates the effect of machining and geometrical parameters (spindle speed, feed rate, axial depth of cut, radial depth of cut and radial rake angle) on spindle and worktable vibration in terms of acceleration amplitude and surface roughness. Experiments were conducted on aluminium alloy 6061-T6 with high-speed steel (HSS) end mill cutter based on the central composite design (CCD). Response surface methodology (RSM) was used to develop the predictive models and the adequacy of the models were verified using analysis of variance (ANOVA). Non-dominated sorting of genetic algorithm (NSGA-II) was adopted to solve the multi objective optimisation problem and the optimised results were resulted with a set of Pareto-optimal solutions. The multi criteria decision making method (MCDM) such as technique for order preference by similarity to ideal solution (TOPSIS) and analytical hierarchy process (AHP) were designed to rank the Pareto optimal solutions based on response of closeness coefficient values.

Surface roughness prediction of particulate composites using artificial neural networks in turning operation

Article history: Received December 10, 2014 Received in revised format: March 2, 2015 Accepted March 2, 2015 Available online March 2 2015 A number of factors, e.g. cutting speed and feed rate, affect the surface roughness in machining process. In this paper, an Artificial Neural Network model was used to forecast surface roughness with related inputs, including cutting speed and feed rate. The output of the ANN model input parameters related to the machined surface roughness parameters. In this research, twelve samples of experimental data were used to train the network. Moreover, four other experimental tests were implemented to test the network. The study concludes that ANN was a reliable and accurate method for predicting machining parameters in CNC turning operation of Particulate Reinforced Aluminum Matrix Composites (PAMCs) specimens with 0%, 5%, 10% and 15% filler. The aim of this work is to decrease the production cost and consequently increase the production rate of these materials for industry without any trial and error method procedure. Growing Science Ltd. All rights reserved. 5 © 201

Analysis of machining parameters effects on surface roughness: a review

This paper aims at presenting the effective parameters on surface roughness in machining process. Many investigations have been carried out on the offline or online machining parameters estimation. Both the optical and artificial intelligence (AI) techniques have been investigated to achieve the objective. AI-based methods which are divided into three branches, including the genetic algorithms (GA), knowledge-based expert systems, and artificial neural networks (ANN), have been discussed. Online or real time AI based systems for monitoring the machine surface roughness have also been described. The effective machining parameters have been categorised into six main branches: such as machine tool, workpiece, tool properties, cutting, thermal and dynamic parameters.