Selection of optimal conditions in the surface grinding process using a differential evolution approach

2007 ◽  
Vol 221 (7) ◽  
pp. 1185-1192 ◽  
Author(s):  
A Gopala Krishna
Keyword(s):  
Differential Evolution ◽  
Production Rate ◽  
Optimization Technique ◽  
Surface Grinding ◽  
Machining Process ◽  
Grinding Process ◽  
Machining Parameters ◽  
Wheel Wear ◽  
Grinding Conditions ◽  
Selection Of

The selection of machining parameters in any machining process significantly affects the production rate, quality, and cost of a component. The present work involves the application of a recently developed global optimization technique called differential evolution to optimize the machining parameters of a surface grinding process. The wheel speed, workpiece speed, depth of dressing, lead of dressing, cross-feed rate, wheel diameter, wheel width, grinding ratio, wheel bond percentage, and grain size are considered as the process variables. The production cost, production rate, and surface finish are evaluated for the optimal grinding conditions, subject to the constraints of thermal damage, wheel wear parameter, and machine tool stiffness. An example is taken from the literature to compare the results obtained by the proposed approach with other approaches.

scholarly journals Study of wire-electrical discharge machining parameters of titanium alloy by using taguchi method

2018 ◽  
Vol 7 (2.8) ◽  
pp. 10
Author(s):  
A VS Ram Prasad ◽  
Koona Ramji ◽  
B Raghu Kumar
Keyword(s):  
Surface Roughness ◽  
Taguchi Method ◽  
Process Parameters ◽  
Optimization Technique ◽  
Machining Process ◽  
Machining Parameters ◽  
Wire Edm ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Edm Process

Machining of Titanium alloys is difficult due to their chemical and physical properties namely excellent strength, chemical reactivity and low thermal conductivity. Traditional machining of such materials leads to formation of continuous chips and tool bits are subjected to chatter which leads to formation of poor surface on machined surface. In this study, Wire-EDM one of the most popular unconventional machining process which was used to machine such difficult-to-cut materials. Effect of Wire-EDM process parameters namely peak current, pulse-on- time, pulse-off-time, servo voltage on MRRand SR was investigated by Taguchi method. 0.25 mm brass wire was used in this process as electrode material. A surface roughness tester (Surftest 301) was used to measure surface roughness value of the machined work surface. A multi-response optimization technique was then utilized to optimize Wire-EDM process parameters for achieving maximum MRR and minimum SR simultaneously.

scholarly journals Effects of the Tribological Behaviour of h-BN MQL Nano Cutting Fluid (NCF-MQL) on Turning Inconel 625

2019 ◽  
Vol 11 (4) ◽  
pp. 107-121 ◽  
Author(s):  
Chinmaya PADHY ◽  
Pariniti SINGH
Keyword(s):  
Optimization Technique ◽  
Minimum Quantity Lubrication ◽  
Chip Morphology ◽  
Machining Process ◽  
Cutting Fluid ◽  
Inconel 625 ◽  
Machining Parameters ◽  
Machining Performance ◽  
Tribological Behaviour ◽  
Machined Surface

Minimum quantity lubrication (MQL) is currently a widely used lubricating technique during machining, in which minimum amount of lubricant in the form of mist is delivered to the machining interface, thus helps to reduce the negative effects caused to the environment and human health. Further, to enhance the productivity of machining process specifically for hard-to-cut materials, nano cutting fluid (suitably mixed nano materials with conventional cutting fluid) is used as an alternative method to conventional lubrication (wet) in MQL. In this study, h-BN nano cutting fluid was formulated with 0.1% vol. concentration of h-BN in conventional cutting fluid (Servo- ‘S’) for NCF-MQL technique and its tribological behaviors on machining(turning) performance of Inconel 625 were studied and compared with other lubricating conditions (dry, wet, MQL conventional). The tribological effects were analyzed in terms of tool wear analysis, chip morphology along with statistical analysis for machined surface and evolved cutting forces during machining. The optimal input machining parameters for experiments were defined by the use of Taguchi and Grey relational based multi response optimization technique. Finally, the tribological study shows that the use of h-BN NCF-MQL is a viable and sustainable option for improving machining performance of hard- to- cut material like Inconel 625.

Application of Taguchi Technique to Surface-Grinding of Mold Steel

2016 ◽  
Vol 689 ◽  
pp. 7-11 ◽  
Author(s):  
Y. Şahin ◽  
Senai Yalcinkaya
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Quality Characteristics ◽  
Cutting Fluid ◽  
Depth Of Cut ◽  
Grinding Process ◽  
Machining Parameters ◽  
Taguchi Technique ◽  
Fluid Environment ◽  
Selection Of

The selection of optimum machining parameters plays a significant role for the quality characteristics of products and its costs for grinding. This study describes the optimization of the grinding process for an optimal parametric combination to yield a surface roughness using the Taguchi method. An orthogonal array and analysis of variance are employed to investigate the effects of cutting environment (A), depth of cut (B) and feed rate (C) on the surface roughness characteristics of mold steels. Confirmation experiments were conducted to verify the optimal testing parameters. The experimental results indicated that the surface finish decreased with cutting-fluid and depth of cut, but decreased with increasing feed rate. It is revealed that the cutting fluid environment had highest physical as well as statistical influence on the surface roughness (71.38%), followed by depth of cut (25.54%), but the least effect was exhibited by feed rate (1.62%).

Designing and implementation of a novel online adaptive control with optimization technique in hard turning

2020 ◽  
pp. 095965182095219
Author(s):  
Vahid Pourmostaghimi ◽  
Mohammad Zadshakoyan
Keyword(s):  
Adaptive Control ◽  
Tool Wear ◽  
Hard Turning ◽  
Cutting Tool ◽  
Optimization Technique ◽  
Cutting Parameters ◽  
Numerical Control ◽  
Machining Process ◽  
Machining Parameters ◽  
Optimization Methodology

Determination of optimum cutting parameters is one of the most essential tasks in process planning of metal parts. However, to achieve the optimal machining performance, the cutting parameters have to be regulated in real time. Therefore, utilizing an intelligent-based control system, which can adjust the machining parameters in accordance with optimal criteria, is inevitable. This article presents an intelligent adaptive control with optimization methodology to optimize material removal rate and machining cost subjected to surface quality constraint in finish turning of hardened AISI D2 considering the real condition of the cutting tool. Wavelet packet transform of cutting tool vibration signals is applied to estimate tool wear. Artificial intelligence techniques (artificial neural networks, genetic programming and particle swarm optimization) are used for modeling of surface roughness and tool wear and optimization of machining process during hard turning. Confirmatory experiments indicated that the efficiency of the proposed adaptive control with optimization methodology is 25.6% higher compared to the traditional computer numerical control turning systems.

scholarly journals Intelligent Selection of Machining Parameters in Multipass Turnings Using Firefly Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Abderrahim Belloufi ◽  
Mekki Assas ◽  
Imane Rezgui
Keyword(s):  
Production Cost ◽  
Firefly Algorithm ◽  
Optimization Technique ◽  
Cutting Parameters ◽  
Machining Parameters ◽  
Metal Parts ◽  
Multipass Turning ◽  
Optimal Cutting Parameters ◽  
Selection Of

Determination of optimal cutting parameters is one of the most important elements in any process planning of metal parts. In this paper, a new optimization technique, firefly algorithm, is used for determining the machining parameters in a multipass turning operation model. The objective considered is minimization of production cost under a set of machining constraints. The optimization is carried out using firefly algorithm. An application example is presented and solved to illustrate the effectiveness of the presented algorithm.

Adaptive Grinding Strategy Development

2016 ◽  
Vol 874 ◽  
pp. 64-69
Author(s):  
Xun Chen
Keyword(s):  
Strategy Development ◽  
Grinding Wheel ◽  
Operational Control ◽  
Operational Parameters ◽  
Control Logic ◽  
Wheel Wear ◽  
Materials Properties ◽  
Grinding Performance ◽  
Grinding Conditions ◽  
Selection Of

Grinding performance can be influenced by various grinding conditions including workpiece materials properties, grinding wheel properties, grinding operational parameters and dressing operational parameters. In order to achieve stable optimal grinding performance, it is important to select the most suitable operational control parameters to match grinding requirement and to minimize the effects of grinding wheel wear and other changes in the process environment. The paper presents a simple adaptive control logic strategy for the selection of dressing and grinding conditions based on available sensing techniques. In this study, desirable grinding behaviour is discussed to demonstrate how to extract useful process information to guide process parameter adjustment for a stable satisfactory grinding performance.

Implementation of Multirate Estimation for the Cylindrical Grinding Process

2008 ◽  
Author(s):  
Cheol W. Lee
Keyword(s):  
Case Studies ◽  
Manufacturing Industry ◽  
State Parameter ◽  
Process Models ◽  
Machining Process ◽  
Grinding Process ◽  
Wheel Wear ◽  
Estimation Scheme ◽  
Inspection Data ◽  
Grinding Power

This paper presents implementation results of the multirate estimation scheme, proposed by Lee (Lee, C.W., 2007, “Multirate Estimation for the Machining Process under Multirate Noise,” Proceedings of the 2007 ASME IMECE, November 11–15, 2007, Seattle, WA), on the cylindrical plunge grinding process. The multirate scheme is an efficient tool for integrating real-time sensor signals with postprocess inspection data for estimating the immeasurable variables. In order to accomplish this goal, process models for grinding power, surface roughness and wheel wear are developed using experimental data. Case studies are performed on simultaneous state-parameter estimation for actual grinding batches after the multirate observers are built based on the process models. Results from case studies validate the applicability of the proposed scheme to challenging estimation tasks in the manufacturing industry that cannot be undertaken by traditional approaches.

ANALYSIS OF WHEEL WEAR USING FORCE DATA IN SURFACE GRINDING

2003 ◽  
Vol 27 (3) ◽  
pp. 193-204 ◽  
Author(s):  
Andrew Warkentin ◽  
Robert Bauer
Keyword(s):  
Mild Steel ◽  
Surface Grinding ◽  
Grinding Process ◽  
Time Varying ◽  
Grinding Forces ◽  
Wheel Wear ◽  
Excessive Heat ◽  
Abrasive Grains ◽  
Different Depths ◽  
Force Data

Grinding involves many randomly shaped and distributed abrasive grains removing material from a workpiece. Wheel wear results when these grains dull, fracture or break away. As a result, grinding forces are time-varying. In order to automate and optimize the grinding process an understanding of how forces are generated and change during grinding is critical to avoid workpiece damage, surface finish deterioration, cracking, excessive heat generation, and excessive residue stresses. This paper builds upon the existing grinding literature by studying the relationships between wheel wear and grinding forces for different depths of cut when surface grinding mild steel with an aluminum oxide wheel.

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