superficial hardening of aisi1045 steel in high efficiency deep grinding through optimization approaches, taguchi method and sa algorithm

Authors

حمیدرضا فضلی شهری

علی اکبر اکبری

abstract

the major problem in material removal process specially grinding is heat generation during the process and thus residual stress on the surface of product. therefore, optimization of high efficiency deep grinding (hedg) process is the main goal of this study in order to reduce heat and residual stress and also increase strength and surface hardness of aisi1045 steel by optimization of the process. in other words, the effects of main parameters e.g. depth of cut, wheel speed, workpiece speed and cross feed on surface hardness have been investigated in this study. operating parameter optimizing through sa method in matlab's toolbox is so that the produced tensile residual stress and temperature decrease and meanwhile surface microhardness improves. beside this, the results are validated by measuring and analyzing surface microhardness, surface temperature and forces. the obtained results reveal a good agreement between the optimization results and experimental observations.

Upgrade to premium to download articles

Sign up to access the full text

Already have an account?login

similar resources

Parametric optimization of cylindrical grinding process through hybrid Taguchi method and RSM approach using genetic algorithm

The present investigation proposes a hybrid technique: Taguchi method, response surface methodology (RSM) and genetic algorithm (GA), to analyze, model and predict vibration and surface roughness in traverse cut cylindrical grinding of aluminum alloy. Experiments have been conducted as per L9 orthogonal array of Taguchi methodology using several levels of the grinding parameters. Analysis of va...

full text

Surface hardness improvement in high efficiency deep grinding process by optimization of operating parameters

The grinding is one of the most important methods that directly affects tolerances in dimensions, quality and finished surface of products. One of the major problems in the material removal processes specially grinding is the heat generation during the process and the residual tensile stress in the surfaces of product. Therefore, optimization of High Efficiency Deep Grinding (HEDG) process is t...

full text

The Study of Deep Drawing of Brass-steel Laminated Sheet Composite Using Taguchi Method

Deep drawing process is one of the most applicable methods in producing industrial parts. In this process, the initial blank deforms to final product using a rigid punch and die. In this investigation, the effect of deep drawing process parameters of brass/steel laminated sheet composites on required forming force has been investigated. The process simulated using finite element method (FEM) an...

full text

Parametric Optimization of TIG Welding on Stainless Steel (202) & Mild Steel by using Taguchi Method

The main objective of industries reveal with manufacturing better quality product at low cost and increase productivity. TIG welding is most vital and common operation use for joining of two similar or dissimilar parts with heating the material or applying the pressure by using the filler material for increasing productivity with less time and cost constrain. The TIG welding parameters are the ...

full text

Prediction and Optimization of Cylindrical Grinding Parameters for Surface Roughness Using Taguchi Method

Recently 304 stainless steel finds many applications like Automotive, Aerospace, Nuclear, Chemical and Cryogenics. The cylindrical grinding parameters on 304 stainless steel are conducted using Taguchi design of experiments of L9 orthogonal array was selected with 3 levels with 3 factors and output parameter of Surface Roughness is measured. The quality of the surface describes the relationship...

full text

My Resources

Save resource for easier access later


Journal title:
علوم کاربردی و محاسباتی در مکانیک

جلد ۲۴، شماره ۱، صفحات ۱۶-۰

Keywords

Hosted on Doprax cloud platform doprax.com

copyright © 2015-2023