Analysis of the Disc Dressing Effects on Grinding Performance—part 1: Simulation of the Disc Dressed Wheel Surface

Baseri, Hamid; Rezaei, Seyed Mehdi; Rahimi, Abdolreza; Saadat, Mozafar

doi:10.1080/10910340802067395

Cited by 25 publications

(10 citation statements)

References 16 publications

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“…In the previous papers by the author et al [7,8], a new stochastic model of the prediction of wheel topography by diamond disc dresser was developed. Then the effects of a disc dressing parameter that affects the wheel topography, specific energy, and workpiece surface roughness were evaluated.…”

Section: Introductionmentioning

confidence: 99%

Design of Adaptive Neuro-Fuzzy Inference System for Estimation of Grinding Performance

Baseri

2011

Materials and Manufacturing Processes

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The grinding performance is influenced and determined by the disc dressing conditions due to effects of dressing process on the wheel surface topography. In this way, prediction of the grinding specific energy helps to optimize the disc dressing conditions to increase grinding performance. The objective of this study is the design of adaptive neuro-fuzzy inference system (ANFIS) for estimation of specific energy in grinding process using the data generated based on experimental observations when the wheel is dressed using a rotary diamond disc dresser. The input parameters of model are dressing speed ratio, dressing depth, and dresser cross-feed rate, and output parameter is grinding specific energy. In the experiment procedure, the grinding conditions are constant and only the dressing conditions are varied. The comparison of the predicted values and the experimental data indicates that the ANFIS model have an acceptable performance to estimation of grinding specific energy. IntroductionGrinding is a complex machining process with a large number of parameters that influence each other. The ground surface and grinding specific energy are affected by the wheel surface. The wheel should be dressed before the machined surface deteriorates beyond a quality limit of surface integrity.In order to achieve the best wheel surface, dressing parameters must be optimally set. Therefore, a systematic dressing method which can evaluate the grinding performance is necessary. There have been a number of attempts to develop and apply mathematical models of material removal in grinding. These models use a simple energy method [1,2] or slip-line field method [3,4] to predict cutting forces. Both can predict grinding forces approximately within 20% from wheel topography and workpiece properties. These models indicate two key parameters to be measured on the wheel: (i) the number of active grits per unit area and (ii) their attack angle as a function of grit depth of cut. Badger and Torrance [5] predicted the number of active grits per unit area and their statistical average slope. These are predicted from the dressing conditions and wheel properties and then compared with the measured topography. The predicted topography is then used to calculate forces and workpiece roughness.In more recent works, Teicher et al.[6] compared the grindability of Ti-6Al-4V regarding cubic boron nitride (CBN) and diamond brazed type monolayer grinding wheels under the influence of different environments. In grinding this alloy, cryogenic cooling did not help visibly for both

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Section: Introductionmentioning

confidence: 99%

Design of Adaptive Neuro-Fuzzy Inference System for Estimation of Grinding Performance

Baseri

2011

Materials and Manufacturing Processes

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“…In a study [19], Pamer studied also the influence of the dressing parameters on the topography of grinding wheel. Besides, many studies have been done to understand the influence of the dressing conditions on quality and accuracy of grinding process [20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Dressing Conditions for Minimum Flatness Tolerance when Grinding SKD11 Tool Steel

Tran

Giang

Ngo

et al. 2021

MSF

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This study is to determine effects of the dressing parameters to the flatness tolerance (Fl) when grinding SKD11 steel using HaiDuong grinding wheel and also propose the suitable dressing parameters to obtain the smallest flatness tolerance. In this paper, the effects of the six input parameters including feed rate (S), depth of rough dressing cut (ar), rough dressing times (nr), depth of finish dressing cut (af), finish dressing times (nf) and non-feeding dressing (nnon) to the flatness tolerance were investigated. To find out the influence of each input parameter on output results, the S/N ratio was analysized. Evaluated experimental results show that, the average flatness tolerance was 4.05μm and deviation of this value was 11.38% compared with the predicted value.

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“…Cebalq [5] found that the different combinations between the dressing mode, dressing conditions, and the grinding wheel specification lead to different inter-grit spacing between the abrasive grits of the grinding-wheel cutting surface and, as a result, different response variables of the grinding process (equivalent grinding thickness, specific metal removal rate, roughness of the grinding-wheel cutting surface, roughness of the ground surface, etc.). Baseri et al [6] and [7] and Baseri [8], Wegener et al [9] and Palmer et al [10] proved that the grindingwheel topography and the conditions under which it is prepared have a profound influence upon the grinding performance, defined by the grinding forces, the power consumption, the cutting zone temperature, the radial wear of the wheel and also the surface finish of the workpiece. Chen et al [11] found that a satisfied and stable grinding process can be controlled in real-time by means of utilizing the combination of optimal parameters, such as spindle speed, effective pack density, and the cutting space of abrasive grits.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of the Dressing Parameters in Fine Cylindrical Grinding

Aleksandrova¹

2019

SV-JME

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The optimum conditions of dressing grinding wheels determined and recommended in the literature are valid only for particular types and tools of dressing and grinding. In this paper an attempt has been made to optimize the dressing system parameters in fine cylindrical grinding. To define the optimum values of dressing process variables (radial in-feed of diamond roller dresser , dressing speed ratio , dress-out time , diamond roller dresser grit size/grinding wheel grit size ratio , type of synthetic diamonds and direction of dressing) a multi-objective optimization has been performed based on genetic algorithm. In the capacity of optimization parameter a generalized geometric-mean utility function has been chosen which appears to be a complex indicator characterizing the roughness and accuracy of the ground surface, grinding wheel lifetime and the manufacturing net costs of grinding operation. The optimization problem has been solved in the following sequence: 1) a model of the generalized utility function has been created reflecting the complex effect of dressing system parameters; 2) the optimum conditions of uni-directional and counter-directional dressing of aluminium oxide grinding wheels by experimental diamond roller dressers of synthetic diamonds of АС32 and АС80 types and different grit size have been determined at which the generalized utility function has a maximum; 3) Pareto optimum solution has been found (frd=0.2 mm/min; qd=0.8; td=4.65 s; qg=2.56), which guarantees the best combination between roughness and deviation from cylindricity of the ground surface, grinding wheel lifetime and the manufacturing net costs of grinding operation.

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Analysis of the Disc Dressing Effects on Grinding Performance—part 1: Simulation of the Disc Dressed Wheel Surface

Cited by 25 publications

References 16 publications

Design of Adaptive Neuro-Fuzzy Inference System for Estimation of Grinding Performance

Design of Adaptive Neuro-Fuzzy Inference System for Estimation of Grinding Performance

Optimizing Dressing Conditions for Minimum Flatness Tolerance when Grinding SKD11 Tool Steel

Multi-Objective Optimization of the Dressing Parameters in Fine Cylindrical Grinding

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