Ultrasonic machining of WC–Co composite material: Experimental investigation and optimization using statistical techniques

Kataria, Ravinder; Kumar, Jatinder; Pabla, B. S.

doi:10.1177/0954405416629586

Cited by 12 publications

(6 citation statements)

References 36 publications

(35 reference statements)

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“…Depending on the requirements, the entire lists for the available USM process parameters and responses can also be selected. The entire information related to these USM process parameters and responses are accumulated from (Kumar & Khamba, 2010;Kataria et al, 2017). Based on these requirements, the developed system predicts the responses as conicity = 0.023-0.038º, SR = 0.78-0.85 µm, MRR = 0.025-0.035 mm 3 /min, TW = 0.98-1.05 mm 3 /min and micro-hardness = 155-160 HV.…”

Section: Example 2: Ultrasonic Machiningmentioning

confidence: 99%

“…However, the material removal tendency decreases because of the loss of energy possessed by the abrasives in the slurry. As the number of particles between the tool and the work surface increases due to higher slurry concentration, loss of energy due to interparticle collision may prevail during this phenomenon (Kataria et al, 2017;Chakraborty et al, 2020).…”

Section: Figure 11 a Typical Error Message For Usm Processmentioning

confidence: 99%

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Development of an intelligent decision model for non-traditional machining processes

Chakraborty

Kumar

2021

Decis. Mak. Appl. Manag. Eng.

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In order to fulfil the ever increasing requirements of various hard and difficult-to-machine materials in automobile, turbine, nuclear, aviation, tool and die making industries, the conventional material removal processes are now being continuously substituted by an array of non-traditional machining (NTM) processes. The efficient and improved capabilities of these NTM processes have made them indispensible for the present day manufacturing industries. While deploying a particular NTM process for a specific machining application, the concerned process engineer must be aware of its capability which is influenced by a large number of controllable parameters. In this paper, an intelligent decision model is designed and developed in VBASIC to guide the concerned process engineer to have an idea about the values of various NTM process responses for a given parametric combination. It would also advise about the tentative settings of different NTM process parameters for achieving a set of target response values. The operational procedure of this developed system is demonstrated with the help of three real time examples.

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Section: Example 2: Ultrasonic Machiningmentioning

confidence: 99%

Section: Figure 11 a Typical Error Message For Usm Processmentioning

confidence: 99%

Development of an intelligent decision model for non-traditional machining processes

Chakraborty

Kumar

2021

Decis. Mak. Appl. Manag. Eng.

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“…Different researchers used the statistical techniques for the experimental design to investigate the predictive model of response characteristics (cutting cost, cutting forces, material removal rate, SR, tool wear, tool life) (Kataria and Kumar, 2014; Sharma et al , 2015; Gupta and Kumar, 2015; Jangra et al , 2016; Kataria et al , 2017; Jha et al , 2018; Singh et al , 2020; Gupta et al , 2020).…”

Section: Introductionmentioning

confidence: 99%

Multi-response optimization while machining of stainless steel 316L using intelligent approach of grey theory and grey-TLBO

Sharma

Дабра²,

Singh

et al. 2021

WJE

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Purpose Stainless steel is widely used in different manufacturing sectors. The purpose of this study is to optimize the process parameters of machining while processing SS316L alloy. The optimization of machining characteristics in the case of SS316L alloy greatly improves the quality and productivity economically. Design/methodology/approach The machining variables in current research are depth of cut, spindle speed and feed rate. The optimization of response characteristics was carried out using the intelligent approach of grey, regression and teaching learning-based optimization (TLBO) and Taguchi-Grey approach. Planning of experiments was made using Taguchi’s based L27 orthogonal array. With the implementation of grey, the response characteristics were normalized and converted into a single response. The regression analysis was used for empirical modeling of the single response induced from the grey application. TLBO is further used to investigate the combinations of machining variables and compared with grey theory. Findings The grey-TLBO based multi-criteria decision-making approach suggests that the optimized setting for material removal rate, mean roughness depth (Rz) and cutting force (Fz) is spindle speed (N): 720 rpm; feed rate (F): 0.3 mm/rev; depth of cut (DoC): 1.7 mm. The grey theory suggests an optimized setting as N: 720 rpm; F: 0.2 mm/rev and DoC: 1.7 mm. Originality/value The parametric optimization during the turning of SS316L using grey-TLBO based intelligent approach is not performed till now. Thus, this intelligent approach will give a path to the researchers working in this direction. However, the grey theory performs better as compared to the grey-TLBO approach.

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“…A large number of process parameters exist in USM which would influence the machining performance, 8 and this enhances the difficulty in studying the material removal mechanism. Generally, slurry characteristics play a particular important role in USM.…”

Section: Introductionmentioning

confidence: 99%

Smoothed particle hydrodynamics simulation and experimental study of ultrasonic machining

Wang

Shimada

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part B:

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Hard and brittle materials like glass and ceramics are highly demanded in modern manufacturing industries. However, their superior physical and mechanical properties lead to high cost of machining. Ultrasonic machining has been regarded as one of the most suitable fabrication techniques for these kinds of materials. A smoothed particle hydrodynamics model was proposed to study the material removal mechanism of the ultrasonic machining in this study. Influences of abrasive materials and the particle shapes on the crack formation of work substrates were investigated using this smoothed particle hydrodynamics model. Experiments were also conducted to verify the simulation model. Both of the simulation and experimental results show that using hard and spherical abrasive particles is helpful to improve the material removal efficiency. This work was the first to demonstrate the crack formation mechanisms during ultrasonic machining with different abrasive particles using smoothed particle hydrodynamics, which is significant for improving the machining performance of the ultrasonic machining process.

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Ultrasonic machining of WC–Co composite material: Experimental investigation and optimization using statistical techniques

Cited by 12 publications

References 36 publications

Development of an intelligent decision model for non-traditional machining processes

Development of an intelligent decision model for non-traditional machining processes

Multi-response optimization while machining of stainless steel 316L using intelligent approach of grey theory and grey-TLBO

Smoothed particle hydrodynamics simulation and experimental study of ultrasonic machining

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