Optimization of Process Parameters of Abrasive Water Jet Machining Using Variations of Cohort Intelligence (CI)

Gulia, Vikas; Nargundkar, Aniket

doi:10.1007/978-981-13-1822-1_43

Cited by 16 publications

(4 citation statements)

References 8 publications

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“…For exploiting capabilities and potentials of AWJM process to the maximum extent, optimization is unavoidable. Gulia and Nargundkar (2019) have applied cohort intelligence (CI, a socio-inspired algorithm adopting the concepts of artificial intelligence) to identify the optimal AWJM process parameters for the output responses (namely, surface roughness (Ra) and kerf ). Their studies indicate better performance of CI when compared to that of firefly algorithm (FA).…”

Section: Algorithms Adopted For Optimization Of Awjm Processmentioning

confidence: 99%

Multi-objective optimization for optimum abrasive water jet machining process parameters of Inconel718 adopting the Taguchi approach

Dharmendra

Kodali

Boggarapu

2019

MMMS

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Purpose The purpose of this paper is to adopt the multi-objective optimization technique for identifying a set of optimum abrasive water jet machining (AWJM) parameters to achieve maximum material removal rate (MRR) and minimum surface roughness. Design/methodology/approach Data of a few experiments as per the Taguchi’s orthogonal array are considered for achieving maximum MRR and minimum surface roughness (Ra) of the Inconel718. Analysis of variance is performed to understand the statistical significance of AWJM input process parameters. Findings Empirical relations are developed for MRR and Ra in terms of the AWJM process parameters and demonstrated their adequacy through comparison of test results. Research limitations/implications The signal-to-noise ratio transformation should be applied to take in to account the scatter in the repetition of tests in each test run. But, many researchers have adopted this transformation on a single output response of each test run, which has no added advantage other than additional computational task. This paper explains the impact of insignificant process parameter in selection of optimal process parameters. This paper demands drawbacks and complexity in existing theories prior to use new algorithms. Practical implications Taguchi approach is quite simple and easy to handle optimization problems, which has no practical implications (if it handles properly). There is no necessity to hunt for new algorithms for obtaining solution for multi-objective optimization AWJM process. Originality/value This paper deals with a case study, which demonstrates the simplicity of the Taguchi approach in solving multi-objective optimization problems with a few number of experiments.

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Section: Algorithms Adopted For Optimization Of Awjm Processmentioning

confidence: 99%

Multi-objective optimization for optimum abrasive water jet machining process parameters of Inconel718 adopting the Taguchi approach

Dharmendra

Kodali

Boggarapu

2019

MMMS

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“…(2018) employed the Taguchi-DEAR (data envelopment analysis-based ranking) methodology to study the impact of process parameters on the machining of Al7075 composites reinforced with TiB2 particles during abrasive water jet machining (AWJM). Various other AWJM processes have identified optimal parameters using algorithms such as cohort intelligence (CI) (Gulia and Nargundkar, 2019), multi-objective cuckoo search (MOCS) (Qiang et al ., 2018), artificial bee colony (ABC) (Pawar et al ., 2018), Jay algorithm (Venkata Rao, 2019), multi-objective optimization by ratio analysis (MOORA) (Kalirasu et al ., 2017), Gray wolf optimizer (GWO) (Chakraborty and Mitra, 2018), gravitational search algorithm (GSA) (Mokkandi et al ., 2017), response surface methodology (RSM) (Ma et al ., 2020), artificial neural network (ANN) (Gong et al ., 2022; Sing et al ., 2021), as well as Taguchi method and evolutionary optimization (Shukla and Singh, 2017).…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization to specify optimal selective laser melting process parameters for SS316 L powder

Prasanth Kumar,

Boggarapu,

Narayana Murty

2023

MMMS

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PurposeThis paper adopts a modified Taguchi approach to develop empirical relationships to the performance characteristics (output responses) in terms of process variables and demonstrated their validity through comparison of test data. The method suggests a few tests as per the orthogonal array and provides complete information for all combinations of levels and process variables. This method also provides the estimated range of output responses so that the scatter in the repeated tests can be assessed prior to the tests.Design/methodology/approachIn order to obtain defect-free products meeting the required specifications, researchers have conducted extensive experiments using powder bed fusion (PBF) process measuring the performance indicators (namely, relative density, surface roughness and hardness) to specify a set of printing parameters (namely, laser power, scanning speed and hatch spacing). A simple and reliable multi-objective optimization method is considered in this paper for specifying a set of optimal process parameters with SS316 L powder. It was reported that test samples printed even with optimal set of input variables revealed irregular shaped, microscopic porosities and improper melt pool formation.FindingsFinally, based on detailed analysis, it is concluded that it is impossible to express the performance indicators, explicitly in terms of equivalent energy density (E_0ˆ*), which is a combination of multiple sets of selective laser melting (SLM) process parameters, with different performance indicators. Empirical relations for the performance indicators are developed in terms of SLM process parameters. Test data are within/close to the expected range.Practical implicationsBased on extensive analysis of the SS316 L data using modified Taguchi approach, the optimized process parameters are laser power = 298 W, scanning speed = 900 mm/s and hatch distance = 0.075 mm, for which the results of surface roughness = 2.77 Ra, relative density = 99.24%, hardness = 334 Hv and equivalent energy density is 4.062. The estimated data for the same are surface roughness is 3.733 Ra, relative density is 99.926%, hardness is 213.64 Hv and equivalent energy density is 3.677.Originality/valueEven though equivalent energy density represents the energy input to the process, the findings of this paper conclude that energy density should no longer be considered as a dependent process parameter, as it provides multiple results for the specified energy density. This aspect has been successfully demonstrated in this paper using test data.

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“…The static and dynamic penalty function approach is incorporated in CI (CI-SPF and CI-DPF) for solving several test problems and manufacturing engineering problems [23]. The multi-CI [24] and variations of CI [25] were used to solve the AWJM problem for minimization of surface roughness. The CI-SPF is adopted for solving complex problems from truss structure and mechanical engineering domain [26,27].…”

Section: Introductionmentioning

confidence: 99%

Optimization of advanced manufacturing processes using socio inspired cohort intelligence algorithm

Kale

Pachpande

Naikwadi

et al. 2022

Int. J. Simul. Multidisci. Des. Optim.

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The demand of Advanced Machining Processes (AMP) is continuously increasing owing to the technological advancement. The problems based on AMP are complex in nature as it consisted of parameters which are interdependent. These problems also consisted of linear and nonlinear constraints. This makes the problem complex which may not be solved using traditional optimization techniques. The optimization of process parameters is indispensable to use AMP's at its aptness and to make it economical to use. This paper states the optimization of process parameters of Ultrasonic machining (USM) and Abrasive water jet machining (AWJM) processes to maximize the Material Removal Rate (MRR) using a socio inspired Cohort Intelligent (CI) algorithm. The constraints involved with these problems are handled using static penalty function approach. The solutions are compared with other contemporary techniques such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Modified Harmony Search (HS_M) and Genetic Algorithm (GA).

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Optimization of Process Parameters of Abrasive Water Jet Machining Using Variations of Cohort Intelligence (CI)

Cited by 16 publications

References 8 publications

Multi-objective optimization for optimum abrasive water jet machining process parameters of Inconel718 adopting the Taguchi approach

Multi-objective optimization for optimum abrasive water jet machining process parameters of Inconel718 adopting the Taguchi approach

Multi-objective optimization to specify optimal selective laser melting process parameters for SS316 L powder

Optimization of advanced manufacturing processes using socio inspired cohort intelligence algorithm

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