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Experimental investigation and parametric optimization of a milling process using multi-criteria decision making methods: a comparative analysis

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Abstract

This paper deals with exploring the influences of cutting speed, feed rate and depth of cut on material removal rate (MRR) and average surface roughness (Ra) during milling operation of aluminum 1100 alloy work material. The experiments are conducted based on Taguchi’s L8 design plan. It is noticed that MRR increases and Ra deteriorates with higher cutting speed and feed rate. Thus, it becomes imperative to deploy multi-criteria decision making (MCDM) tools to identify the most appropriate combination of the considered milling parameters leading to a compromise solution resulting in higher MRR and lower Ra. Six popular MCDM techniques in the form of weighted sum model, weighted product model, weighted aggregated sum product assessment, multi-objective optimization on the basis of ratio analysis, evaluation based on distance from average solution and technique for order preference by similarity to the ideal solution are employed and comprehensively assessed here to search out the optimal machining condition for the said process. It is revealed that most of the adopted MCDM techniques are successful in identifying the corresponding compromise solution. Excellent values of Spearman’s rank correlation (≥ 0.93) prove high similarities between the ranking patterns derived using the considered MCDM techniques, except weighted sum model. It can be revealed from the detailed analysis that higher MRR can be obtained at an optimal parametric combination of cutting speed = 210 rpm, feed rate = 40 mm/min and depth of cut = 0.4 mm. On the other hand, an optimal parametric intermix of cutting speed = 170 rpm, feed rate = 40 mm/min and depth of cut = 0.4 mm would lead to better surface quality of the machined components.

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Abbreviations

AHP:

Analytic Hierarchy Process

BBD:

Box–Behnken Design

CFRP:

Carbon Fiber Reinforced Polymer

COPRAS:

COmplex PRoportional ASsessment

DOC:

Depth of cut

GFRP:

Glass Fiber Reinforced Polymer

MABAC:

Multi-Attributive Border Approximation area Comparison

MARICA:

Multi-Attributive Real–Ideal Comparative Analysis

MOORA:

Multi-Objective Optimization on the basis of Ratio Analysis

MRR:

Material Removal Rate

OA:

Orthogonal Array

PIV:

Proximity Valued Index

SECA:

Simultaneous Evaluation of Criteria and Alternatives

TOPSIS:

Technique for Order Preference by Similarity to the Ideal Solution

WPM:

Weighted Product Model

ARAS:

Additive Ratio ASsessment

CCD:

Central Composite Design

CoCoSo:

Combined Compromise Solution

DFA:

Desirability Function Approach

EDAS:

Evaluation based on Distance from Average Solution

GRA:

Grey Relational Analysis

MARCOS:

Measurement Alternatives and Ranking according to COmpromise Solution

MCDM:

Multi-Criteria Decision Making

MMC:

Metal Matrix Composite

NSGA-II:

Non-dominated Sorting Genetic Algorithm-II

PCA:

Principal Component Analysis

Ra:

Average Surface Roughness

SWARA:

Step-wise Weight Assessment Ratio Analysis

WASPAS:

Weighted Aggregated Sum Product Assessment

WSM:

Weighted Sum Model

λ :

A constant (0 ≤ λ ≤ 1)

AV j :

Average solution for jth criterion

d j :

Degree of diversity for jth criterion

M :

Number of alternatives (experimental trials)

n ij :

Normalized value of xij

NDA ij :

Negative distance from average solution

NSP :

Normalized value of SP

r ij :

Element of weighted normalized decision matrix

S i − :

Distance of ith alternative from the ideal worst solution

S i WPM :

Overall score for WPM method

SN :

Weighted sum of NDA

w j :

Weight of jth criterion

y i :

Overall score for MOORA method

AS i :

Appraisal score for EDAS method

CC i :

Closeness coefficient of ith alternative

e j :

Entropy value for jth criterion

n :

Number of criteria (responses)

\(n^{\prime}_{ij}\) :

Vector normalized value of xij

NSN :

Normalized value of SN

PDA ij :

Positive distance from average solution

S i + :

Distance of ith alternative from the ideal best solution

S i WASPAS :

Overall score for WASPAS method

S i WSM :

Overall score for WSM method

SP :

Weighted sum of PDA

x ij :

Performance measure of ith alternative against jth criterion

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Authors and Affiliations

  1. Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, India

    Kanak Kalita

  2. Department of Automobile Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India

    S. Madhu

  3. Data Analytics Lab, REST Labs, Kaveripattinam, Krishnagiri, India

    M. Ramachandran

  4. Department of Production Engineering, Jadavpur University, Kolkata, India

    Shankar Chakraborty

  5. Department of Mechanical Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majhitar, India

    Ranjan Kumar Ghadai

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Kalita, K., Madhu, S., Ramachandran, M. et al. Experimental investigation and parametric optimization of a milling process using multi-criteria decision making methods: a comparative analysis. Int J Interact Des Manuf 17, 453–467 (2023). https://doi.org/10.1007/s12008-022-00973-3

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