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Optimization of surface roughness, tool wear and material removal rate in turning of Inconel 718 with ceramic composite tools using MCDM methods based on Taguchi methodology

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Abstract

One of the major challenges faced by industries nowadays is to achieve the most suitable response values while fulfilling the requirements of both the manufacturers and end users. In order to achieve this goal and meet these requirements, optimization techniques have to be introduced and applied. The present study investigates the application of three multi-objective optimization techniques (TOPSIS- Technique for Order Performance by Similarity to Ideal Solution, DEAR- Data Envelopment Analysis Ranking and GRA- Grey Relational Analysis) that are based on the signal to noise (S/N) ratio in order to achieve the best technological parameters represented by the arithmetic mean roughness (Ra), the wear of the inserts (Vb) and the chip removal rate (MRR) during the turning of a refractory alloy (Inconel 718) with a composite ceramic cutting tool (CC670) following a Taguchi plan (L18-21 × 33). The objective is to find out the best combination of the cutting parameters represented by the cutting speed (Vc), the feed rate (f), the depth of cut (ap) and the nose radius (r) that leads to the minimization of (Ra) and (Vb) along with the maximization of (MRR). The results achieved to demonstrate the effectiveness of the three methods that have led to similar results represented by the optimal parameters represented by Vc = 200 m/min, f = 0.16 mm/rev and ap = 0.25 mm. However, in terms of the tool nose radius, the DEAR approach gave a nose radius of r = 1.2 mm while both the TOPSIS and GRA methods led to r = 1.6 mm.

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Acknowledgements

The present research was undertaken by the “Metal Cutting Research Group” of the Structures and Mechanics Laboratory (LMS) of Université 8 Mai 1945, Guelma, Algeria, and received funding from the General Directorate of Scientific Research and Technological Development (DGRSDT) under the PRFU research project A11N01UN240120190001.

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Authors and Affiliations

  1. Applied Mechanics of New Materials Laboratory-LMANM, université 8 Mai 1945, P.O. Box 401, 24000, Guelma, Algeria

    H Boumaza & A Haddad

  2. Mechanics and Structures Research Laboratory (LMS), université 8 Mai 1945, P.O. Box 401, 24000, Guelma, Algeria

    S Belhadi, M A Yallese & K Safi

Authors
  1. H Boumaza
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  2. S Belhadi
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  3. M A Yallese
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  4. K Safi
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  5. A Haddad
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Corresponding author

Correspondence to A Haddad.

Nomenclature

Nomenclature


Acronyms

AHP:

Analytic Hierarchy Process

ANN:

Artificial Neural Network

ARAS:

Additive Ratio Assessment

ASCF:

Atomized Spray Cutting Fluid

CODAS:

COmbinative Distance-based ASsessment

DEAR:

Data Envelopment Analysis based Ranking methodology

DFA:

Desirability Function Approach

EDM:

Electrical Discharge Machining

FEM:

Finite Element Method

FFD:

Full Factorial Design

FIS:

Fuzzy Inference System

GRA:

Grey Relational Analysis

NSGA-II:

Non dominated Sorting Genetic Algorithm II

MRR:

Material Removal Rate

PSO:

Particle Swarm Optimization

S/N:

Signal to Noise ratio

TGRA:

Taguchi Grey Relational Analysis

TOPSIS:

Technique for Order Preference by Similarity to Ideal Solution

Roman letters

ap:

Depth of cut

f:

Feed rate

I:

Peak current

r:

Nose radius

Ra:

Arithmetic mean roughness

Toff:

Pulse-off time

Ton:

Pulse-on time

V:

Gap voltage

Vb:

Insert wear

Vc:

Cutting speed

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Boumaza, H., Belhadi, S., Yallese, M.A. et al. Optimization of surface roughness, tool wear and material removal rate in turning of Inconel 718 with ceramic composite tools using MCDM methods based on Taguchi methodology. Sādhanā 48, 1 (2023). https://doi.org/10.1007/s12046-022-02060-5

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