https://orcid.org/0000-0002-3026-0423
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ABSTRACT
The present work aims to evaluate the effect of cutting factors, namely cutting speed (Vc), feed per tooth (fz), and depth of cut (ap) on surface quality (average arithmetic roughness Ra and total roughness Rt) and productivity (material removal rate MRR) when face milling polyoxymethylene (POM C). In this context, the experiments were planned according to the standard orthogonal network of Taguchi L16 (4^3). An analysis of variance (ANOVA) was performed to study the influence of each input factor on the output parameters. The processing of the results made it possible to propose prediction models for the surface. Finally, a mono and multi-objective optimisation was performed using the grey relational analysis (GRA) approach and ranking based on data envelopment analysis (DEAR) coupled with the Taguchi method based on the signal-to-noise ratio (S/N). ANOVA results revealed that feed per tooth (fz) has the greatest influence on (Ra, Rt, and MRR), with successive contributions of (97.69, 97.05, and 66.26)%. On the other hand, GRA and DEAR resulted in the same cutting regime (Vc = 251.2 m/min, fz = 0.004 mm/tooth, and ap = 6 mm), leading to optimised responses (Ra = 1.67 µm and MRR = 11.51 cm3/min).
KEYWORDS:
Abbreviations
| POM C | = | Polyoxymethylene copolymer |
| PTFE | = | Polytetrafluoroethylene |
| UHMWPE | = | Ultra-high-molecular-weight polyethylene |
| GFRP | = | Glass fiber reinforced polymer |
| GRA | = | Grey relational analysis |
| DEAR | = | Data envelopment analysis based ranking |
| RSM | = | Response Surface Methodology |
| ANOVA | = | Analysis of variance |
| DoF | = | Degree of Freedom |
| Seq-SS | = | Sequential Sum of Squares |
| Adj-SS | = | Adjusted Sum of Squares |
| Adj-MS | = | Adjusted Main of Squares |
| S/N | = | Signal-to-noise ratio |
| ANN | = | Artificial neural network |
| WEDM | = | Wire electrical discharge machining |
| MQL | = | Minimum quantity lubrication |
| Vc | = | Cutting speed |
| fz | = | Feed per tooth |
| ap | = | Axial depth of cut |
| Ra | = | Average arithmetic roughness |
| Rt | = | Total roughness |
| MRR | = | Material removal rate |
| MCDM | = | Multi-criteria decision making |
| GRG | = | Grey relational grade |
| MRPI | = | Multi-performance ranking index |
Acknowledgements
The authors express their gratitude to LRTAPM of Badji Mokhtar University - Annaba, Algeria and LMS of 8 May 1945 University - Guelma, Algeria, for providing their equipment and facilities to carry out this research work. The authors would like to acknowledge DGRSDT, Algeria, for their support and help.
Disclosure statement
The authors declare that they have no known competing personal or financial interests that could have appeared to influence the work reported in this paper.
Availability of data and material
The authors affirm that the data supporting the results of this study and the used equipment are available within the article.
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