GRA Assisted Multi-Response Optimization of Process Parameters in Sustainable Machining AISI D3 with Cutting Oil Developed from Waste Lubricating Oil

Abstract

The global need for sustainable manufacturing processes is increasing and the need for reducing the environmental footprint of traditional petrol/chemical based cutting fluids has come into play, which makes sustainable machining an important field of research. This study proposes to investigate the multi-response optimization of the machining parameters of turning AISI D3 tool steel with a cutting oil obtained from recycled waste lubricating oil under Minimum Quantity Lubrication (MQL) conditions. The cutting oil was developed by refining used cutting oil and adding the appropriate additives which improve the properties of the oil, such as lubricating properties, anti-wear properties, corrosion resistance and resistance to oxidation. The Taguchi L18 orthogonal array was used to design turning experiments with input parameters of MQL air pressure, cutting speed, feed rate and depth of cut. The machining performance was investigated by cutting forces (Fx, Fy and Fz), surface roughness (Ra), and flank wear of the tools (Vb). Grey Relational Analysis (GRA) was applied to optimize multiple performance characteristics by converting them to a single Grey Relational Grade (GRG) and Analysis of Variance (ANOVA) was used to identify the statistical significance of the machining parameters. From the experimental results, it was observed that the best machining condition was obtained at air pressure of MQL=1 bar, cutting speed=150 m/min, feed rate=0.04 mm/rev and depth of cut=0.4 mm. The results of ANOVA showed that within the investigated range, the depth of cut has the greatest influence on the overall machining performance of the process, it has a statistically significant effect at the probability level of 99.7 percent (p = 0.003), while the effects of other factors (MQL air pressure, cutting speed and feed rate) were comparatively small. Developed waste lubricating oil-based cutting fluid has exhibited good cutting performance and the valorisation of waste with reduction of reliance on virgin petroleum-based cutting fluids. The use of recycled cutting oil with the Taguchi–Grey Relational Analysis approach represents an efficient method for multi-response optimization, and is a step towards environmentally friendly cutting oil use in machining.

Introduction

Sustainable manufacturing aims to reduce environmental impacts while maintaining machining efficiency and product quality. Conventional petroleum-based cutting fluids used in machining operations create environmental, health, and disposal concerns due to their non-renewable origin and harmful chemical composition. Waste lubricating oil (WLO), generated from automotive and industrial applications, is a major environmental pollutant containing degraded hydrocarbons, heavy metals, and oxidation products. However, after proper purification and treatment, WLO can be converted into effective cutting fluids, promoting waste recycling, resource conservation, and circular economy practices.

Previous research has explored sustainable cutting fluids such as vegetable oils, biodegradable lubricants, nano-enhanced fluids, and recycled oils. Although vegetable oils provide good lubrication and biodegradability, their thermal and oxidative stability under severe machining conditions is limited. Recycled WLO-based cutting oils can provide comparable lubrication performance, improved load-carrying capacity, reduced production costs, and lower environmental impact. However, limited research has investigated their application in machining difficult-to-cut materials such as hardened tool steels.

AISI D3 cold work tool steel is widely used for dies, punches, cutting tools, and wear-resistant components due to its high hardness, chromium content, and wear resistance. However, its machining is challenging because of high cutting forces, elevated temperatures, rapid tool wear, and poor surface finish. Therefore, selecting suitable machining parameters and cutting fluids is essential to improve tool life, surface quality, and machining performance.

Since machining performance involves multiple conflicting responses, including cutting force, surface roughness, material removal rate, and tool wear, single-response optimization is insufficient for practical applications. Grey Relational Analysis (GRA), combined with Taguchi Design of Experiments (DOE), provides an effective multi-response optimization technique by converting multiple quality characteristics into a single Grey Relational Grade (GRG). This approach enables identification of optimum machining conditions with fewer experiments.

The study focuses on developing a sustainable cutting oil from purified waste lubricating oil and evaluating its performance during turning of AISI D3 steel. Taguchi’s L18 orthogonal array was used to conduct experiments by varying Minimum Quantity Lubrication (MQL) pressure, cutting speed, feed rate, and depth of cut. Machining responses such as cutting forces, surface roughness, and tool flank wear were measured using advanced instruments including a lathe dynamometer, surface roughness tester, and digital microscope.

The developed cutting fluid was prepared using waste lubricating oil mixed with water and enhanced with additives such as extreme pressure agents, antioxidants, corrosion inhibitors, emulsifiers, polyglycols, and esters. The machining experiments evaluated the influence of process parameters on performance characteristics. Grey Relational Analysis was applied to optimize multiple responses, and Analysis of Variance (ANOVA) was used to determine the significance of machining parameters.

The research highlights the potential of recycled waste lubricating oil as an environmentally friendly alternative to conventional cutting fluids. The integration of WLO-based cutting oil with Taguchi-GRA optimization provides a sustainable approach that reduces petroleum dependency, improves machining performance, supports waste valorization, and promotes circular economy principles in manufacturing.

Conclusion

In this study, the multi-response optimization of machining parameters during turning of AISI D3 tool steel under Minimum Quantity Lubrication (MQL) conditions has been carried out using cutting oil that was synthesized using recycled waste lubricating oil. The use of Grey Relational Analysis (GRA) method coupled with Taguchi method proved to be successful for optimizing multiple machining responses such as cutting forces, surface roughness and tool flank wear. This study\'s key findings include: A treated waste lubricating oil has been treated to successfully develop a sustainable cutting oil by the incorporation of suitable additives following IS 1115 specifications. The developed cutting fluid was suitable for the machining AISI D3 steel under MQL condition, which can lead to resource recovery and sustainable manufacturing. The Taguchi–Grey Rel relational Analysis approach was used to convert several machining responses to a single Grey Rel relational Grade (GRG) in order to optimize the cutting force, surface roughness and tool flank wear by conducting a few number of experiments. The optimum machining parameter combination which was obtained from Grey Relational Grade analysis was 1 bar MQL air pressure, 150 m/min cutting speed, 0.04 mm/rev feed rate and 0.4 mm depth of cut (A?–B?–C?–D?), which gave the optimum overall machining performance. Analysis of Variance (ANOVA) showed that the machining parameter Depth of Cut has the most significant influence on the Grey Relational Grade and is statistically significant for the combined optimization of the machining responses (F = 11.36, p = 0.003). On the other hand, the air pressure, cutting speed and feed rate of the MQL showed no statistically significant effects in the range of the investigated parameters. Based on the results of the main effects analysis and contour plot analysis, it is concluded that the greater the depth of cut, the greater were the cutting forces, cutting temperature, tool loading and tool wear, which led to the reduction of the Grey Relational Grade of the machining performance. Lower feed rate and higher cutting speed resulted in better surface finish and reduced surface roughness. The Taguchi response analysis results are shown to be in close agreement with the ANOVA results, which further proves the reliability of the Grey Relational Analysis technique for multi-response optimization in sustainable machining applications. The results show that oil formulation using recycled waste lubricating oil can be used as an alternative to petroleum based cutting fluids to achieve the same machining performance while being environmentally friendly. The suggested method offers waste valorization, decreases environmental pollution, follows the principles of the circular economy and offers a practical model for the sustainable optimization of the machining process of difficult-to-machine materials.

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Copyright

Copyright © 2026 Sonali More, Prof. Dayanand Ghatge. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.