Green Machining Characteristics Study of Al-6063 in CNC Milling Using Taguchi Method and Grey Relational Analysis

Green machining strategies in the manufacturing sector help to maintain the product value by considering the environmental impacts. Also, improvisation in the quality contribution of the parts can minimize the environmental consequences by improving resource efficiency, specifically in terms of coolants used in machining. Certain hazardous impacts have been witnessed because of longer exposure to such a machining environment. To address it, many researchers have concentrated on providing a healthy machining environment either by introducing dry machining or by minimum quantity lubrication (MQL). The proposed study addresses this context. The influence of these tactics on the attained surface quality of Al-6063 is quantified in this paper in terms of surface integrity (Ra) and removal rate of material (MRR). The study involves single-response optimization using the Taguchi design and multiresponse optimization using grey relational analysis (GRA). The results reveal that the depth of cut (Dc) and spindle speed (Ss) have the greatest impact on Ra and MRR. The machinability of Al-6063 is examined by considering the key machinability parameters, such as the spindle speed (Ss), feed rate (Fr), and the depth of cut (Dc), to arrive at the best possible surface roughness and removal rate of the material. As a typical Taguchi approach cannot perform multiresponse optimization, grey relational analysis is used. The grey relational analysis combined with Taguchi gives a novel methodology for multioptimization. The entire study is performed in dry condition and under minimum quantity lubrication. The results suggest that the responses are highly influenced by the depth of cut and spindle speed.

RSM based expert system development for cutting force prediction during machining of Ti–6Al–4V under minimum quantity lubrication

In recent days the manufacturing process have become more precise and cost efficient due to advancement in the field of computer technology. Information technology has been integrated with manufacturing practice and has resulted in time reduction from concept of a product to marketing of the product. Cutting force generated is the main manufacturing issue raised among industries as it clearly affects quality and cost of the final product. Hence using extensive literature and data base knowledge optimum cutting parameters are selected. Therefore, this paper focuses on a response surface methodology (RSM) based expert system that has been developed using JAVA programming with the help of response surface second order model to automatically generate values of cutting force during machining of Ti–6Al–4V alloy under minimum quantity lubrication (MQL) for different process input parameters. From RSM it has been observed that calculated value of F (20.36) was greater than the F-table value (3.02) and hence the model developed can be effectively used for machining of Ti–6Al–4V alloy. Further the developed RSM based expert system model can be successfully used to predict the force generated during cutting process while machining Ti–6Al–4V alloy under MQL conditions.

Development of a Low Cost Eco-Friendly Minimum Quantity Lubrication System for Machining Processes

Recently all environmental worries are calling for reducing the usage of fluids in machining operations. One of the promising solutions that appeared lately is minimum quantity lubrication (MQL). This research aimed to develop an eco-friendly cooling system for a lathe machine and assess its performance. After considering the customer needs, the needs were translated into engineering specifications in the conceptual design phase, and then the quality function deployment was developed. Three concepts were generated and evaluated considering the selection criteria, and a final concept was selected using the decision matrix method. Following this, a detailed design and fabrication of the subsystems such as the oil tank and a structure accommodate all the components. The developed system was tested on six different workpiece samples to compare the MQL system with the conventional one. In general, the MQL system resulted in lower surface roughness values as well as lower tool wear.

Performance Assessment and Chip Morphology Evaluation of Austenitic Stainless Steel under Sustainable Machining Conditions

Sustainable manufacturing has received great attention in the last few decades for obtaining high quality products with minimal costs and minimal negative impacts on environment. Sustainable machining is one of the main sustainable manufacturing branches, which is concerned with improving environmental conditions, reducing power consumption, and minimizing machining costs. In the current study, the performance of three sustainable machining techniques, namely dry, compressed air cooling, and minimum quantity lubrication, is compared with conventional flood machining during the turning of austenitic stainless steel (Nitronic 60). This alloy is widely used in aerospace engine components, medical applications, gas power industries, and nuclear power systems due to its superior mechanical and thermal properties. Machining was performed using SiAlON ceramic tool with four different cutting speeds, feeds and a constant depth of cut. Consequently, various chip characteristics such as chip morphology, chip thickness, saw tooth distance and chip segmentation frequency were analyzed with both optical and scanning electron microscopes. Performance assessment was performed under the investigated cutting conditions. Our results show that the tool life under MQL machining are 138%, 72%, and 11% greater than dry, compressed air, and flooded conditions, respectively. The use of SiAlON ceramic tool results is more economically viable under the MQL environment as the overall machining cost per component is lower ($0.27) as compared to dry ($0.36), compressed air ($0.31), and flooded ($0.29) machining conditions. The minimum quantity lubrication technique outperformed the other investigated techniques in terms of eco-friendly aspects, economic feasibility, and technical viability to improve sustainability.

Study of a Multicriterion Decision-Making Approach to the MQL Turning of AISI 304 Steel Using Hybrid Nanocutting Fluid

The enormous use of cutting fluid in machining leads to an increase in machining costs, along with different health hazards. Cutting fluid can be used efficiently using the MQL (minimum quantity lubrication) method, which aids in improving the machining performance. This paper contains multiple responses, namely, force, surface roughness, and temperature, so there arises a need for a multicriteria optimization technique. Therefore, in this paper, multiobjective optimization based on ratio analysis (MOORA), VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), and technique for order of preference by similarity to ideal solution (TOPSIS) are used to solve different multiobjective problems, and response surface methodology is also used for optimization and to validate the results obtained by multicriterion decision-making technique (MCDM) techniques. The design of the experiment is based on the Box–Behnken technique, which used four input parameters: feed rate, depth of cut, cutting speed, and nanofluid concentration, respectively. The experiments were performed on AISI 304 steel in turning with minimum quantity lubrication (MQL) and found that the use of hybrid nanofluid (Alumina–Graphene) reduces response parameters by approximately 13% in forces, 31% in surface roughness, and 14% in temperature, as compared to Alumina nanofluid. The response parameters are analyzed using analysis of variance (ANOVA), where the depth of cut and feed rate showed a major impact on response parameters. After using all three MCDM techniques, it was found that, at fixed weight factor with each MCDM technique, a similar process parameter was achieved (velocity of 90 m/min, feed of 0.08 mm/min, depth of cut of 0.6 mm, and nanoparticle concentration of 1.5%, respectively) for optimum response. The above stated multicriterion techniques employed in this work aid decision makers in selecting optimum parameters depending upon the desired targets. Thus, this work is a novel approach to studying the effectiveness of hybrid nanofluids in the machining of AISI 304 steel using MCDM techniques.

Research of multi-response optimization of milling process of hardened S50C steel using minimum quantity lubrication of Vietnamese peanut oil

This study aims to build a regression model when surveying the milling process on S50C steel using Minimum Quantity Lubrication (MQL) of Vietnamese peanut oil-based on Response Surface Methodology. The paper analyses and evaluates the effect of cutting parameters, flow rates, and pressures in minimum quantity lubrication system on cutting force and surface roughness in the milling process of S50C carbon steel materials after heat treatment (reaching a hardness of 52 HRC). The Taguchi method, one of the most effective experimental planning methods nowadays, is used in this study. The statistical analysis software, namely Minitab 19, is utilized to build a regression model between parameters of the cutting process, flow rates and pressures of the minimum quantity lubrication system and the cutting force, surface roughness of the part when machining on a 5-axis CNC milling machine. Thereby analyzing and predicting the effect of cutting parameters and minimum quantity lubrication conditions on the surface roughness and cutting force during machining to determine the influence level them. In this work, the regression models of Ra and F were achieved by using the optimizer tool in Minitab 19. Moreover, the multi-response optimization problem was solved. The optimum cutting parameters and lubricating conditions are as follows: Cutting velocity Vc=190.909 m/min, feed rate fz=0.02 mm/tooth, axial depth of cut ap=0.1 and nozzle pressure P=5.596 MPa, flow rate Q=108.887 ml/h. The output parameters obtained from the above parameters are Ra=0.0586 and F=162.035 N, respectively. This result not only provides the foundation for future research but also contributes reference data for the machining process