Elsevier

Journal of Cleaner Production

Volume 53, 15 August 2013, Pages 195-203
Journal of Cleaner Production

Optimization of cutting parameters for minimizing energy consumption in turning of AISI 6061 T6 using Taguchi methodology and ANOVA

https://doi.org/10.1016/j.jclepro.2013.03.049Get rights and content

Highlights

  • Energy consumed during machining is the variable that must be optimized.

  • For minimizing energy consumed, feed rate is the most significant factor (87.79%).

  • For minimizing surface roughness, feed rate is the most significant factor (98.06%).

  • Higher feed rate minimizes the energy consumption but maximizes surface roughness.

Abstract

Machine tools are responsible for environmental impacts owing to their energy consumption. Cutting parameters have been optimized to minimize cutting power, power consumed or cutting energy. However, these response variables do not consider the energy demand that ensures the readiness of the machine tool. The present paper outlines an experimental study to optimize cutting parameters during turning of AISI 6061 T6 under roughing conditions in order to get the minimum energy consumption. An orthogonal array, signal to noise (S/N) ratio and analysis of variance (ANOVA) were employed to analyze the effects and contributions of depth of cut, feed rate and cutting speed on the response variable. A comparison was done to highlight the importance of correctly selecting the response variable to be analyzed, due to the difference of the values of cutting parameters needed to optimize cutting power, cutting energy, power consumed and energy consumed during the machining process. Additional, the relationship between cutting parameters, energy consumption, and surface roughness was analyzed in order to determine the levels of the cutting parameters that lead to minimum energy consumption and minimum surface roughness. The results of this research work showed that feed rate is the most significant factor for minimizing energy consumption and surface roughness. Nevertheless, the level of this factor needed to achieve minimum energy consumption is not the same as the one needed to obtain minimum surface roughness. Higher feed rate provides minimum energy consumption but will lead to higher surface roughness.

Introduction

Preliminary environmental studies for machine tools used in discrete part manufacturing (e.g. turning and milling) indicate that more than 99% of the environmental impacts are due to the consumption of electrical energy (Li et al., 2011). It is necessary to improve manufacturing processes. As a result, many companies are adopting the concept of cleaner production.

Cleaner production relies on the creation of products using systems that do not pollute and conserve natural resources. The model should be attractive for consumers and be economically viable, safe and healthful (Pusavec et al., 2010b).

Companies that owe machine tools will save money and achieve a better sustainability performance if their energy consumption is reduced. Many of them do not consider energy efficiency as a high priority to improve their profits. For example, an estimated of two-thirds of the electrical energy used by the machining industry is for running motors and drives for cutting tools (Pusavec et al., 2010b).

Improving energy efficiency of manufacturing processes requires knowledge about the energy consumption as a function of the machine tool and cutting process itself (Li et al., 2011). One of the processes widely used in manufacturing is turning. Despite decades of optimizing this process based on cost and productivity, optimizing it using the energy consumption criterion has not received significant attention.

Applications of Taguchi techniques and statistical analysis have been used to optimize machining processes. Taguchi used experimental design for minimizing variation around a target value and for designing processes to be robust to environmental conditions and to component variations (Ross, 1996). Analysis of variance (ANOVA) is used to interpret experimental data.

Machine tools comprise numerous motors and auxiliary components whose energy consumption can vary strongly during machining. The main spindle drive, for example, and the coolant system work near their rated power during roughing, while the power consumption during finishing is significantly lower (Heidenhain, 2010).

Therefore, this research work focuses on the energy consumed only in roughing due to the fact that is a part of the process in which a large amount of material from the workpiece is removed, so the energy needed to perform the operation is greater than the consumed to remove material during finishing turning.

In the present work, aluminum was employed to perform the experimental trials. This metal is chosen by different industries (e.g. construction, transport) due to its lightness, corrosion resistance, yield resistance, conductivity and ductility, among other properties. Also, aluminum is a 100% recyclable metal.

A key element to maximize sustainability performance is to choose materials that are both in abundant supply and have the potential for recycling and re-use with no significant environmental impact. According to Pusavec et al. (2010a), aluminum has the best sustainability evaluation, compared to steel, stainless steel, cast iron, titanium and copper alloys. This sustainability evaluation covered several factors: abundance of raw material, pollution during machining, ease of recycling, life of metal and cost of finished product.

This paper presents a work done using the Taguchi methodology for optimizing a roughing turning process. The objective of the experiment was to optimize cutting parameters so as to get the lowest value of energy consumed by the machine during all the machining process, not only in material removal.

In order to demonstrate the difference in the values of the cutting parameters needed for minimize energy or power consumption, two comparisons are presented: the former between cutting power and power consumed by the machine and the latter between cutting energy and energy consumed by the machine. In addition, the relationship between cutting parameters, energy consumption and surface roughness was analyzed.

Section snippets

Energy consumption of machine tools

According to O’Driscoll and O’Donnell (2013), demand for energy has become so intense that it has outgrown supply so the difficult task of guaranteeing a secure energy supply arises. Complex manufacturing facilities consume a significant amount of the industrial sectors electrical energy; it is used to power motors, compressors and machine tools.

The energy consumed in the manufacturing sector is used in production processes which mainly emerge from production equipments. Machine tool is one of

Taguchi methodology

Most of the investigations presented in Section 2 employed Taguchi techniques, such as orthogonal arrays and S/N ratio analysis in order to find out the optimal values of cutting parameters that minimize the response variable. Taguchi methodology allows obtaining results using fewer experimental runs than other techniques. The results obtained may be not optimal, but when these results are implemented, process is improved. Therefore, less money and time are spent when Taguchi techniques are

Selection of process parameters

In turning process, the geometry cutting knowledge and cutting parameters control allows to guarantee the quality of machined components and to optimize costs (Correia and Davim, 2011).

Process parameters that affect the characteristics of turned parts are cutting tool parameters (tool geometry and tool material) and cutting parameters (cutting velocity, feed rate, depth of cut). These parameters depend on the workpiece material and the machine tool chosen to perform the turning operation.

Results and data analysis

The results obtained from the experimental runs carried out, according to the orthogonal array shown in Table 3 are presented in this section. Table 4 shows data for power consumed, the average of each level and its S/N ratio. Table 5 contains data for cutting power, average and S/N ratio for each level. Energy consumed from the grid was obtained from multiplying power consumed and cycle time (Table 6). The same procedure was done in order to compute energy consumed only in material removal (

Conclusions

Taguchi methodology was employed for optimizing a roughing turning process, involving aluminum AlSI 6061 T6 as the material to be machined and a cutting tool made of silicon carbide, reinforced ceramic grade. Optimum values of cutting parameters were found out in order to minimize cutting power, cutting energy, power and energy consumed during the machining process, and surface roughness Ra.

The energy consumed per machining cycle is the response variable that should be analyzed because it

Acknowledgments

The author would like to thank the Consejo Nacional de Ciencia y Tecnología (CONACyT) and the ITESM Campus Estado de México for providing financial resources.

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