Abstract
Abstract. Productivity and economy are key elements of any sustainable manufacturing system. While productivity is associated to quantity and quality, economy focuses on energy efficient processes achieving an overall high output to input ratio. Machining of hard-to-cut materials has always posed a challenge due to increased tool wear and energy loss. Cryogenics have emerged as an effective means to improve sustainability in the recent past. In the present research the use of cooling conditions has been investigated as an input variable to analyze its effect on tool wear, specific cutting energy and surface roughness in combination with other input machining parameters of feed rate, cutting speed and depth of cut. Experimental design was based on Taguchi design of experiment. Analysis of Variance (ANOVA) was carried out to ascertain the contribution ratio of each input. Results showed the positive effect of coolant usage, particularly cryogenic, on process responses. Tool wear was improved by 33 % whereas specific cutting energy and surface roughness were improved by 10 % and 9 % respectively by adapting the optimum machining conditions.
Highlights
In the present day world major efforts are made to ensure that industry processes are as productive and efficient as possible
Carbide tool is recommended for machining titanium based alloys because of its impact strength and toughness over varying temperature ranges (Zhao and Hong, 1992b)
Tool wear rate was calculated from Eq (1)
Summary
In the present day world major efforts are made to ensure that industry processes are as productive and efficient as possible. From year 1751–2000 around 337×109 t CO2 was emitted into the atmosphere leading to global warming as well as environmental degradation (Jaffery et al, 2014). The industrial sector accounts for around 26 % of the total CO2 emissions (Birol, 2017). Manufacturing sectors accounts for almost 50 % of these emissions. Environmental concerns as well as energy security issues have focused research towards sustainability, productivity and economy of manufacturing processes. This has led towards the objective of process optimization where output responses are augmented by careful selection of input parameters
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