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There is an increase in awareness about sustainable manufacturing process. Manufacturing industries are backbone of a country's economy. Although it is important but there is a great concern about consumption of resources and waste creation. The primary aim of this study was to explore sustainability concern in turning process in an Indian machining industry. The effect of cutting parameters, Speed/Feed/Depth of Cut, the machining environment, Dry/MQL/Wet, and the type of cutting tool on sustainability factors under study were observed. Analysis of Variance (ANOVA) was used to analyse the data obtained from experimentation in a small scale machining industry. The process is modelled mathematically using response surface methodology (RSM).The economic and environmental aspect like surface roughness, material removal rate and energy consumption were considered as sustainability factors. The model helps to understand the effect of the cutting parameters and conditions on surface finish, energy consumption, and material removal rate. The process was optimized for minimum power consumption considering environmental concern as prime importance. Studies suggest that the cutting environment and tool type influenced on the power consumption during turning process. Extended form of the proposed model could be useful to predict the environmental impact due to machining process, which would bring environmental concern into conventional machining.

Six Sigma is one of the popular methodologies used by the companies to improve the quality and productivity. It uses a detailed analysis of the process to determine the causes of the problem and proposes a successful improvement. Various approaches are adopted while following Six Sigma methodologies and one of them is DMAIC. The successful implementation of DMAIC and FTA is discussed in this paper. In this study, the major problem was of continuous rework up to 16%, which was leading to wastage of man hours and labor cost. Initially, fault tree analysis (FTA) was used to detect the key process input variables (KPIVs) affecting the output. Multivariable regression analysis was performed to know the possible relationship between the KPIVs and the output. The DMAIC methodology was successfully implemented to reduce the rework from 16% bores per month to 2.20% bores per month. The other problem of nonuniform step bores was also reduced significantly.

The demand for fuel is increasing, and the availability of fossil fuel reserves is limited. The amount of concern arising from the emission problems causing the environment and ecosystem are increasing exponentially. It requires the industry to find the optimum solution. Biodiesel can be stored and used as petroleum diesel. It can be used in blended or pure forms without any modification in the engine. Use of bio-diesel has shown a remarkable reduction of toxic emissions and noise and emissions. This research deals with the use of Jatropha oil as biodiesel to improve the emission characteristics; at the same time, the performance characteristics need to be improved. The diesel engine is optimized with different blends of Jatroha oil as biodiesel, compression ratio, and load using L27 orthogonal array of full factorial design of experiment. The emission parameters, such as HC, CO, and CO2 are measured. The performance parameters viz brake power, brake thermal efficiency, specific fuel consumption, and volumetric efficiency are calculated. The entropy method determines the weight. Optimization is performed using multi-criteria decision-making technique with the TOPSIS method.The results show that blend B10 and a compression ratio of 15 found to be the optimum setting for diesel engine using biodiesel blends to optimize the performance.