Austenitic stainless steel AISI 316L is used in many applications, including chemical industry, nuclear power plants, and medical devices, because of its high mechanical properties and corrosion resistance. Machinability study on the stainless steel is of interest. Toward sustainable manufacturing, this study also includes the power consumption during machining along with other machining responses of cutting force, surface roughness, and tool life. Turning on the stainless steel was performed using coated carbide tool without using cutting fluid. The turning was performed at various cutting speeds (90, 150, and 210 m/min) and feeds (0.10, 0.16, and 0.22 mm/rev). Response surface methodology was adopted in designing the experiments to quantify the effect of cutting speed and feed on the machining responses. It was found that cutting speed was proportional to power consumption and was inversely proportional to tool life, and showed no significant effect on the cutting force and the surface roughness. Feed was proportional to cutting force, power consumption, and surface roughness and was inversely proportional to tool life. Empirical equations developed from the results for all machining responses were shown to be useful in determining the optimum cutting parameters range.

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This paper proposed a new algorithm for fault location and classification using wavelet based on Clarke's transformation to obtain the fault current. This novel method of fault current approach is studied by comparing the use of the glide path of the fault voltage. The current alpha and beta (Current Mode) were used to transform the signal using discrete wavelet transform (DWT). The fault location was determined by using the Clarke's transformation, and then turned into a wavelet, which was very precise and thorough. The most accurate was the mother wavelet Db4 which had the fastest time and smallest error detection when compared with the other wavelet mothers. In this study, the Clarke's transformation is also compared with the Karenbauer's, which has produced results with similar error percentage. The simulation results using PSCAD / EMTDC software showed that the proposed algorithm could distinguish internal and external faults to get the current signal in the transformation of a signal fault.

Downsizing energy consumption during the machining of metals is vital for sustainable manufacturing. As a prerequisite, energy consumption should be determined, through direct or indirect measurement. The manufacturing process of interest is the finish turning which has been explored to generate (near) net shapes, particularly for hardened steels. In this paper, we propose using measured cutting forces to calculate the electrical energy consumption during the finish turning process of metals where typically the depth of cut is lower than the cutting tool nose radius. In this approach, the resultant cutting force should be used for calculating the energy consumption, instead of only the main (tangential) cutting force as used in the conventional approach. A case study was carried out where a hardened stainless steel (AISI 420, hardness of 47–48 HRC) was turned using a coated carbide tool, with a nose radius of 0.8 mm, without cutting fluid, and at 0.4 mm depth of cut. The experimental design varied the cutting speed (100, 130, and 170 m/min) and feed (0.10, 0.125, and 0.16 mm) while other parameters were kept constant. The results indicate that the electrical energy consumption during the particular dry turning of hardened steel can be calculated using cutting force data as proposed. This generally means machining studies that measure cutting forces can also present energy consumption during the finish or hard turning of metals, without specifically measuring the power consumption of the machining process. For this particular dry turning of hardened stainless steel, cutting parameters optimization in terms of machining responses (i.e., low surface roughness, long tool life, low cutting force, and low energy consumption) was also determined to provide an insight on how energy consumption can be integrated with other machining responses towards sustainable machining process of metals.