The objective of the current study is to investigate the performance of tungsten carbide tool inserts in face milling of EN-31 steel under various machining environments viz. dry machining, air cooling, minimum quanti...
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The objective of the current study is to investigate the performance of tungsten carbide tool inserts in face milling of EN-31 steel under various machining environments viz. dry machining, air cooling, minimum quantity lubrication(MQL) and flood cooling. Tool performance is generally determined by the progression of the tool flank wear during machining. Using Taguchi's approach of design of experiments, machining environments is combined with machining parameters such as cutting speed, feed rate and depth of cut, and experiments is carried out as per mixed L18 orthogonal array. Taguchi based analysis of mean (ANOM) and analysis of variance (ANOVA) is utilized to check the effects of input parameters on tool flank wear. The optimum machining parametric setting for minimum tool flank wear is observed as MQL with lubricant flow rate (LFR) of 150 ml/hr, 110 m/min cutting speed, 60 mm/min feed rate and 0.4 mm depth of cut. From the results of the ANOM, tool flank wear in the case of MQL with LFR of 150 ml/hr is observed to be 5.29% lower as compared to flood cooling. The mathematical model revealed that the second-order regression model accurately determined the variability of tool flank wear with the input parameters with least error.
The relationship between the structure parameters of PTT cotton union fabric and the fabric elasticity was analyzed by the method of multi-variable linear regression. The results indicated that the length of float was...
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ISBN:
(纸本)9787122067876
The relationship between the structure parameters of PTT cotton union fabric and the fabric elasticity was analyzed by the method of multi-variable linear regression. The results indicated that the length of float was the most important factor to influence the fabric elasticity, the second important factor was warp tightness and the third factor was weft density. The elastic recovery was mostly influenced by weft count and the elastic elongation in elastic direction.
This paper presents short term load forecasting using multi-variable linear regression (MLR) for big data. Load forecasting is very important for planning, operation, resource scheduling and so on in power system. Tot...
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This paper presents short term load forecasting using multi-variable linear regression (MLR) for big data. Load forecasting is very important for planning, operation, resource scheduling and so on in power system. Total electric demand dynamically changes in a power system and mainly depends on temperature, humidity, wind speed, human nature, regular activities, events, etc. input variables. For the help of sensors and data science, enough historical and future input data with good accuracy are easily available. On the other hand, linearregression is a proven method, widely used in industries for forecasting. It is deterministic and robust. However, it is slow for big data because it needs large size matrix operations. In this paper, linearregression is formulated for small number of variables with big data and multi-core parallel processing is applied in all matrix operations that allow unlimited historical big data and unlimited scenarios in acceptable execution time limit. Mean absolute percent error is 3.99% of real field recorded data shown in Simulation and Result section.
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