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.
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.
Material loading is one of the most critical operations in earthmoving projects. A number of different equipment is available for loading operations. Project managers should consider different technical and economic i...
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Material loading is one of the most critical operations in earthmoving projects. A number of different equipment is available for loading operations. Project managers should consider different technical and economic issues at the feasibility study stage and try to select the optimum type and size of equipment fleet, regarding the production needs and project specifications. The backhoe shovel is very popular for digging, loading and flattening tasks. Adequate cost estimation is one of the most critical tasks in feasibility studies of equipment fleet selection. This paper presents two different cost models for the preliminary and detailed feasibility study stages. These models estimate the capital and operating cost of backhoe shovels using uni-variable exponential regression (UVER) as well as multi-variable linear regression (MVLR), based on principal component analysis. The UVER cost model is suitable for quick cost estimation at the early stages of project evaluation, while the MVLR cost function, which is more detailed, can be useful for the feasibility study stage. Independent variables of MVLR include bucket size, digging depth, dump height, weight and power. Model evaluations show that these functions could be a credible tool for cost estimations in prefeasibility and feasibility studies of mining and construction projects.
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