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Fuzzy Logic-Based Approach To Investigate The Novel Uses Of Nano Suspended Lubrication In Precise Machining Of Aerospace Al Tempered Grade 6061
Faculty of Engineering University of Malaya
Temper-grade aluminum alloy Al-6061-T6 is commonly used for many engineering purposes owing to its superior mechanical properties. Due to the practical importance, machining Al-6061-T6 alloy is crucial for different applications. The development of computer numerical control (CNC) of milling machines is in progress by researchers worldwide for its noteworthy advantages. The quality of machining determines the product's appearance, function and reliability. Appropriate lubrication at the machining zone improves the tribological characteristic of Al-6061-T6 alloy, leading to higher product quality. For reasons of ambiguity during machining, the soft computing technique is chosen to predict the output. In this particular research scope, …
Cutting Force-Based Adaptive Neuro-Fuzzy Approach For Accurate Surface Roughness Prediction In End Milling Operation For Intelligent Machining
Faculty of Engineering University of Malaya
End milling is one of the most common metal removal operations encountered in industrial processes. Product quality is a critical issue as it plays a vital role in how products perform and is also a factor with great influence on manufacturing cost. Surface roughness usually serves as an indicator of product quality. During cutting, surface roughness measurement is impossible as the cutting tool is engaged with the workpiece, chip and cutting fluid. However, cutting force measurement is easier and could be used as an indirect parameter to predict surface roughness. In this research work, a correlation analysis was initially performed …
Fuzzy Logic Based Model For Predicting Surface Roughness Of Machined Al-Si-Cu-Fe Die Casting Alloy Using Different Additives-Turning
Faculty of Engineering University of Malaya
This paper presents a fuzzy logic artificial intelligence technique for predicting the machining performance of Al-Si-Cu-Fe die casting alloy treated with different additives including strontium, bismuth and antimony to improve surface roughness. The Pareto-ANOVA optimization method was used to obtain the optimum parameter conditions for the machining process. Experiments were carried out using oblique dry CNC turning. The machining parameters of cutting speed, feed rate and depth of cut were optimized according to surface roughness values. The results indicated that a cutting speed of 250 m/min, a feed rate of 0.05 mm/rev, and a depth of cut of 0.15 mm …
Investigating The Machinability Of Al-Si-Cu Cast Alloy Containing Bismuth And Antimony Using Coated Carbide Insert
Faculty of Engineering University of Malaya
Surface roughness and cutting force are two key measures that describe machined surface integrity and power requirement evaluation, respectively. This investigation presents the effect of melt treatment with addition of bismuth and antimony on machinability when turning Al-11% Si-2% Cu alloy. The experiments are carried out under oblique dry cutting conditions using a PVD TIN-coated insert at three cutting speeds of 70, 130 and 250 m/min, feed rates of 0.05, 0.1, 0.15 mm/rev, and 0.05 mm constant depth of cut. It was found that the Bi-containing workpiece possess the best surface roughness value and lowest cutting force due to formation …
Investigating The Surface Tribology Of Roller-Burnished Polymer Using The Fuzzy Rule-Based Approach
Investigating The Surface Tribology Of Roller-Burnished Polymer Using The Fuzzy Rule-Based Approach
Faculty of Engineering University of Malaya
Burnishing is specified as the plastic deformation cold working process applied as surface treatment and smoothing following machining to obtain a superior surface roughness finish. The present experimental study was carried out on roller-burnished polyurethane using a roller burnishing tool. An analysis was done to investigate the effect of burnishing depth, speed, feed rate, and roller width on the surface roughness of the polymer workpiece. Roughness (R-a) prediction was achieved with a fuzzy rule-based system. The results indicate 95% accuracy between the fuzzy predicted roughness values and experimental results. Link to Full-Text Articles : www.tandfonline.com/doi/abs/10.1080/10402004.2014.962205