Atul B. Andhare

Application of psychoacoustics for gear fault diagnosis using artificial neural network

Identification of correct working of gearbox is a very important function during end of line inspection in the assembly line while manufacturing the gearbox. Such inspection is performed by an operator by listening to the sound of gearbox while running it on a test bench. Based on the sound emitted by the gearbox combined with experience and judgment of the operator, the gearbox is passed or rejected for fitting inside the vehicle. This paper makes an attempt to use artificial intelligence techniques to identify gearbox condition in the above environment by using psychoacoustic features to replace human hearing. Experiments are carried out on a gearbox test rig and sound data are acquired for good and faulty gear conditions. Psychoacoustic features and statistical indices are extracted from the data and these are then used as input to an artificial neural network. The artificial neural network output is the condition of gearbox. Performances of psychoacoustic and statistical indices are then compared. It is found that psychoacoustic features are able to predict gearbox condition with an accuracy of 99% and 98% for good and faulty conditions, respectively, whereas the statistical features are able to do the same with 97% and 98% accuracy. Therefore, it is concluded that psychoacoustic features have the potential to be used for the end of line inspection of gearbox in manufacturing environment and the process of inspection can be made objective by eliminating operator’s ability and judgment.

Performance of multi-walled carbon nanotube-based nanofluid in turning operation

ABSTRACT This paper evaluates the performance of nanofluid using multi-walled carbon nanotubes (MWCNT) in distilled water and sodium dodecyl sulfate surfactant for turning operation on EN 31 material. Turning was performed without any fluid, with conventional, mineral oil–based cutting fluid, and with nanofluid. The flow rates of both fluids were limited to 1 L/h and these fluids were applied at the tool tip through gravity feed. Cutting forces, wear on tool, and surface finish on workpiece were measured as responses while turning under the three conditions. The responses obtained in three different conditions of turning are then compared. It is found that application of MWCNT-based nanofluid resulted in 49% and 30% lesser tool wear than machining without any fluid and machining with mineral oil–based fluid, respectively. The use of nanofluid also resulted in 5–8% lesser cutting force and 9–22% better surface finish of the workpiece as compared with conventional cutting fluid. Thus, MWCNT-based nanofluid performed better than the conventional, oil-based cutting fluid for turning of EN 31 bars.

Optimization of Surface Roughness in Turning of Ti-6Al-4V Using Response Surface Methodology and TLBO

Surface roughness is an important surface integrity parameter for difficult to cut alloys such as Titanium alloys (Ti-6Al-4V). In the present work, initially a mathematical model is developed for predicting surface roughness for turning operation using Response Surface Methodology (RSM). Later, a recently developed advanced optimization algorithm named as Teaching Learning Based Optimization (TLBO) is used for further parameter optimization of the equation developed using RSM. The design of experiments was performed using central composite design (CCD). Analysis of variance (ANOVA) demonstrated the significant and non-significant parameters as well as validity of predicted model. RSM describes the effect of main and mixed (interaction) variables on the surface roughness of titanium alloys. RSM analysis over experimental results showed that surface roughness decreased as cutting speed increased whereas it increased with increase in feed rate. Depth of cut had no effect on surface roughness. By comparing the predicted and measured values of surface roughness the maximum error was found to be 7.447 %. It indicates that the developed model can be effectively used to predict the surface roughness. Further optimization of the roughness equation was carried out by TLBO method. It gave minimum surface roughness as 0.3120 μm at the cutting speed of 1704 RPM (171.217 m/min), feed rate of 55.6 mm/min (.033 mm/rev) and depth of cut of 0.7 mm. These results were confirmed by confirmation experiment and were better than that of RSM.Copyright

Properties of Dispersion of Multiwalled Carbon Nanotubes as Cutting Fluid

ABSTRACT This article presents the results of experiments performed to evaluate properties of dispersion of multiwalled carbon nanotubes (MWCNT) in water with sodium dodecyl sulfate (SDS) as a dispersant. Different samples of varying concentrations of MWCNTs were prepared for the analysis and properties including thermal conductivity, pH value, viscosity, wettability, etc., were evaluated. These properties were compared with the properties of conventional cutting fluid, which was taken as a mix of water and mineral oil. It was found that the thermal conductivity of the MWCNT dispersion was higher than the conventional cutting fluid by about 42%. There was a decrease in contact angle by about 70%. Thus, dispersing MWCNTs in water with SDS increases the thermal conductivity and wettability of fluid. The pH value of fluid with 0.2 vol% MWCNTs was found to be 8.4. It thus reduced the corrosive nature of water. Nanoparticles of MWCNTs did not have much influence on the viscosity of the base fluid. Thus, the use of MWCNTs in water with SDS appeared to result in a better cutting fluid for machining than conventional cutting fluid.

How cryogenic techniques help in machining of nickel alloys? A review

ABSTRACT Nickel alloys are extensively used in aerospace, automotive, marine, nuclear, petro-chemical and food processing industries due to properties like high strength, resistance to heat, resistance to corrosion, etc. However, machining of these alloys pose many challenges in machining such as: work hardening, high temperatures at the cutting zone, rapid tool wear, reduced tool-life, etc. Attempts are made to overcome these challenges by using various cryogenic techniques. This paper, therefore discusses different techniques such as cryogenic cooling, cryogenic treatment of tool and simultaneous use of cryogenic cooling of tool and heating of workpiece (hybrid technique) and their effects on machinability of Nickel alloys with the help of indicators like tool-life, surface roughness, residual stresses, etc. It is concluded that cryogenic techniques are helpful in improving the machining performance by way of improvement in tool-life and surface quality. This happens due to better cooling by cryogen and improved tool properties after cryogenic treatment. However, based on the published works, it is not possible to decide about the following: correct amount of cryogen required for cooling, appropriate cryogenic tool treatment cycle to be used and the best parameters for machining of Nickel alloys. Therefore, future research should focus on these aspects.

Evaluation of performance of nanofluid using multiwalled carbon nanotubes for machining of Ti–6AL–4V

ABSTRACT Machining of titanium alloys generate very high temperature in the cutting zone. This results in rapid tool wear and poor surface properties. Therefore, improvement in cutting performance in machining of titanium alloys is very much dependent on effectiveness of the cooling strategies applied. In the present work, performance of nanofluid using multiwalled carbon nanotubes (MWCNTs) dispersed in distilled water and sodium dodecyl sulfate (SDS) as surfactant is evaluated for turning operation on Ti–6Al–4V workpieces. Turning operations were carried out under three different conditions – dry, with conventional cutting fluid and with nanofluid. Nanofluid application was limited to 1 L/h and it was applied at the tool tip through gravity feed. Various machining responses like cutting force, surface finish and tool wear were analyzed while turning at optimum cutting parameters as 150 m/min, 0.1 mm/rev and 1 mm depth of cut. Later on, machining performance of nanofluid is confirmed at low cutting speed of 90 m/min. Nanofluid outperformed conventional cutting fluid with 34% reduction in tool wear, average 28% drop in cutting forces and 7% decrease in surface roughness at cutting speed of 150 m/min.

Multiobjective optimization for improving machinability of Ti-6Al-4V using RSM and advanced algorithms

Abstract This paper explores use of Teaching Learning Based Optimization (TLBO), ‘JAYA’ (Sanskrit word means Victory) and Genetic Algorithm (GA) for the combined minimization of roughness of machined surface and forces generated in cutting in turning of Ti-6Al-4V. Experimentation was carried out with Response Surface Methodology (RSM) and the Central Composite Design (CCD). Speed of cutting (m/min), feed rate (mm/min) and depth of cut (mm) were the design variables for optimization. Two responses (roughness of machined surface and force of cutting) were independently minimized. RSM was useful in finding empirical relations and the effect of each parameter and their interactions on the responses considered. Analysis of variance (ANOVA) was used to find out the effective and non-effective factors and correctness of the models. Later on, a multi-objective optimization function was developed for minimizing both – roughness in machined surface and force generated in cutting using weights method and the correctness of weights were confirmed by Analytical Hierarchy Process (AHP). After formulating the combined objective function, TLBO, ‘JAYA’ and GA methods were used for further parameter optimization of the turning process. Performance of TLBO and ‘JAYA’ algorithm was compared with that of Genetic Algorithm (GA). It is found that TLBO and ‘JAYA’ performed better than GA in the combined minimization of roughness and forces in while turning Ti-6Al-4V. It is also found from the results that higher cutting speed (171.4 m/min) and lower feed rate (55.6 mm/min) can produce better surface roughness and minimum cutting forces in machining of Ti-6Al-4V.

Modeling and Dynamic Force Simulation for Detection of Profile Error in Spur Gear Pair

This paper describes modeling and simulation of spur gear pair in Adams–View. Initially, virtual model of an ideal gear pair is created in Adams and variation of the dynamic force is obtained by considering the tooth mesh stiffness and mesh damping coefficient. The magnitude of force variation thus obtained is confirmed with the design calculations. The dynamic force magnitudes thus obtained and those found in Adams are closely matching. Later on, tooth profile error is introduced in the gear pair by removing part of involute surface from one of the gears making one tooth surface as flat. Dynamic force variation is now obtained for the defective gear pair. After comparing the dynamic forces for defect free and defective gear pairs, it is found that change in dynamic force pattern is a good indicator of tooth profile error in spur gears.