Posture prediction and optimization for a manual assembly operation involving lifting of weights

The present work combines ergonomics with the posture prediction in the assembly process to avoid musculoskeletal issues of human operator. For improved productivity the operator should be in a better work environment and in sound health. The purpose of this paper is to provide a different perspective to avoid ergonomic risk factors in manual assembly. Here, a human is modeled as 20-DOF as modeled in robotic analysis and simulated in a virtual environment. In the present study, two objective cost functions i.e. joint discomfort function and energy expenditure function have been employed for evaluating the optimized posture. For posture prediction, a combined multi-objective optimization (MOO) method is used and the objective cost functions are minimized i.e. less joint discomfort and less energy in MOO method required to do the manual assembly operation and consequently, the results are compared and finally the movements are tested using REBA technique

Analysis, predictive modelling and multi-response optimization in electrical discharge machining of Al-22%SiC metal matrix composite for minimization of surface roughness and hole overcut

Due to the widespread engineering applications of metal matrix composites especially in automotive, aerospace, military, and electricity industries; the achievement of desired shape and contour of the machined end product with intricate geometry and dimensions that are very challenging task. This experimental investigation deals with electrical discharge machining of newly engineered metal matrix composite of aluminum reinforced with 22 wt.% of silicon carbide particles (Al-22%SiC MMC) using a brass electrode to analyze the machined part quality concerning surface roughness and overcut. Forty-six sets of experimental trials are conducted by considering five machining parameters (discharge current, gap voltage, pulse-on-time, pulse-off-time and flushing pressure) based on Box-Behnken's design of experiments (BBDOEs). This article demonstrates the methodology for predictive modeling and multi-response optimization of machining accuracy and surface quality to enhance the hole quality in Al-SiC based MMC, employing response surface methodology (RSM) and desirability function approach (DFA). Finally, a novel approach has been proposed for economic analysis which estimated the total machining cost per part of rupees 211.08 during EDM of Al-SiC MMC under optimum machining conditions. Thereafter, under the influence of discharge current several observations are performed on machined surface morphology and hole characteristics by scanning electron microscope to establish the process. The result shows that discharge current has the significant contribution (38.16% for Ra, 37.12% in case of OC) in degradation of surface finish as well as the dimensional deviation of hole diameter, especially overcut. The machining data generated for the Al-SiC MMC will be useful for the industry

Experimental investigation, modelling and optimization in hard turning of high strength low alloy steel (AISI 4340)

The present study addresses the machinability investigation of high strength low alloy grade AISI 4340 steel with coated ceramic tools on surface roughness, tool wear, and economic analysis by considering the hard turning process parameters such as cutting speed, feed and depth of cut. Twenty seven set of trials according to full factorial design of experiments are conducted. Analysis of variance, multiple regression method, Taguchi method, desirability function approach and Gilbert’s technique are employed for parametric influence study, predictive modelling, response optimization, tool life estimation followed by cost analysis. Results indicated that feed and cutting speed are the most significant and crucial factors for hard turning operation in order to achieve minimum surface roughness of machined component as well as flank wear of cutting tool. Abrasions, adhesion followed by plastic deformation are the key wear mechanisms of coated ceramic insert, resulted 47 min of tool life under optimum cutting conditions and ensued lower total machining cost per part ($ 0.29 only) due to higher tool life, and reduced downtime that justifies cost effectiveness of hard turning. Novelty aspects, the current research work demonstrates the substitution of conventional, expensive and slow cylindrical grinding process, and proposes the most expensive CBN tool alternative using coated ceramic tools in hard turning process from techno-economical and ecological point of views in line with the industrial requirements

Process parameter optimization of Al/SiC metal matrix composites during ultrasonic machining process

Metal matrix is highly acceptable composites providing good strength for industrial use. In many field of industries, especially aerospace industry metal matrix composites of type Al/SiC is used because of its superior properties. In this research work, experimentalanalysis has been done for producing through hole on metal matrix composites with suitable quality ultrasonic machining (USM) process. Three unconstrained process parameters are chosen, like abrasive slurry concentration, power rating sand tool feed rate. Material removal rate (MRR) is considered as response parameter. The effects of each parameter have been analyzed here. Analysis of variance (ANOVA) has also been applied to identify the most significant factor. Response surface methodology (RSM) has been utilized to developed empirical model for determine the performance of ultrasonic process. Optimization technique has been used to find out the maximum process MRR. Confirmation verification test has been done to improve optimal parametric condition for getting maximum MRR. This research paper gives viability application of USM process for producing of through hole on metal matrix composites and various applications in industry

Parametric optimization of Nd:YAG laser microgrooving on aluminum oxide using integrated RSM-ANN-GA approach

Abstract Nowadays in highly competitive precision industries, the micromachining of advanced engineering materials is extremely demand as it has extensive application in the fields of automobile, electronic, biomedical and aerospace engineering. The present work addresses the modeling and optimization study on dimensional deviations of square-shaped microgroove in laser micromachining of aluminum oxide (Al2O3) ceramic material with pulsed Nd:YAG laser by considering the air pressure, lamp current, pulse frequency, pulse width and cutting speed as process parameters. Thirty-two sets of laser microgrooving trials based on central composite design (CCD) design of experiments (DOEs) are performed, and response surface method (RSM), artificial neural network (ANN) and genetic algorithm (GA) are subsequently applied for mathematical modeling and multi-response optimization. The performance of the predictive ANN model based on 5-8-8-3 architecture gave the minimum error (MSE = 0.000099) and presented highly promising to confidence with percentage error less than 3% in comparison with experimental result data set. The ANN model combined with GA leads to minimum deviation of upper width, lower width and depth value of − 0.0278 mm, 0.0102 mm and − 0.0308 mm, respectively, corresponding to optimum laser microgrooving process parameters such as 1.2 kgf/cm2 of air pressure, 19.5 Amp of lamp current, 4 kHz of pulse frequency, 6% of pulse width and 24 mm/s of cutting speed. Finally, the results have been verified by performing a confirmatory test

Experimental Investigation on Dimensional Characteristics and Surface Morphology of Microchannels Fabricated on Smart Ceramic by DPSS Nd:YAG Laser

730-737Smart ceramic material like barium titanate (BaTiO3) is in high demand in today's highly competitive precision industries; as it has numerous applications in electronic, biomedical, and aerospace engineering.In this endeavor, laser micro-milling approach (LMMA) hasbeenattempted with a suitable experimental design plan; to scrutinize the laser influencing variables against the LMMA outcomes during the processing of BaTiO3 throughout the fabrication of microchannels. This article presents an investigational act on the fabricated micro-channels to discern the impacts of LMMA parameters (gas pressure, scan strategy, current and scanning speed) against the dimensional (like deviations in channel upper and lower width) and surface characteristics of the surface feature. The surface morphology study hasbeen accomplished with the support of energy dispersive spectroscopy (EDS) in conjunction with scanning electron microscope (SEM) to scrutinize the elemental alterations and surface characteristics at the zone of laser ablation. A statistical multi-objective optimization (MOO) technique known as grey relational analysis (GRA) has beenused later in this paper to predict an optimal parametric setting. The MOO results’ efficacy has been validated further in the corroboration assessments, the predicted optimal solutions have been obtained with an error of 4.57 %, 3.89 % and 4.88 % for W-RCL, LWD and UWD respectively

ESTIMATING THE EFFECT OF MACHINING PARAMETERS ON SURFACE ROUGHNESS DURING MACHINING OF HARDENED EN24 STEEL USING COATED CARBIDE INSERTS

In the present study, an attempt has been made to evaluate the performance of multilayer coated carbide inserts during dry turning of hardened EN24 steel (47 HRC). The effect of machining parameters (depth of cut, feed and cutting speed) on surface roughness parameters (Ra and Rz) were investigated by applying ANOVA. The experiments were planned based on Taguchi’s L27 Orthogonal array design. Results showed that surface roughness parameters (Ra and Rz) are mainly influenced by feed and cutting speed, whereas depth of cut exhibits minimum influence on surface roughness (Rz) and neglegible influence in case of surface roughness (Ra). The experimental data were further anlyzed to predict the optimal range of surface roughness parameters (Ra and Rz). Finally, second order regression models were carried out to find out the relationship between the machining parameters and surface roughness parameters