Comparison of ANN and DoE for the prediction of laser machined micro-channel dimensions

This paper presents four models developed for the prediction of the dimensions of laser formed micro-channels. Artificial Neural Networks (ANNs) are often used for the development of predictive models. Three feed-forward, back-propagation ANN models varied in terms of the number and the selection of training data, were developed. These ANN models were constructed in LabVIEW coding. The performance of these ANN models was compared with a 33 statistical design of experiments (DoE) model built with the same input data. When compared with the actual results two of the ANN models showed greater prediction error than the DoE model. The other ANN model showed an improved predictive capability that was approximately twice as good as that provided from the DoE model

Continuous trench, pulsed laser ablation for micro-machining applications

The generation of controlled 3D micro-features by pulsed laser ablation in various materials requires an understanding of the material's temporal and energetic response to the laser beam. The key enabler of pulsed laser ablation for micro-machining is the prediction of the removal rate of the target material, thus allowing real-life machining to be simulated mathematically. Usually, the modelling of micro-machining by pulsed laser ablation is done using a pulse-by-pulse evaluation of the surface modification, which could lead to inaccuracies when pulses overlap. To address these issues, a novel continuous evaluation of the surface modification that use trenches as a basic feature is presented in this paper. The work investigates the accuracy of this innovative continuous modelling framework for micro-machining tasks on several materials. The model is calibrated using a very limited number of trenches produced for a range of powers and feed speeds; it is then able to predict the change in topography with a size comparable to the laser beam spot that arises from essentially arbitrary toolpaths. The validity of the model has been proven by being able to predict the surface obtained from single trenches with constant feed speed, single trenches with variable feed speed and overlapped trenches with constant feed speed for three different materials (graphite, polycrystalline diamond and a metal-matrix diamond CMX850) with low error. For the three materials tested, it is found that the average error in the model prediction for a single trench at constant feed speed is lower than 5 % and for overlapped trenches the error is always lower than 10 %. This innovative modelling framework opens avenues to: (i) generate in a repeatable and predictable manner any desired workpiece microtopography; (ii) understand the pulsed laser ablation machining process, in respect of the geometry of the trench produced, therefore improving the geometry of the resulting parts; (iii) enable numerical optimisation for the beam path, thus supporting the development of accurate and flexible computer assisted machining software for pulsed laser ablation micro-machining applications

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

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

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

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