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In general machine tool vibration that occurs during any metal cutting operation or machining process. These vibrations increase the temperature of the cutting tool in additional which ultimately causes the reduction of cutting tool life. In this study two types of materials (Copper and Brass) were used as a damping material to decrease cutting tool vibration and cutting tool tip temperature. The structural modification was made in the normal cutting tool holder to provide the additional damping effects during the metal cutting operation. A new damped turning tool holder was fabricated by fitting these materials to improve the stability of the cutting tool holder. The standard and recommended levels of input parameters like cutting speed, feed rate, depth of cut and overhang length of the tool were selected for experimental works. The influence of input parameters was investigated for various cutting conditions. From the result, the optimum conditions and desired outcome was obtained with the damped turning tool holder also considerably vibration control was achieved during turning operation.

The modeling and simulation of metal cutting processes become very important method in order to decrease the cost of the experimental investigations. This investigation focused on the validation of simulated cutting forces obtained from a finite element based Deform2D software. Also study the variation of cutting speed and feed rate on cutting forces and tool interface temperature in the cutting zone region has been studied using Finite Element Model (FEM). Deform2D software was implemented to simulate the cutting forces and tool interface temperature during machining with different speed and feed rate with constant depth (1 mm) at normal room temperature. The cutting forces and tool tip temperature values were gradually increased with increment in cutting speed and feed rate. The optimum minimized values of cutting force and tool tip temperature was predicted with the aid of taguchi analysis. It was found that the optimum value obtained from taguchi method was compared with simulated result by Deform2D and dry machining.

Summary Background Antibiotic-associated diarrhoea (AAD) is a side-effect of antibiotic consumption and probiotics have been shown to reduce AAD. Methods A multicentre, double-blind, placebo-controlled, randomized trial was conducted to evaluate the role of Lactobacillus casei DN114001 (combined as a drink with two regular yoghurt bacterial strains) in reducing AAD and Clostridioides difficile infection in patients aged over 55 years. The primary outcome was the incidence of AAD during 2 weeks of follow-up. Results A total of 1127 patients (mean age ± standard deviation: 73.6 ± 10.5) were randomized to the active group (N = 549) or placebo group (N = 577). Both groups were followed up as per protocol. The proportion of patients experiencing AAD during follow-up was 19.3% (106/549) in the probiotic group vs 17.9% (103/577) in the placebo group (unadjusted odds ratio 1.10, 95% confidence interval 0.82–1.49, P = 0.53). Conclusions No significant evidence was found of a beneficial effect of the specific probiotic formulation in preventing AAD in this elderly population drawn from a number of different UK hospitals. However, in the UK and in many other healthcare systems there have, in recent years, been many changes in antibiotic stewardship policies, an overall decrease in incidence in C. difficile infection, as well as an increased awareness of infection prevention, and modifications in nursing practice. In light of these factors, it is impossible to conclude definitively from the current trial that the study-specific probiotic formulation has no role in preventing AAD, and it is our view that further trials may be indicated, controlling for these variables.

The purpose of this article is to examine the relationship of sustainable manufacturing process and sustainability performance, which considers the environmental, economic, and social aspects. Sustainable manufacturing considering ecological system and corrosion properties has attracted interest in gaining deep insights into the application of daily business operations. Currently, there is an internal pressure in the firms to reduce the costs of production, increase the product quantity with quality, minimize the defects and reliability as well as facilitate faster and flexible delivery. In addition, these firms attempt to seek to satisfy end users and improve operational efficiency. Market pressure, society’s demand for environmental-friendly process, and regulatory compliance are mostly discussed as additional pressure to the green operations initiative in the automotive firms. In this article, an existing model was designed using the computer-aided design software and the results were analyzed to fill research gaps in the effects of the eco-system on environmental and operational performance. This study provides new research opportunities for developing models in building simulation-based design of eco-efficient systems. Finally, the article suggests that the operations should apply parsimony principles of ecosystem measurements to foresee the imminent organizational outcome to enlighten industry counterparts with guidance to enhance further robust statistical modeling and construct development.

In the manufacturing industry, most of the components are made by machining operations. The performance of the product to a large extent is dependent on the accuracy and consistency of the machining processes. Various parameters are considered to achieve the high quality of the machining. Out of which, vibration is one of the important parameters that will lead poor quality of the product and also reduce the tool life. Vibrations are induced by metal cutting operation during machining. Turning operations and especially boring operations are associated with severe vibration-related problems. Boring operation is often done with a boring bar, which is necessarily long and slender so that it can fit into the multipart work piece geometry. Such tools are lack of dynamic stiffness and stability, this manufacturing operation is repeatedly plagued with self-excited vibrations known as chatter. Magnetorheological (MR) fluid is employed in this research work to control the vibration of a boring tool. The MR fluid damper has received great attention due to its ability to reversibly change from a free flowing, linear, viscous liquid to a semi-solid when exposed to magnetic fields in just a few milliseconds and was also found to be effective in suppressing tool vibration. Vibration control during machining process is an important tactic to suppress the chatter vibration. The aim of this approach is to reduce the relative displacements between the tool and the work piece during the machining process.

A novel fast error model generation has been predicted. The most effective way to reduce the experimental time is to increase the depth of cut and maintain the linearity of the error model. Using resistant thermometer, detector temperatures at different points are noted. Graphical analysis is made.

The recent developments in manufacturing organizations recognize sustainability as an important value addition for survival in the competitive scenario. The design engineers are in search of approaches for creating environmental conscious products. The purpose of this paper is to report a research carried out for ensuring sustainable product design by the integration of Design for Environment (DFE) and Design for Manufacture and Assembly (DFMA) methodologies. In this context, this paper reports a case study carried out in an automotive component. The candidate product is the charge alternator pulley. The existing pulley has been created using Computer Aided Design. Then sustainability analysis was conducted on the existing component for determining environmental impact. This is followed by the engineering analysis of the component using ANSYS. Then conceptual design changes were developed in the proposed product using DFMA concept. Then the environmental impact has been evaluated in terms of carbo...

Regenerative chatter in machining process is a root cause for tool wear and poor surface finish resulting in low production rate. Hence research has to be focused to identify an opportunity to suppress these chatters. This investigation is attempted to reduce the tool wear in boring process by incorporation of shape memory alloy (SMA) mass dampers in boring tool. The copper and Zinc based SMA mass dampers were placed at various position of boring bar and the effect of damper position and machining parameters on the tool wear was studied. Boring process was carried out on grey cast iron pipe by varying the damper position (54, 64 and 74 mm), cutting velocity (25, 50 and 75 m/min) and depth of cut (0.2, 0.4 and 0.6 mm). The prediction model was developed to correlate the relationship between machining parameters and tool wear. The optimum damper position and machining parameters to minimize the tool wear was determined using response surface methodology (RSM). The confirmation test on the optimized value has witnessed efficient prediction of the damper position and machining parameters for reduced tool wear in boring process.

Particle damping is one of the promising damping techniques in effectively suppressing chatter in boring process. In earlier studies, granular particles are packed with the single particle type used to suppress chatter. This paper attempts at a novel hybrid approach in particle damping to increase the effectiveness of chatter suppression and enhance the machining stability. In this hybrid approach, Copper and Zinc particles are used together in equal proportion for packing inside the boring bar. The study finds this combined packing significantly improves the damping characteristic compared to the unmodified boring bar and also bar packed with single (Cu or Zn) particle type. The assessment benchmarks were the dynamic characteristics, modal analysis, damping ratio, logarithmic decrement and resonance gap of the machining system, as well as the displacement amplitude of the boring bar and the surface roughness of the work piece. The displacement amplitude of the boring bar and the surface roughness of the work piece in the combined packing case are reduced by 55% and 80% respectively when it is compared to the unmodified case.

In order to render cost effective and environmentally friendly products, sustainability is gaining importance in contemporary manufacturing organizations. The rapidly changing marketplace also means that organizations are in search of integrated approaches for creating sustainable product. In this context, this paper reports a case study carried out in an automotive component of single cylinder four stroke petrol engines. The candidate product is connecting rod. The existing handle of connecting rod has been modeled using computer-aided design (CAD). Then, the sustainability analysis has been carried out on the existing component for determining the environmental impact. This is followed by the engineering analysis of the component using computer aided engineering (CAE). Then the redesign of the handle performed by using design for manufacture and assembly (DFMA) principles. The sustainability factors have been measured in terms of carbon footprint, air acidification, energy consumption, and water impacts. It has been found that the redesigned product possesses minimal environmental impact. It could be knew from the results of the case study that the implementation of DFE (design for environment) and DFMA concept could initiate new developments in sustainable product design with minimal impact on the environment. This integrated approach is considered to be valuable for its implementation in similar product development situations to achieve business benefits such as reduced cost and time which would in turn improve the profitability.

Micromachining, which is a manufacturing technique to produce miniature parts, carries with it the major factor of precision in machining. The various processes of micromachining, be it contact or non contact methods, have their own merits and demerits. However, producing microstructures such as micro-holes which find widespread application in highly sensitive products such as automotive fuel injection nozzles, watches, medical electronics, and camera parts requires a high degree of accuracy in its profile parameters. Microdrilling, the most ideal micromachining process to generate micro-holes, can generate deeper holes with better straightness, better roundness, and smoother surfaces. This paper focuses on all existing microdrilling techniques that have been used in producing micro-holes along with the various strategies that have been adapted to improve the accuracy in hole dimensioning and its shape. The review of the various accuracy-related microdrilling operations will enable one to understand better the need for accuracy in micro-hole production, the various microdrilling techniques, and most essentially how the accuracy has been improved with technology advancement and better research. It also provides an overview which will help further propagation and implementation of more techniques to further enhance the accuracy of microdrilling operations, bringing about precise components of micromachining.

Latest trend in increasing the performance characteristics of precision machines is reducing error in machines. Today research is focused on Machine tool accuracy. The effect of thermal error contributes to major part of 70% of volumetric error. The present study aims to establish a new compensation method for CNC Turning centre. Selection of proper temperature variables is a great task in thermal error compensation. In this paper 3 thermal process variables namely spindle speed; temperature and time are used to create thermal error models. Using ANSYS simulation experiments are created.

Purpose– Micro-EDM is an important process in the field of micro-machining. Especially, theμEDM is one of the technologies widely used for manufacture of micro-parts, micro-tools and micro-components, etc. The accuracy and repeatability of theμEDM process is still highly dependent on theμWEDG process. The electrode generation and regeneration is considered a key enabling technology for improving the performance of theμEDM process. Many engineers considered the Taguchi technique as engineering judgment during multiple response optimizations. This paper aims to focus on the use of micro-WEDG process to generate a micro-tool (electrode) with minimum surface roughness and higher metal removal rate (MRR).Design/methodology/approach– In this research work, the Taguchi quality loss function analysis is used to examine and explain the influences of three process parameters (feed rate, capacitance and voltage) on the output responses such as MRR and surface roughness. Further, the optimized machining parameters were determined considering the multiple response objective using Taguchi multi-response signal-to-noise ratio.Findings– Based on the experimental result, it was concluded that the Taguchi technique is suitable for the optimization of multi-response problem.Originality/value– This paper presents an alternative approach using Taguchi's quality loss function. In most of the modern technological situations, more than one response variable is pertinent to the success of an industrial process. In this research work, the influence of feed rate, capacitance and voltage on the MRR and surface roughness (multiple responses) is investigated.

A patient with digital ischaemia and gangrene was treated with iloprost and antiplatelets for two weeks. His vasculitic screen was negative except for a positive HIV test. His vasculitis improved three weeks after treatment with antiretroviral medications. Though vasculitis is well known to be associated with HIV infection, very few cases of HIV present as vasculitis.

This paper deals with the application of Six-Sigma methodology to the flywheel casting process in foundry to minimize the defects in this process. The primary tools used in this interventionist process were the process map, cause-and-effect matrix and the failure mode effective analysis. The present study proposes to measure the performance criteria of the process through investigating the effect of working parameters, namely, moisture content, green strength, permeability, and loss on ignition on sand preparation. The experimental results were statistically analyzed and modeled through response surface methodology (RSM). Based on the findings, the optimized process parameters were taken for experiment and better performance obtained in the production process was confirmed. The comparison between the existing process and the proposed process has been attempted in this paper and the results have been discussed in detail.

In the present trend of technological development, micro-machining is gaining popularity in the miniaturization of industrial products. In this work, a hybrid process of micro-wire electrical discharge grinding and micro-electrical discharge machining (EDM) is used in order to minimize inaccuracies due to clamping and damage during transfer of electrodes. The adaptive neuro-fuzzy inference system (ANFIS) and back propagation (BP)-based artificial neural network (ANN) models have been developed for the prediction of multiple quality responses in micro-EDM operations. Feed rate, capacitance, gap voltage, and threshold values were taken as the input parameters and metal removal rate, surface roughness and tool wear ratio as the output parameters. The results obtained from the ANFIS and the BP-based ANN models were compared with observed values. It is found that the predicted values of the responses are in good agreement with the experimental values and it is also observed that the ANFIS model outperforms BP-based ANN.

This study demonstrates optimization of sand casting process parameters using Taguchi's design of experiments method. The process parameters considered are moisture content, permeability, loss on ignition, compressive strength, volatile content, vent holes, pouring time, pouring temperature, and mold pressure. An attempt has been made to obtain optimal level of the process parameters in order to generate the optimum quality characteristics of the cast iron flywheel castings. An orthogonal array, the signal-to-noise ratio, and analysis of variance are used to analyze the effect of selected process parameters and their levels on the casting defects. The result indicates that the selected process parameters significantly affect the casting defects of cast iron flywheel castings. A confirmation run is used to verify the results, which indicates that this method is more efficient in determining the best casting parameters for flywheel castings.

This paper presents a system for automated, non-contact and flexible prediction of surface roughness of end milled parts through machine vision system which is integrated with an adaptive neuro-fuzzy inference system (ANFIS). The images of milled surface grabbed by the machine vision system could be extracted using the algorithm developed in this work, in the spatial frequency domain using a twodimensional Fourier transform (2D FT) to get the features of image texture (major peak frequency F1, principal component magnitude squared value F2, and the average gray level Ga). Cutting speed(S), feed rate (F), depth of cut (D), Ga, F1 and F2 were taken as the input parameters and surface roughness as the output parameter. The results obtained from the ANFIS model was compared with the back propagation (BP) based artificial neural network (ANN). It is found that the error percentage is very close, and it is also observed that the convergence speed for the ANFIS model is higher than the BP-based ANN. The ...

The variety of vision inspection systems for welding defects in the present manufacturing scenario is needed for overcoming certain limitations such as the problem of inaccuracy in the images, non-uniformed illumination, noise and deficient contrast, and confusion in defects if they occur in the same spot at the surface and subsurface. Hence, it is imperative to design a new vision inspection system which will enable to overcome the aforementioned problems in welding. A sophisticated new vision inspection system using machine vision has been developed for this study to identify and classify the surface defects of butt joint as per standard EN25817 in metal inert gas (MIG) welding. In this proposed vision system, images of welding surfaces are captured through a CCD camera. Four frames of sequence of images are obtained using four zones of LEDs using the front light illumination system in this method. From these images, the regions of interest are segmented and the average gray levels of the characteristic features of these images are calculated. The same process can be extended further to four zones (four quadrants) of four types of welded joints. Finally, welded joints can be classified into one of the four predefined ones based on the back-propagation neural network. The proposed system demonstrates an overall accuracy of 95% from the 80 real samples tested.

In this paper, a new attempt has been made in the area of tool-based micromachining for automated, non-contact, and flexible prediction of quality responses such as average surface roughness (Ra), tool wear ratio (TWR) and metal removal rate (MRR) of micro-turned miniaturized parts through a machine vision system (MVS) which is integrated with an adaptive neuro-fuzzy inference system (ANFIS). The images of machined surface grabbed by the MVS could be extracted using the algorithm developed in this work, to get the features of image texture [average gray level (Ga)]. This work presents an area-based surface characterization technique which applies the basic light scattering principles used in other optimal optical measurement systems. These principles are applied in a novel fashion which is especially suitable for in-process prediction and control. The main objective of this study is to design an ANFIS for estimation of Ra, TWR, and MRR in micro-turning process. Cutting speed (S), feed rate (F), depth of cut (D), Ga were taken as input parameters and Ra, TWR, MRR as the output parameters. The results obtained from the ANFIS model were compared with experimental values. It is found that the predicted values of the responses are in good agreement with the experimental values.

It is an autosomal dominant, generalized disorder, affecting the connective tissue, which imparts rigidity, support, and flexibility to the cartilage, tendons, blood vessels, heart valves and other vital parts of the body. It is predominantly caused by chromosomal abnormality1 in the extracellular matrix protein fibrillin. This disease affects multiple systems.2 About 1 in 5000 are affected with the disease and around 26% of the cases have no family history. It manifests mainly as proximal aortic aneurysm, ocular lens displacement, bone overgrowth, especially the long bones. Few people diagnosed with the syndrome may experience a mitral valve prolapse, a condition that may be associated with uneven or rapid heartbeats and breathlessness. Life expectancy of patients with Marfans syndrome is mainly determined by the severity of cardiovascular involvement and has improved substantially as a result of medical and surgical management.3 Early detection and appropriate treatment is critical for patients with Marfans syndrome, as they are at an increased risk of life threatening complications of the cardiovascular system such as aortic dissection and rupture.4The patients may require lifelong treatment, representing a major commitment for themselves as well as care givers.

In this paper, trajectory tracking of two-link planar rigid robot manipulator using proportional-integral-derivative (PID) control law based on open source, C++ software has been presented. Traject...

This paper presents a system for automated, non-contact, and flexible prediction of surface roughness of end-milled parts through a machine vision system which is integrated with an artificial neural network (ANN). The images of milled surface grabbed by the machine vision system could be extracted using the algorithm developed in this work, in the spatial frequency domain using a two-dimensional Fourier transform to get the features of image texture (major peak frequency F 1, principal component magnitude squared value F 2, and the average gray level G a). Since F1 is the distance between the major peak and the origin, it is a robust measure to overcome the effect of lighting of the environment. The periodically occurring features such as feed marks and tool marks present in the gray-level image can be easily observed from the principal component magnitude squared value F 2. The experimental machining variables speed S, feedrate F, depth of cut D, and the response extracted image variables F 1, F 2, and G a could be used as input data, and the response surface roughness R a measured by Surfcorder SE-1100 (traditional stylus method) could be used as output data of an ANN ability to construct the relationships between input and output variables. The ANN was trained using the back-propagation algorithm developed in this work due to its superior strength in pattern recognition and reasonable speed. Using the trained ANN, the experimental result had shown that the surface roughness of milled parts predicted by machine vision system over a wide range of machining conditions could be got with a reasonable accuracy compared with those measured by traditional stylus method. Compared with the stylus method, the constructed machine vision system is a useful method for prediction of the surface roughness faster, with a lower price, and lower environment noise in manufacturing process. Experimental results have shown that the proposed machine vision system can be implemented for automated prediction of surface roughness with accuracy of 97.53%. The results are encouraging that machine vision system can be extended to many real-time industrial prediction applications.

In recent years, the measurement of surface roughness of a workpiece plays a vital role since the roughness of a surface has a considerable influence on the product quality and the functional aspects. In this work, a differential evolution algorithm (DEA)-based artificial neural network (ANN) has been used for the prediction of surface roughness in turning operations. Cutting speed, feed rate, depth of cut, and average gray level of the surface image of workpiece, acquired by computer vision, were taken as the input parameters and surface roughness as the output parameter. The results obtained from the DEA-based ANN model were compared with the backpropagation (BP)-based ANN. It is found that the error percentage is very close, and it is also observed that the convergence speed for the DEA-based ANN is higher than the BP-based ANN.

Machining of composite materials is an important and current topic in modern researches on manufacturing processes. Determination of optimal cutting parameters is one of the most important elements in the machinability study of composites. Optimization has significant practical importance particularly for operating the machineries. In order to increase the accuracy of drill holes, the tool must be in good condition always as much as possible. To achieve good condition of tool, the optimization of machining parameters like drill bit diameter, spindle speed, and feed rate are mandatory. The objective of this paper is to study the effect of these process parameters on thrust force, torque, and tool wear in drilling of coir fiber-reinforced composites. The optimal settings of the parameters were determined through experiments planned, conducted, and analyzed using the Box–Behnken design, Nelder–Mead, and genetic algorithm methods. This paper also aimed to increase the cutting condition of tool, i.e., minimization of tool wear by applying the optimized input parameters using Nelder–Mead and genetic algorithm techniques.

Optimization techniques using evolutionary algorithm (EA) are becoming more popular in engineering design and manufacturing activities because of the availability and affordability of high-speed computers. In this work, an attempt was made to solve multi-objective optimization problem in turning by using multi-objective differential evolution (MODE) algorithm and non-dominated sorting genetic algorithm(NSGA-II). Optimization in turning means determination of the optimal set of machining parameters to satisfy the objectives within the operational constraints. These objectives may be minimum tool wear, maximum metal removal rate or any weighted combination of both. The main machining parameters which are considered as variables of the optimization are cutting speed, feed rate, and depth of cut. The optimum set of these three input parameters is determined for a particular job-tool combination of EN24 steel and tungsten carbide during a single-pass turning which minimizes the tool wear and maximizes the metal removal rate after satisfying the constraints of temperature and surface roughness. The regression models, developed for tool wear, temperature, and surface roughness were used for the problem formulation. The non-dominated solution set obtained from MODE was compared with NSGA-II using the performance metrics and reported

In an advanced manufacturing system, accurate assessment of tool life estimation is very essential for optimising the cutting performance in turning operations. Estimation of tool life generally requires considerable time and material and hence it is a relatively expensive procedure. In this present work, back-propagation feed forward artificial neural network (ANN) has been used for tool life prediction. Speed, feed, depth of cut and flank wear were taken as input parameters and tool life as an output parameter. Twenty-five patterns were used for training the network. Recently there have been significant research efforts to apply evolutionary computational techniques for determining the network weights. Hence an evolutionary technique named particle swarm optimisation has been used instead of a back-propagation algorithm and it is proven that the experimental results matched well with the values predicted by both artificial neural network with back-propagation and the proposed method. It is found that the computational time is greatly reduced by this method .

Nowadays, micro-machining processes are playing an important role because of the technological developments in the MEMS, electrical, medical and aerospace industries. In this research work, the principal component analysis (PCA) technique is used to examine and explains the influences of three process parameters (spindle speed, feed rate and depth of cut) on the output responses such as MRR and Ra in micro-end milling process. Also this approach is used for the optimisation of multiple responses in micro-end milling operation to achieve maximum metal removal rate (MRR) and minimum surface roughness (Ra). Further, the optimised machining parameters were determined considering the multiple response objective using multiple response performance index value. The predicted value of multiple response performance index (Z) is 0.6412 which is calculated by using additive model. The measured value of multiple response performance index is 0.707, which shows good prediction accuracy. From the experimental result, it was concluded that, the PCA technique is suitable for the optimisation of multi-response problem.

In recent days, micro-machining plays increasingly important roles since there is a growing demand for a miniaturisation of industrial products. This paper focuses on the Taguchi optimisation methodology, which is applied to optimise cutting parameters in micro-end milling operation when machining aluminium block with carbide end mill cutter. In this work, an orthogonal array, signal to noise ratio and Pareto analysis of variance are employed to analyse the effect of the micro end milling parameters such as spindle speed, feed rate and depth of cut. The Taguchi technique for the design of experiment and the interaction of micro end milling parameters are investigated. From the experimental result and analysis shows that high cutting speed, low feed rate and low depth of cut such combination of optimised parameters gives low value of surface roughness. Also, from the analysis it shows that Taguchi technique is suitable to solve the above problem.

Micro-manufacturing is the vital technology in the micro-engineering field for the production of micro-tools and miniature components. This paper focuses on the use of micro-WEDG process to generate a micro tool (electrode) with minimum surface roughness (Ra) and higher material removal rate (MRR). It examines and explains the influences of three process parameters such as feed rate, capacitance and voltage on the output responses such as MRR and Ra. In this experimental work, statistical models have been developed using three-level full factorial experimental design. Further, the optimised machining parameters were determined considering the multiple response objective using design expert software. Also, this paper explains the use of developed models for multiple-response optimisation. These optimised machining parameters are validated experimentally and it is concluded that the measured response values are in good agreement with the predicted values.

Highly accurate miniaturised components are increasingly in demand for various industries such as aerospace, biomedical, electronics, environmental, communications and automotive. Micro-machining is the process of manufacturing the components by using the miniaturised technologies. In this research work, the Taguchi quality loss function analysis is used to examine and explains the influences of three process parameters (spindle speed, feed rate and depth of cut) on the output responses such as MRR and Ra in micro-end milling process. Also, this approach is used for the optimisation of multiple responses in micro-end milling operation to achieve maximum metal removal rate (MRR) and minimum surface roughness (Ra). Further, the optimised machining parameters were determined considering the multiple response objective using Taguchi MSNR. From the experimental result, it was concluded that the Taguchi technique is suitable for the optimisation of multi-response problem.

Machine vision methods of roughness measurement are being developed worldwide due to their inherent advantages including non-contact and rapid surface measurement capability. In this work, a back propagation (BP) and a differential evolution algorithm (DEA) based on artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) model have been used for the prediction of surface roughness in turning operations. Cutting speed, feed rate, depth of cut and average grey level of the surface image of work-piece, acquired by computer vision were taken as the input parameters and surface roughness as the output parameter. The results obtained from the ANN-BP, ANFIS and ANN-DEA models were compared with observed values. It is found that the predicted values are in good agreement with the experimental values. It is also found that the error percentage is minimal and it is also observed that the convergence speed for the ANN-DEA model is higher than the ANN-BP and ANFIS.

In this paper, the prediction and comparison of thrust force and torque in drilling of roselle/sisal hybrid composite material were presented. This new approach for natural fibre hybrid composite materials is based on artificial neural network (ANN) and regression models (RM). The series of drilling experiments using HSS-twist drill bits were conducted on composite specimen using MAXMILL CNC machining centre. Drill tool dynamometer has been used to measure thrust force and torque during the drilling processes. Thrust force and torque were taken as response variables and feed rate, cutting speed and drill diameter were taken as input variables. The response variables were predicted with the help of empirical relation using RM and ANN models. The predicted values of the responses by both ANN and RM were compared with the experimental values and their closeness with the experimental values was determined. The results indicate that the ANN model is more effective than RM model in prediction of thrust force and torque in drilling of natural fibre hybrid composite materials.

Micro-machining is the basic technology of micro-engineering for the production of micro-sized parts and components. Microend milling is the most important micromachining process, widely used for the manufacturing industries due to its capability of producing tedious geometric surfaces with good accuracy and surface finish. In micro end milling material removal rate is one of the important aspects, which require attention both from industry personnel as well as in Research and development. In modern industry one of the trends is to manufacture low cost product in short time. MRR which indicates processing time of the work piece and it is an important factor that greatly influences production rate and cost. MRR greatly vary with the change of cutting process parameters. This paper focuses the taguchi technique for the optimization in micro-end milling operation to achieve maximum metal removal rate (MRR) considering the spindle speed, feed rate and depth of cut as the cutting parameters. An orthogonal array, signal-to-noise (S/N) ratio and Pareto analysis of variance (ANOVA) are employed to analyze the effect of these milling parameters. The analysis of the result shows that the optimal combination for higher metal removal rate (MRR) is medium cutting speed, high feed rate and high depth of cut. Using Taguchi method for design of experiment (DOE), other significant effects such as the interaction among milling parameters are also investigated. The study shows that the Taguchi method is suitable to solve the stated problem. Based on the verification experiment it is concluded that the percentage of error of response was less.

The micro-electrical discharge machining (micro-EDM) process has proved to be an appropriate nonconventional machining method for manufacturing accurate and complex three dimensional structural micro-parts and micro-tools which are difficult to be produced by conventional machining processes. The accuracy and the repeatability of the μEDM process are still highly dependent on the μWEDG process. In this work, an orthogonal array, signal to noise (S/N) ratio and Pareto analysis of variance (ANOVA) are employed to analyze the effect of the micro-WEDG parameters such as feed rate, capacitance and voltage on MRR. This paper focuses on the Taguchi technique for the optimization in micro-WEDG process to achieve maximum metal removal rate (MRR) considering the feed rate, capacitance and voltage as the cutting parameters. Based on the experimental result and analysis it is concluded that medium feed rate, high value of capacitance and voltage such combination of optimized parameters gives high value of MRR (0.39mg/min). The experimental result shows that the most influential parameter on the MRR is feed rate, capacitance and voltage. Also from the verification experiment it is concluded that Taguchi technique is suitable to solve the above said problem.