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In recent years, minimum quantity lubrication machining has played a critical role in extending tool life, improving surface finish, and reducing tool wear during the machining of hardened steel. The reported investigation involved the utility of Hardox 500 steel for the machining process while the cubic boron nitride coated tool performed the function of removing the material. The investigation was carried out by adopting two different techniques to predict the responses, viz., the response surface methodology and the artificial neural network. These techniques have been employed to predict the possible responses in investigating machining characteristics. Also, due consideration has been made concerning the reduction of tool-tip temperature and surface roughness. The entire investigation was carried out using three different environments. They are dry machining, wet, and minimum quantity lubrication. A scanning electron microscope was used for examining the morphology of the worn tool surface.

Due to high impact resistance and abrasive resistance, AISI 4340 has been widely used in aircraft landing gear and high power transmission gears. During high speed machining process friction plays more important role in increasing temperature between tool and work piece. Owing to higher temperature in work piece and cutting tool increases wear rate and reduces the tool life. This paper investigates the effect of rake angle in normal cutting tool with various tool geometry and micro groove cutting tool during machining AISI 4340 steel with tungsten carbide cutting tool in simulation environment. Tool wear rate of micro groove cutting tool is minimum compared with other cutting tool with dissimilar tool geometry. Also micro groove tool minimize the cutting tool interface temperature.

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 major hindrance for any production industry in obtaining higher yield is the lack of achieving full material removal rate in the machine tools. If achieved, the surface quality of the machined works will be compromised. An attempt was made in this work to reduce the compromise of surface finish by integrating a plastic compliant damper that is capable of reducing the effects of unwanted vibrations generated during the machining process. The damper is designed to degenerate the effects of vibration and thereby improve product finish. It is made of acrylonitrile butadiene styrene by fused deposition modelling (an additive manufacturing technique). Measuring the vibration and cutting force is indirectly related to finish in product and tool wear rate. The stability of tool is improved greatly by the new compliant damper possessing displacement resistance. The effect of variation in cutting conditions on the performance of conventional rubber damper and plastic compliant damper was analyzed. The highest speed, feed rate, and depth of cut of 540 rpm, 0.02 mm/s, and 1.5 mm, respectively, found to be cutting condition at which imparted minimum surface roughness values of 2.80 μm and 0.52 μm with conventional rubber damper and plastic compliant damper, respectively. The speed and feed rate and depth of cut are found to be important parameters while studying surface roughness. The peak surface roughness is reduced by 48% by using the proposed compliant damper.

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.

AA 5059 aluminium alloy is well suited for defense applications as a result of high–strength to weight ratio and more toughness. The aim of this work is to investigate the effect of GTAW (Gas Tungsten Arc Welding) process parameters (Welding current, Welding speed and Gas flow rate) on welding of AA 5059 alloy. Response surface methodology (RSM) was employed to develop the empirical relationships to predict micro hardness and tensile strength of GTAW AA 5059 aluminium alloy at 95% confidence level. The results indicated that welding speed has the greatest influence followed by welding current and gas flow rate.

The present investigation pertains to synthesize aluminium bronze silicon carbide composite by powder metallurgy route. Three various weight percentages of silicon carbide (0, 2, 4 & 6) were reinforced with aluminium bronze matrix (Cu-20 %Al-4 %Ni). This compact was heated at two different temperatures such as 650 and 750 °C using tubular furnace. The effect of silicon carbide on density, sinterability, compression test and hardness test was analyzed. The scanning electron microscope and energy dispersive spectroscopy were used to confirm the presence of alloying elements. The results showed that the sinterability and density were reduced with an increase in silicon carbide content. There was significant improvement in densification of composites sintered at 750 °C relative to composites sintered at 650 °C. The maximum percentage of increase in density was 7.34 %, which was noticed for 4 % SiC reinforced aluminium bronze sintered at 750 °C. Aluminium bronze- 4 % SiC composite (750 °C) sintered exhibited maximum hardness of 32HRC. Among the composites sintered at 750 °C, 6 % SiC reinforced possesses lowest sinterability of 0.44.The composite reinforced with 6 %SiC exhibited lowest compressive strength among other composites. The 4 wt% SiC reinforced composites sintered at 750 °C has highest corrosion resistance.There was significant improvement in densification of composites sintered at 750 °C relative to composites sintered at 650 °C. The maximum percentage of increase in density was 7.34 %, which was noticed for 4 % SiC reinforced aluminium bronze sintered at 750 °C. Aluminium bronze- 4 % SiC composite (750 °C) sintered exhibited maximum hardness of 32HRC. Among the composites sintered at 750 °C, 6 % SiC reinforced possesses lowest sinterability of 0.44.The composite reinforced with 6 %SiC exhibited lowest compressive strength among other composites. The 4 wt% SiC reinforced composites sintered at 750 °C has highest corrosion resistance.

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.

Micro parts of various kinds are now in demand for fabricating high-tech products such as micro-electronic parts and MEMS applications. In order to supply these micro parts, the development of micromachining technology becomes increasingly important. Among the various stages of micromachining, the fabrication of holes and pins of precise nature are essential for many miniature components. Micro-hole drilling, is used in various precision industries, such as the production of automotive fuel injection nozzles, watch and camera parts to generate deeper holes with better straightness, better roundness and smoother surfaces. Electrical discharge machining (EDM) is a non-contact machining process for electrically conductive materials which can produce high aspect ratio holes, particularly useful in the tool making industry. The objective of the study on micro-EDM drilling process parameters is to analyse the effect of drilling parameters such as the feed rate (A), capacitance (B) and voltage (V) on machining stainless steel shim using a tungsten electrode of 300 µm diameter. The performance measures are surface roughness (Ra), metal removal rate and tool wear ratio. Response surface methodology has been used and experiments have been conducted using central composite design. Statistical models of the output responses have been developed using three level full factorial design of experiment. The developed models have been used for multi response optimization by desirability function approach to determine the optimum machining parameters. These optimal machining parameters are then validated experimentally, and it is observed that the response values are very close to the predicted values with less error.

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.

This research article deals with the first law of thermodynamic analysis on solar photovoltaic thermal hybrid system with three kinds of models namely model 1 (fully transverse obstacles), model 2 (partially transverse obstacles) and model 3 (longitudinal obstacles with baffles) experimentally. It was designed, fabricated and tested at the tropical climatic condition of Chennai. Two different mass flow rates of air (0.0071 kg/s and 0.0085 kg/s) have been used for enhancing the performance of the system. The performance of solar PVT hybrid system relies on solar radiation, physical geometry and mass flow rate of air. All the models were subjected to first law analyses which were compared with respect to local time and other parameters such as glazing temperature, outlet air temperature. The PVT hybrid system experimentally proved to enhance the thermal performances with an increasing air mass flow rate. The obtained experimental results are in good agreement with previous research findings.

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.

Chatter or self-excited vibration during machining process is always a serious problem which reduces intended dimensional accuracy. This investigation is mainly about reducing induced vibration through the electroless coating of nickel–phosphorus (Ni–P) on high carbon steel (EN 31) boring bar. The high carbon steel (EN31) substrate has been electroless coated with nickel–phosphorus at varying pH values (4, 5 and 6), and the damping effect of pH values on material deposition has been investigated. The coated substrate was subjected to heat treatment process and the influence of pH values and heat treatment process on structural stabilities of the coated specimens were analyzed using impact hammer test, and scanning electron microscope, energy-dispersive X-ray micro analysis and X-ray diffraction technique were used to study the structural changes in the Ni–P-coated boring bars. The pH values and heat treatment processes were found to play a significant role in improving the damping properties of the boring bars. The boring bar coated at 4 pH values resulted in better deposition of nickel and phosphorous over the substrate and offered better damping properties to the substrate. The heat treatment process on the coated specimen was found to render a crystalline structure to the substrate, thus offering improved damping proprieties to the materials.

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.

Coir fiber is available in abundance in nature. Coconut shell is a food waste material. Both are procured from the coconut. Coir fiber–reinforced coconut shell powder–based polyester resin composite is fabricated by using the hand lay-up technique. Here, coir fiber acts as a reinforcing agent. Coconut shell powder acts as a filler material. Polyester resin acts as a binding material. Composites are fabricated as per ASTM standards. Tests of mechanical behaviors such as tensile strength and compressive strength are carried out, and the results are analyzed based on the influence of coir fiber diameter and length.

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.

An investigation has been carried out to make use of coir, a natural fibre abundantly available in India. Coir-polyester composites were prepared and their mechanical and machinability characteristics were studied. The short coir-fibre-reinforced composites exhibited the tensile, flexural and impact strength of 16·1709 MPa, 29·2611 MPa and 46·1740 J/m, respectively. The regression equations were developed and optimized for studying drilling characteristics of coir-polyester composites using the Taguchi approach. A drill bit diameter of 6 mm, spindle speed of 600 rpm and feed rate of 0·3 mm/rev gave the minimum value of thrust force, torque and tool wear in drilling analysis.

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.

The interest in fibre-reinforced polymer composites is growing rapidly due to its high performance in terms of mechanical properties, significant processing advantages, excellent chemical resistance, low cost, and low density. The development of composite materials based on the reinforcement of two or more fibre types in a matrix leads to the production of hybrid composites. In the present work, woven coir–glass hybrid polyester composites were developed and their mechanical properties were evaluated for different stacking sequences. Scanning electron micrographs of fractured surfaces were used for a qualitative evaluation of interfacial properties of woven coir–glass hybrid polyester composites. These results indicated that coir–glass hybrid composites offered the merits of both natural and synthetic fibres.

The fiber that serves as a reinforcement in reinforced composites may be artificial or natural. Past studies show that artificial fibers such as glass, carbon, etc., have been generally used in fiber-reinforced composites. Although glass and other synthetic fiber-reinforced composites possess high specific strength, their fields of application are very limited because of their inherent higher cost of production and low biodegradability. In this study, woven coir and woven coir–glass fiber-reinforced polyester composites were developed and their mechanical properties were evaluated. Scanning electron micrographs of fractured surfaces were used for qualitative evaluations of the interfacial properties of woven coir and woven coir–glass hybrid polyester composites. The results indicated that the properties of woven coir composites can be considerably improved by incorporation of glass plies.

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.

The tensile, flexural, and impact properties of coir fiber/polyester composites were evaluated. The untreated green husk coir fibers with different proportion in length and content were used as reinforcements in polyester polymer-based matrices. The mathematical models of tensile, flexural, and impact properties were developed and optimized using statistical package to find the optimum fiber parameters for maximum mechanical properties. The results are shown that the fiber content in weight percentage is playing major role than the fiber length on the improvement of tensile, flexural, and impact strength properties.

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 this work, the variation of mechanical properties such as tensile, flexural, and impact strengths of roselle and sisal fibers hybrid polyester composite at dry and wet conditions were studied. The composites of roselle/sisal polyester-based hybrid composites with different weight% of fibers were prepared. Roselle and sisal fibers at a ratio of 1:1 had been incorporated in unsaturated polyester resin at various fiber lengths. When the fiber content and length of the roselle and sisal fibers were increased, the tensile and flexural strength of the composite increased. When the samples were subjected to moisture environment, decrease in tensile and flexural strength was observed. The maximum percentage of strength reductions in tensile and flexural strength were observed for the composites having the fiber length of 150 mm and 30 wt% fiber content. For impact strength, it was with the composites of 20 wt% and 150 mm at wet conditions compared to dry conditions. The percentage of strength reductions increased with fiber content and length in wet conditions. A scatter in the impact strength values was identified on both the conditions. The moisture absorption characteristics of the natural fibers are very important to produce the natural fiber hybrid composite materials with the positive hybrid effect. The experimental results are compared with theoretical and empirical or statistical results and found to be in good agreement.

Nickel-diamond composite coatings are produced by electro deposition using sedimentation techniques on mild steel substrate at various cathode current density, pH and temperature. Electro deposition was carried out from a conventional Watts bath. Natural diamond powder of 6-12 mm size was used in the study. The volume percent incorporation of diamond on the coated specimens was measured gravimetrically. Artificial Neural Network (ANN) and regression models (a mathematical model) were used to predict the volume percent incorporation of diamond in the Ni-diamond metal matrix. In this work, Volume fraction of diamond deposition (Vfd) was taken as response variable (output variable) and current density, pH and temperature were taken as input variables. The prediction of response variable was obtained with the help of empirical relation between the response variable and input variables using ANN and also through DOE. The predicted values of the responses by ANN and regression models were compared with the experimental values and their closeness with the experimental values was determined.

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 .