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The heating value of coal is necessary for its usage in power generating. This is tested experimentally, although theoretical models have been used to forecast the heating value; either lower heating value or greater heating value. The same approach has been used to biomass, which is being promoted as an alternative for coal or as a co-fired fuel. Biofuel production was seen as a significant countermeasure for lowering anthropogenic CO_2 emissions, removing the worsening greenhouse effect in the atmosphere, and slowing global warming. Thermochemical conversion is thought to be the most effective method of generating biofuels from biomass, with the least potential for global warming. The torrefied biomass is used for making biomass material into a useful energy and it also enhance the property of that material by torrified it.

After World War II, the COVID-19 pandemic has caused so much losses to humankind that some countries have started calling it a third world war or biological war, which has been imposed for reorganization and dominance over the world economy. The whole world has come to stand still and efforts of all the countries are concentrated to avoid extinction of human species. Most of the companies have come out with zero production in the month of April 2020. This paper explores the impact of this seismically disruptive environmental event on the global environment and energy scenario. It provides a valuable guide to researchers, environmentalists, and policy makers to provide a linkage between COVID-19, and various environmental concerns and remedial measures have also been suggested to combat SARS-CoV-2. Although the future trajectory of the pandemic remains uncertain, the pandemic has already brought many changes to how people live, and is likely to bring many more. As all the disasters have their time limits, one should be optimistic and must put all our feet together to win over this pandemic and respect the ecosystem before it is too late.

Bearings, as a key part of rotating machinery, are prone to failure due to fatigue and aging resulting from their long-term and high-load operation. To ensure stability of the mechanical equipment, monitoring bearing health is helpful to guarantee smooth operation of machinery and increasing machinery availability. This article puts forward an intelligent non-invasive thermal images-based fault diagnostic approach to periodically monitor condition of the rolling contact bearings in respect of their deterioration due to defects on the inner race, outer race and balls/rollers. Thermal images of four bearing conditions, including one healthy and three faulty states, have been considered followed by a performance classification based comparative analysis using Support Vector Machines (SVM) and Convolutional Neural Network (CNN). The CNN consists of many tools under its cap but for this work, the AlexNet architecture is used which has proved to be more effective than SVM. The experimental findings reveal that non-contact infrared thermography has enormous potential for automatically identifying problems and detecting early warning, regardless of speed, resulting in negligible shutdowns of the system due to bearing failure.

Bearings are considered as indispensable and critical components of mechanical equipment, which supports the basic forces and dynamic loads. Bearing faults can results in catastrophic failure of equipment, specially aircraft, automobile, turbine and other rotary components. An efficient and fault free performance of bearings is required because of continuous nature of operation. Any incipient failure if not detected well in time can be a costly affair which may cause significant financial losses. Vibration, temperature and acoustic signals have been used for machinery health monitoring, vibration signature being the most widely used parameter. This article overviews the vibration signals based studies conducted for monitoring the health of the rotating machinery and will be useful for practising engineers and researchers working in the field of rotary machinery.

Combustion experts are in search of some alternative fuel from last few decades owing to diminishing petroleum products and unexpected variations in habitat, which are result of venomous emissions from the CI engines. The present investigation intended to assess the performance and emission parameters of a diesel engine by fueling it with pilot fuel (blends of diesel and n-butanol) and primary fuel (Biogas). Results revealed that BTE, HC and CO increases whilst NOx and smoke emissions were reduced by using the pilot and primary fuel together in relation with natural diesel. Experimentation was done using Taguchi L9 orthogonal array design. The engine load, flow rate of biogas and butanol in fuel blend percentage were selected as input parameters whereas brake thermal efficiency (BTE) and emission characteristics i.e., HC, CO, NOx and smoke were chosen as response variables. ANOVA was carried out for the responses by utilizing MINITAB software. The higher value of raw data and S/N ratio for BTE was noted with high engine load, low flow rate of biogas and butanol blend percent. For the emission characteristics i.e., HC, CO and smoke, lower raw data and high S/N ratio values were attained in the order of rank engine load > butanol blend percent > biogas flow rate while the similar values for NOx were attained in the rank engine load > biogas flow rate > butanol blend percent. Taguchi design was noted to be an effective tool for the optimization of various response parameters and the optimum levels of input parameters were calculated after analysis. Full engine load for BTE and HC, Biogas flow rate of 15 lpm for BTE, HC and CO, and 20 % of butanol blend for HC, CO and smoke were found to be the optimum conditions for the conducted experimentation. © 2022 Elsevier Ltd

Circular journal bearings constitute the heart of power generation equipments due to their simple design, ease in manufacturing and high load-carrying capacity. Hydrodynamic action in these bearings is manifested by the presence of a single oil film, which increases the oil film temperature at higher loads. However, in order to improve the performance of bearings and for overall optimization of equipment efficiency, lubricants with specialized additives are being explored. With the advancements in nanotechnology, lubricants have been blended with nanoparticles for enhancing tribological characteristics. The existing studies are more focused on examining the influence of nanolubricants for boundary lubrication regime. Hence, this article investigates the effect of CuO and TiO2 based nanolubricants in hydrodynamic lubrication regime by studying the static thermal characteristics of circular bearing. Comparative performance analysis has been conducted with both the nanoparticles based lubricants at different concentrations by mixing them in two mineral-based oils having different viscosities. The modified Krieger Dougherty method has been applied for determining the viscosity of nanolubricants. Hydrodynamic pressure and oil film temperature calculations have been performed by developing a model in MATLAB®R2020a software by considering the effect of variation in the thermophysical properties of nanolubricants. There has been a significant increase in load capacity with just small rise in oil film temperature with the use of nanolubricants. Load capacity with 2wt% addition of TiO2 increased by 14.23% at a journal speed of 2000rpm and eccentricity ratio of 0.6, while there was an increase of 9.23% in the case of CuO at the same parameters. The proposed methodology has an enormous potential for assessing the performance enhancement of turbomachinery used for power generation.

Condition-based maintenance is always an important strategy of maintenance to prolong the effective life of rotating machines as they run on high speeds with a variety of loads in some cases under severe conditions. If the monitoring of the current condition is not done accurately then rotating machines such as turbines, engine, bearing, shafts, gearbox, motors, and compressors leads to catastrophic failures with some serious consequences on the rate of production, safety, loss of manpower and sudden increase in repairing cost. Condition-based maintenance is also be called predictive type maintenance is a far superior technique as compared to preventive maintenance and run-to-break maintenance. In predictive maintenance current status of the machine while in operation, being monitored carefully and based on a brief analysis of the future condition of the machine predicted. This paper going to review the various intelligent condition monitoring techniques developed or used by various researchers to monitor the health of rotating machines and able to predict the faults at the earliest time.

Robotic Process Automation (RPA) technology is used to automate tasks and create software bots. Actually, RPA is that technology that mimics the actions of humans step by step to automate the entire process. There are several tools for RPA such as Blue prism, Automation Anywhere, Uipath, and Microsoft Power Automation. These tools are used to identify the end-to-end business process and need to create the code that will describe the business process and run the commands. Presently, the Automation process is used in several fields such as accounting and finance, in travel bookings, Traveling payment refunds, managing and resolving the complaints of the customers, hotel inventories, etc. But this paper is based on managing the customer complaints that are faced during their travel time with the use of the Automation process. Process of Automation is very well nowadays, to do everything very quickly and with the better way.

Bearings are considered as indispensable and critical components of mechanical equipment, which support the basic forces and dynamic loads. Across different condition monitoring (CM) techniques, infrared thermography (IRT) has gained the limelight due to its noncontact nature, high accuracy, and reliability. This article presents the use of IRT for the bearing fault diagnosis. A two-dimensional discrete wavelet transform (2D-DWT) has been applied for the decomposition of the thermal image. Principal component analysis (PCA) has been used for the reduction of dimensionality of extracted features, and thereafter the most relevant features are accomplished. Furthermore, support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighbor (KNN) as the classifiers were considered for classification of faults and performance assessment. The results reveal that the SVM outperformed LDA as well as KNN. Noncontact condition monitoring shows a great potential to be implemented in determining the health of machine. The utilization of noncontact thermal imaging-based instruments has enormous potential in anticipating the maintenance and increased machine availability.

Condition monitoring is a significant requirement for ensuring safe and reliable working of machining processes and rotary components. Recent developments in digital signal processing techniques along with emergence of miniature sensors and high-speed data acquisition devices furnish a peculiar opportunity for the development and implementation of effective, in-situ, non-intrusive condition monitoring methods for a broad range of machining processes and rotary components. The selection of most appropriate signal processing technique, best suited for a particular application, is a major challenge in the field of condition monitoring, especially when working in a competitive industrial environment. This problem can only be solved if one has a thorough understanding of various aspects such as which parameter to be monitored, aim of monitoring, processing limitations and possible future scope of such monitoring method. Signal processing methods applicable to machining processes and rotary components have been investigated in relation to the parameters monitored, purpose of monitoring and future scope for that method. Limitations of such processing methods have also been reviewed to make the reader aware of disadvantages in using a particular signal processing method. This paper is intended as a valuable guide for researchers to assist in identification and application of the best possible condition monitoring method for machining processes and rotary components.

Bearing is one of the most crucial parts in induction motor (IM) as a result there is a constant call for effective diagnosis of bearing faults for reliable operation. Infrared thermography (IRT) is appreciably used as a non-destructive and non-contact method to detect the bearing defects in a rotary machine. However, its performance is limited by insignificant information and string noise present in the infrared thermal image. To address this issue, an emergent two dimensional discrete wavelet transform (2D-DWT) based IRT method has been proposed in this article for diagnosing the different bearing faults in IM, namely, inner and outer race defects, and lack of lubrication. The dimensionality of the extracted features was reduced using principal component analysis (PCA) and thereafter the selected features were ranked in the order of most relevant features using the Mahalanobis distance (MD) method to achieve the optimal feature set. Finally these selected features have been passed to the complex decision tree (CDT), linear discriminant analysis (LDA) and support vector machine (SVM) for fault classification and performance evaluation. The classification results reveal that the SVM outperformed CDT and LDA. The proposed strategy can be used for self-adaptive recognition of bearing faults in IM which helps to avoid the unplanned and unwanted system shutdowns due to the bearing failure.

This article addresses automatic license plate recognizer as a key element of today’s fast trending approaches used for smart city development. The deployment of heavy manual forces is the biggest challenge in the path of efficient execution of transportation rules in metro cities. In order to help the government in achieving this milestone, a thorough research helped in developing a product which could very effectively and efficiently be used in the implementation of odd/even transport rule coined by the NCT Government, Delhi. Automatic license plate recognition has become the latest trend in the management of contemporary urban and national street arrangements, and this latest trend has been blended with programming in python to enable a handy product for the government to easily enable them to distinguish between the faulty and genuine vehicles without much need of manual forces. This article throws light on the techniques used for making this product and the literature survey done on the previously existing techniques.

Moisture plays a vital role in several aspects of storage, drying, processing of grains and to understand the properties of grains. In general, the moisture content in grains can be measured by direct and indirect methods. Schematically, the former includes gravimetric method whereas the latter comprises resistance, capacitance-based measurements. Also, the direct method has been proved to be more time-consuming in comparison to the indirect one. Apart from these methods, calorimetry based measurements, the Karl Fischer method, is restricted to their laboratory research purpose and is not being used in industries for real-time measurement. In this article, a new capacitance-based method for grain moisture measurement has been developed which relies upon the integration of IC555 timer. The experimental findings revealed that with an increase in mean frequency value from 343310.04 to 355271.16, the capacitance values dropped from 2.03446369 to 1.9678978 respectively and these results have proved to be economical and reliable in terms of providing a promising relationship between the grain moisture content and capacitance of the circuit in further investigation. The effectiveness of the developed method was corroborated by comparing the results with the resistance measurement method.

In industrial applications, various types of materials are used and strength of weldment of two alloys is a major cause of interest among engineering community. In this study P11 and P22 steels were welded using ER −70 - S6 wire electrode with metal inert gas (MIG) welding process using CO2 as shielding gas. The strength of the weldment was tested by impact testing, Hardness by Micro Vicker hardness and Microstructure of weldment before and after the failure was examined using a metallurgical microscope. The results of experiments performed on the specimen on impact testing machine showed that the impact energy of welded specimen was 49 J in Charpy impact test, whereas, that of parent metal P11 was 48 J and for P22, it was 64 J. The result of impact test shows that the impact energy of weldment of P11 and P22 lies between the impact energy of parent alloys. The microhardness at Heat affected zone (HAZ) was highest and at base metal, it was lowest whereas, at weldment microhardness value lies in between microhardness value of base metal and HAZ. Specimen were heat treated in a furnace in an air environment at 700, 800 and 900 °C for 2 h and subsequently cooled in the furnace. The results of impact test show increased strength of weldment after post weld heat treatment (PWHT) at all temperatures however, the strength was found to decrease with increase in temperature of PWHTs.

Sensor technology is the backbone of diverse engineering and biomedical applications that has attracted the attention of academicians and researchers. Design and development of an efficient sensor requires a material that can monitor large structural changes in response to a small applied input with high degree of precision and consistency. Graphene is a unique two-dimensional material with its spectacular structural and electronic properties that has appealed towards the emergence of sensors of new era. This paper presents the state-of-the-art review on scope and development of graphene based strain, gas, magnetic, piezoelectric, photoelectric and biosensors for chemical, mechanical, electrochemical and biomedical applications. A detailed survey of previously published studies has been conducted as a valuable tool for researchers to select the appropriate graphene based material(s) and substrates for further exploration in the field of sensors. Despite the exemplary performance of graphene based sensors, there is a dire need to exploit the high intrinsic mobilities of graphene with improved substrates for diverse applications.

Magnetorheological finishing is highly developed unconventional machining process used for nanometer range surface finishing with the help of magnetic abrasive particles. The cutting forces are generated and controlled by the magnetic field in this process, which is developed between the electromagnetic poles and magnetic abrasives. Excellent process capabilities were attained by this technology. But one of the major limitation or drawbacks is in the wide utilization of the process in industries. This is due to the huge cost of magnetic abrasives, which act as cutting tool in these processes. Very few studies have been performed in the direction of development of new or alternative magnetic abrasives. The present research mainly focuses on the development of magnetic abrasives and performance of these magnetic abrasives. This investigation aims to develop magnetic abrasives by different techniques i.e. Simple Mixing, Adhesive bonding, Microwave sintering and Mechanical alloying, then investigating the performance on brass work piece with SiC abrasives and to find the best technique. The surface topography was done through the x-ray diffraction (XRD) and scanning electron microscope (SEM) to analyze the surface obtained after finishing. The Mechanical Alloying is the best technique concluded through this work to prepare the magnetic abrasives, which further gives the best results in terms of Material removal rate (MRR) and percentage improvement in surface finish (PISF) upon the internal surface of hollow cylindrical brass pipes.

A programmable logical control based Smart Machine Controller (SMC) has been developed to control the EDM process. SMC extracts the EDM process variables for rough, semi-finish and finish machining operations from the developed database. The experimental database was created by conducting pilot experimentations after conducting the validation experiments, the SMC was tested on six different dies and mold steel materials with original machine controller and the results show that the MRR and surface finish improved significantly when used with SMC machining parameters. The developed SMC is very effective and highly interactive when compared to the original die-sinking EDM machine controller.

The present study focusses to enhance the wear resistance of electroless Ni-P coatings with the co-deposition of selflubricating nano graphite particles synthesized by mechanical milling using a high energy planetary ball mill. The coatings were developed on an aluminum substrate and nano graphite particles were co-deposited (4 g/l) into the Ni-P matrix using alkaline hypophosphite reduced electroless bath. Changes in properties after heat treatment at 220 °C for 4 h in vacuum have been correlated with morphological and microstructural changes shown by field emission scanning electron microscopy (FESEM) and X-ray diffraction (XRD) respectively. Energy dispersive analysis of X-ray (EDAX) determined the quality and elemental composition of coatings. Wear resistance, coefficient of friction (pin on disc) and microhardness of electroless Ni-P-graphite nanocomposite coatings were calculated and contrasted with Ni-P coatings. The outcomes reveal that the size of synthesized graphite nanoparticles is 20 nm. Microhardness and wear resistance of electroless coatings have improved after heat treatment. Excellent wear resistance has been observed at the cost of some hardness in Ni-P-graphite nanocomposite coatings over Ni-P coatings.

Timely detection of faults in bearings can save time, efforts and maintenance costs of rotating equipments. To avoid the physical connection of vibration pickup to the machine tool, a non-contact type vibration pickup has been designed and developed in this study to acquire the vibration data for bearing health monitoring under load and speed variation. Fault diagnosis has been accomplished using a Hilbert transform for denoising the signal. The dimensionality of the extracted features was reduced using Principal Component Analysis (PCA) and thereafter the selected features were ranked in order of relevance using the Sequential Floating Forward Selection (SFFS) method for reducing the number of input features and finding the most optimal feature set. Finally, these selected features have been passed to Support Vector Machines (SVM) and Artificial Neural Networks (ANN) for identifying and further classifying the various bearing defects. A comparative analysis of the effectiveness of SVM and ANN has been carried out. The results reveal that the vibration signatures obtained from developed non-contact sensor (NCS) compare well with the accelerometer data obtained under the same conditions. Classification accuracy achieved by the developed NCS with other sensors reported in the literature compares very well. The proposed strategy can be used for automatic recognition of machine faults which will help in providing early warnings to avoid unwanted and unplanned system shutdowns due to failure of the bearings.

In this paper, a comparative study was conducted to observe the flow patterns of bulk powder materials through a conical shaped hopper. Hoppers were designed based on Jenike charts and further analyzed using discrete element method. The paper focuses on the flow of micro-sized powder materials and how their area flow pattern changes along with velocity distribution variation with a change in hopper angle. Discrete element method was used to predict the flow patterns for different hopper angles and to study the transition of flow from funnel flow to mass flow. A comparison was made among these hoppers based on their average velocity and average mass flow rate to have an optimum design for mass flow. It was observed that a decrease in hopper angle resulted in an increase in both the mass flow rate and average velocity.

Microwave joining is a new field of research that needs to be explored and standardized to make it commercially viable. In this work, effects of process parameters are analyzed during joining of Inconel-625 through microwave hybrid heating. Experiments are conducted inside a microwave applicator under varied experimental conditions. Effects of variation in process parameters are analyzed to see their effect on melting of the joint interface. Effect of four input parameters (size of drilled hole of vertical charcoal feeder, thickness of graphite sheet, thickness of insulation brick, surface contact between refractory and insulation brick) on the melting region have been investigated during this work. Results of this work show the correct melting of the joint zone, absence of defects such as crater formation and no spreading of the charcoal powder on specimen.

Journal bearings play a significant role to increase overall level of reliability in machines. Advancements in lubrication methodology have a great impact on the performance of journal bearings which operate under multifarious parameters and lubrication conditions. Bearing behaviour is monitored by bringing out changes in various aspects like geometry, misalignment value, restrictor design parameter, restrictor types, eccentricity, roughness, aspect ratio, lubricant feeding conditions and lubricating oil properties. Performance of bearings is tremendously dependent on the variation in lubricant properties. This paper presents the state-of-the-art review on the behaviour of lubricants, namely, micropolar, power-law fluids and couple stress lubricants and their effects on journal bearings efficiency. An effort is made to understand the behaviour of different non-Newtonian lubricants on various journal bearing configurations and their comparison with Newtonian lubricants so as to choose the appropriate lubricant for specific bearing configuration. Non- Newtonian lubricants have shown improvements in journal bearing performance when compared with Newtonian lubricants operating at selected conditions.

Graphene (GR) resembles as a planar sheet having thickness equivalent to almost one atom of carbon in which carbon atoms are closely packed representing a honeycomb lattice. In the domain of material science and condensed matter, graphene is a fast growing star with good potential for real time application in diverse scientific and technical fields. The documented characteristics and scope of this unique two-dimensional structure opened new possibilities for wide range of devices and systems. Although GR has been established as well-known electronic material, there has been less exploration of the synthesis of single sheet GR. This state of art is an attempt to have overview of recent research in field of GR related to its synthesis and scope in multifarious applications in context to electronics and energy in general and related to field emission and sensors in particular. The scope of graphene nanoparticles in tribology and industrial automation has also been explored. Where applicable, the weaknesses of the existing knowledge base and future directions for study have also been emphasised.

Condition monitoring and fault diagnosis of working machines have gained significant attention due to their prospective benefits, such as enhanced productivity, decreased repair and maintenance costs and enhanced machine operation. In this paper, a thermal image based non-contact methodology has been proposed to diagnose the gear faults using support vector machines (SVM). The thermal images acquired from gearbox simulator were preprocessed using 2D-discrete wavelet transform to decompose the thermal images. The relevant features were extracted from converted thermal gray-scaled images followed by selecting the strongest feature using Mahalanobis distance criteria. Finally, the selected features were given to a SVM classifier for classifying the different gear faults. The experimental findings indicate that fault diagnosis using thermography for rotary machinery can be put into practice to industrial fields as a new smart fault diagnostic method with excellent prediction performance.

Careful selection and location of sensors are of key importance in the construction and implementation of an effective condition monitoring system. There is a continual call for depletion of maintenance and operational costs of rotating machine elements. Health monitoring of bearings, which is regarded as a crucial component of rotating machinery, is expected to facilitate in the preclusion of machine breakdown and ensure the machine availability by maintenance actions in time. The effectiveness and reliability of measurement approaches for monitoring the condition of the bearing are influenced by both the signal processing techniques and locations of the sensors selected for fault characteristic extraction. This paper proposes a low cost Non-contact optimal sensor placement (NC-OSP) methodology in order to get information of high quality related to the dynamic features of the machines. Experiments were conducted using different bearing conditions under different operating conditions and sensor placement positions. Mathematical models have been proposed based on the response parameters and input variables experimentation using response surface methodology. Results indicate that the most effective input variable to control the response parameters viz. FFT vibration amplitude of characteristic frequency and RMS value of time domain was the shaft speed followed by load and angle of incidence. The responses proposed by the optimal models have been observed at 8° angle of incidence with maximum load and speed. The optimal outcomes have been validated experimentally which proved that the predicted results were in sync with the actual ones. The proposed work has significant potential in industrial environment with complex systems, where the condition monitoring approach with proper placement of sensors can play an important role. Keywords: Condition monitoring (CM), Vibration signatures, Response surface methodology (RSM), Non-contact optimal sensor placement (NC-OSP), Non-contact vibration sensor

Wire ropes are widely used in civil, mechanical and mining engineering. Safety of human life while using wire rope is always been a matter of great concern. Consequently, an efficient and safe performance of wire rope has always been an area of research. Fretting behaviour under repeated cyclic load in practical application is key player in performance of wire rope. Fretting behaviour is affected by a number of parameters viz. wire rope material, its tensile strength, lubrication and coating material and environmental factors like corrosive medium, operating temperature etc. There is a huge potential for further research in the area of fretting behaviour analysis. This paper presents a state-of-the-art review of key variables, their effect as well as remedial solution of fretting behaviour of wire rope.

The global energy situation requires the efficient use of resources and the development of new materials and processes for meeting current energy demand. Traditional materials have been explored to large extent for use in energy saving and storage devices. Graphene, being a path-breaking discovery of the present era, has become one of the most-researched materials due to its fascinating properties, such as high tensile strength, half-integer quantum Hall effect and excellent electrical/thermal conductivity. This paper presents an in-depth review on the exploration of deploying diverse derivatives and morphologies of graphene in various energy-saving and environmentally friendly applications. Use of graphene in lubricants has resulted in improvements to anti-wear characteristics and reduced frictional losses. This comprehensive survey facilitates the researchers in selecting the appropriate graphene derivative(s) and their compatibility with various materials to fabricate high-performance composites for usage in solar cells, fuel cells, supercapacitor applications, rechargeable batteries and automotive sectors.

The recommender system is improving with the increase in the information obtained from numerous application domains. Recommendation or the prediction of an item depends on the rating and review given by an individual or a group of customers. New user information can also be predicted using the searching history or the profile information of the customer. In Travel Recommender System, the locations of interest are figured out based on the activities carried out by the user or the preference of that particular user. It also helps in exploring the diverse geographical areas of interest of the user. The increasing demands of this system enhances the scope in development of user behaviour that is based on recommendation approaches. It also effectively deals with the sparsity problem by searching through a large amount of data to provide users with individual contents and services. This article explores the various aspects and potentiality of existing approaches in the Travel Recommender System based on user profiles for future research directions and recommendation framework.

Accelerometers, used as vibration pickups in machine health monitoring systems, need physical connection to the machine tool through cables, complicating physical systems. A non-contact laser based vibration sensor has been developed and used for bearing health monitoring in this article. The vibration data have been acquired under speed and load variation. Hilbert transform (HT) has been applied for denoising the vibration signal. An extraction of condition monitoring indicators from both raw and envelope signals has been made, and the dimensionality of these extracted indicators was deducted with principal component analysis (PCA). Sequential floating forward selection (SFFS) method has been implemented for ranking the selected indicators in order of significance for reduction in the input vector size and for finalizing the most optimal indicator set. Finally, the selected indicators are passed to k-nearest neighbor (kNN) and weighted kNN (WkNN) for diagnosing the bearing defects. The comparative analysis of the effectiveness of kNN and WkNN has been executed. It is evident from the experimental results that the vibration signals obtained from developed non-contact sensor compare adequately with the accelerometer data obtained under similar conditions. The performance of WkNN has been found to be slower compared to kNN. The proposed fault detection methodology compares very well with the other reported methods in the literature. The non-contact fault detection methodology has an enormous potential for automatic recognition of defects in the machine, which can provide early signals to avoid catastrophic failure and unplanned equipment shutdowns.

The economic crisis brought an unprecedented attention to the requirement of energy conservation for sustainability in the developed world. Sustainability or sustainable development is a major issue in triple bottom line (TBL) for any enterprise, economics, social and environmental and associated aspects including various costs. Optimization of costs plays an important role in sustainable development. The use of mathematical optimization techniques in cost aspects of sustainability will further add efficiency in sustainability. In this paper, mathematical formulation of sustainability has been discussed in terms of cost minimization and maximization of profit objectives in the form of multiple objective programming model along with solution of the problem by elementary optimization technique (weighted aggregated method). The aim of this paper is to present a unique optimization model on sustainability associated costs with multiobjective programming approach. The proposed model has also been illustrated with real problem of manufacturing industry in order to prove its applicability in this domain.

This paper aims to optimize the wire electric discharge machining (WEDM) parameters using multi response surface methodology. Experiments were performed on Nimonic 80A alloy to improve the response variables, namely material removal rate (MRR) and Kerf width (Kw). SEM analysis was carried out on the different samples obtained using different levels of input input parameters, namely, peak current, gap voltage, duty cycle and wire speed to evaluate the quality of machined surface. The influence of these parameters on MRR and Kw were determined using ANOVA. The analysis results revealed that the peak current is the main factor affecting MRR and Kw. The optimized WEDM parameters obtained using multi response function of RSM technique for machining of Nimonic 80A alloy are peak current (57A), gap voltage (90 V), duty cycle (75 µs), and wire speed (7 mm/min) of wire electrode. The characterization results show that high discharge energy results in deep craters with micro craters at low energy levels on the machined surface of the specimen. The results obtained using the proposed methodology would provide sufficient data for enhancing the machining of Nimonic 80A alloy on WEDM.

In this paper, a model based on adaptive neuro-fuzzy inference system (ANFIS) is developed to predict the behavior of a natural thermal convection system. ANFIS model is able to successfully imitate the effects of variation in input parameters such as current and voltage on the response parameters such as temperature at different locations of the thermal system. The results obtained with the help of the developed ANFIS model are compared with the findings of the experiments in the form of graphs and also numerically by determining the error norms. Comparison of the ANFIS model-based predictions with the actual experimental results shows that the proposed model is able to identify the behavioral characteristics of the natural convection thermal system very accurately. The outcome of the proposed model is found out to be the best for the prediction of temperature at the location of fourth temperature sensor where the level of temperature is low. The proposed ANFIS model can be used further to develop a control system in order to control the temperature at different locations of the thermal system by varying the current and voltage parameters.

In this work, electromagnetic microwave energy radiations at 2.45 GHz frequency have been utilized to produce 5 mm thick joints of SS304-SS316. Filler powder mixture of fine nickel powder and epoxy blumer 1450XX was taken as interface slurry between the mating faces of the joints. Nano and micro particle sized nickel powder of 70 nm (powder 1), 20 µm (powder 2) and 50 µm (powder 3) were used during three sets of experiments. Activated charcoal powder was utilized as susceptor medium to initiate the microwave heating process. Scanning electron microscopy results showed that the joints were crack free and homogenous. Elemental spectroscopy was carried out to observe and compare the presence of different elemental composition in the joining region with different filler powders.

The ubiquity of artificial intelligence in the manufacturing domain draws inspiration for the present article. The implementation of a neural network technique is still a difficult and time-consuming effort for the industry. Prediction of machining variables is a considerable issue that needs to be explored for preventive maintenance of the machine structure and to optimize the surface quality. This work aims at predicting response parameters of the dry turning process for Inconel 825 alloy using deep-cryogenic treated tungsten-carbide insert through artificial neural network technique. Process parameters considered in this work were cutting speed, feed and depth of cut, whereas, surface-roughness, tool-wear, and material-removal-rate were taken as the three response parameters.14 types of training functions were compared based upon their error indices searching for the training function which best suits this work. Artificial Neural Network (ANN) model was developed by taking Bayesian regularization back propagation based training function. The response values predicted by the ANN were in very close approximation to the actual experimental value with the mean square error of only 0.0011 μm2, 39.0882 μm2 and 0.0520 cm6/min2in the prediction of surface-roughness, tool-wear, and material-removal-rate of dry turning process of Inconel 825 using treated carbide tool.

There is a constant call for reduction of operational and maintenance costs of induction motors (IMs). These costs can be significantly reduced if the health of the system is monitored regularly. This allows for early detection of the degeneration of the motor health, alleviating a proactive response, minimizing unscheduled downtime, and unexpected breakdowns. The condition based monitoring has become an important task for engineers and researchers mainly in industrial applications such as railways, oil extracting mills, industrial drives, agriculture, mining industry etc. Owing to the demand and influence of condition monitoring and fault diagnosis in IMs and keeping in mind the prerequisite for future research, this paper presents the state of the art review describing different type of IM faults and their diagnostic schemes. Several monitoring techniques available for fault diagnosis of IM have been identified and represented. The utilization of non-invasive techniques for data acquisition in automatic timely scheduling of the maintenance and predicting failure aspects of dynamic machines holds a great scope in future.