Optimization of Production and Industrial Systems

In the competitive world, most organizations adopt modern tools and techniques for improving their productivity without hammering other business objectives. However, in developing countries like India, which are labor intensive at the same time industry faces a paucity of skilled personnel. In order to achieve success and be profitable, MSMEs are greatly dependent on skilled operators. This study was conducted at a medium-scale special purpose machine manufacturing enterprise for operator's skill enhancement. The objective of the proposed research is to model Training Within Industry–Job Instruction (TWI-JI) management approach to empower the skills among the operators and assess them using the skill matrix and job breakdown sheet (JBS). The welding operators’ skill data has been collected and mapped for different types of welding and assembly operations. Pre-post analysis is performed using certain rating parameters to assess operators’ proficiency for TWI-JI. The implementation of TWI-JI resulted in an overall improvement of the average skill level of the welding section by 14.92%, average operator-wise skill improvement by 14.96%, and an average increase in production output is 20.12%.

Grid-connected photovoltaic (PV) systems convert sunlight into usable electricity for a building, feeding excess energy back into the grid for others to use. The system includes solar panels that generate DC power, which is converted to AC power via an inverter to power the building's electrical needs. If the building's demand exceeds the solar panels' output, the grid provides the extra power. Grid-connected PV systems support the transition to sustainable low-carbon transportation by enabling convenient charging of electric vehicles and promoting their adoption, reducing dependence on fossil fuels and emissions.

The ongoing revolution in the Industry 4.0 (I4.0) environment has spurred substantial advancements for numerous businesses and has amplified the need for small and medium enterprises (SMEs) to undergo digital transformation. The present study aims to explore and analyse the various I4.0 implementation enablers in SMEs. The authors employed ISM method to analyse the interrelationships among the variables under consideration. Subsequently, a MICMAC analysis is performed to group the variables into four categories: drivers, dependents, autonomous, and linkages. In our investigation, it was determined that factors such as data security, customer-centric innovativeness, and implementation cost served as driving variables. Meanwhile, government support, top management commitment, and availability of skilled employees were found to act as driving variables. ISM model results rely on expert assessments and could suffer from input bias, affecting results validity. The study could be improved by a comprehensive examination of variables and validated with structural equation modelling techniques. A comprehensive examination of the interconnections among the enabling factors can assist managers in facilitating a I4.0 implementation. The study presents a composite methodology for analysing I4.0 enablers, including government support, top management commitment, skilled employees, IT infrastructure, and R&D.

This study explores the integration of Lean and Six Sigma quality techniques in Industry 4.0, focusing on barriers and enablers. Industry 4.0 has transformed manufacturing through digitalization, automation, and data analytics. However, effective integration of quality improvement methodologies is crucial for fully harnessing its potential. The review provides an overview of Lean and Six Sigma, highlighting their strengths in driving operational excellence. It discusses challenges faced in integrating these techniques, including technological complexities, organizational culture, skill gaps and resistance to change. A framework for strategic integration is presented, encompassing leadership commitment, employee engagement, collaboration, and advanced technologies. Practical strategies and best practices for overcoming barriers and leveraging enablers are discussed, offering actionable insights. Embracing the strategic integration of Lean and Six Sigma in Industry 4.0 enhances operational efficiency, reduces defects, and improves customer satisfaction. This manuscript serves as a valuable resource for managers, practitioners and researchers navigating the confluence of barriers and enablers in achieving quality excellence in the digital age.

The integration of machine learning (ML) into supply chain management has led to a shift towards Industry 4.0 (I4.0) practices. These practices have the potential to facilitate sustainable business practices through the implementation of a green supply chain (GSC). The adoption of ML-based supply chain strategies has been shown to enhance decision-making, productivity, and system performance at the organizational level. Despite these benefits, research into sustainable supply chains utilizing I4.0 technologies is limited. To address this gap, a systematic literature review was conducted to examine the progress and potential of Industry 4.0 technologies in achieving a greener supply chain. The study thoroughly discussed the role and impact of various I4.0 technologies on the GSC. The findings of this review provide new research directions and scopes for I4.0 research areas that can be beneficial for both academia and industry. These include environmental and operational challenges, as well as the need for skilled professionals to manage and operate these technologies. This study highlights the potential benefits of I4.0 technologies in facilitating sustainable supply chains and provides insight into the challenges and limitations of implementing these technologies. The review also emphasizes the need for further research and development to maximize the potential of I4.0 in achieving a GSC.

The modern business environment is characterized by increasing pressure to enhance sustainability in supply chain (SC) operations. The implementation of Industry 4.0 (I4.0) technologies and the growing prominence of additive manufacturing (AM) has provided organizations with new opportunities to reduce waste and increase efficiency. Few studies explain the potential benefits of enhancing SC sustainability through I4.0 and AM technologies through bibliometric-based review. This bibliometric-based review aims to provide a comprehensive overview of the current state of research in the area of enhancing SC sustainability through I4.0 and additive manufacturing technologies. The review will explore the current literature, identify the most frequently cited authors, organizations, and journals, and discuss the key findings and recommendations for future research. The objective of this review is to provide valuable insights for organizations and researchers looking to understand the impact of I4.0 and additive manufacturing on SC sustainability. The study will examine the challenges and limitations of implementing these technologies in real-world supply chain operations and discuss the potential benefits and outcomes of such implementations. The review will also provide a roadmap for future research in this area, helping to guide further investigation into the impact of these technologies on SC sustainability.

Production capacity is not always used to its full potential due to various bottlenecks arising within the production line. Identifying and rectifying the bottlenecks can thus facilitate an optimal production output, which will eventually maximize the total profit. We identified two important bottlenecks within the production line by the application of the theory of constraints (TOC). The first bottleneck was associated with the machine capacity, causing a mismatch in demand and supply, and this constraint has been elevated by a financial investment decision. The TOC methodology is again applied to find and solve the next bottleneck. The random job-machine assignment was found to be the second bottleneck. A potential decrease in the makespan has been observed by applying two mixed integer linear programming models (MILP models). Job is not splittable in model 1, whereas job is splittable in model 2, and the setup time is independent of any sequences. The MILP model is executed using Python. The results will give the job-machine assignment which potentially marked a reduction in the makespan. The results are then compared with the original makespan. Potential reduction in makespan will meet the demand in time as well as the industry can now manufacture more products within the given time. The production operation has been improved compared to the current operation by a reduction in makespan. Suitable suggestions and recommendations are given at the end based on the results of the study.

This paper reviews the spherical motor in existence. There are multiple spherical motors with unique designs. Given the rising expectations for modern machine tools and robotic equipment, multi-DOF are required for product quality and the requirement to generate free-form surfaces. As a result, new machine tool designs and drive technologies are required. Researchers have been working on the development of various designs and coil arrangements of the spherical motor to attain the most efficient performance. The magnetic pole arrangement and control system plays a crucial role in attaining the desired outcome. The magnetic pole arrangement is important to create efficient torque to drive a primer. There have been studies about the magnetic pole arrangement and which arrangement serves the best purpose. The control system is required to obtain precise motion and keep the system in a stable state. This paper briefly discusses the different designs, magnetic pole arrangement, and control system.

With advancement of technology, each field is growing rapidly and becoming automated. Automation in industrial area has led to enhancement of quantity of products as well as made the whole process less time consuming. In industries, at a time, a lot of parameters are controlled for maintaining the appropriate environment in the industry. But till now, many mishaps occur in industry due to loss of readings while monitoring and controlling of various parameters, and this is controlled by the engineers. It is very difficult for a human to continuously monitor the system. In our paper, we have designed a smart plant monitoring system to overcome this problem in which we are taking the readings or values of different parameters on a single server (portal) using Internet of Things (IoT). We have monitored and recorded the values of parameters such as level, temperature, and humidity and if any parameter exceeds its limit, then the system takes the predefined action.

The Agile technique, which has a few guiding standards, is used by nearly all IT businesses. With dexterous, organizations can use an iterative approach to provide value to their clients more quickly and with fewer problems or escalations in both the environment of extend administration and computer program development. Customer-focused, self-organizing, and cross-functional teams that collaborate to identify needs and make progress arrangements are key skills in program improvement. Spry organizations place a strong emphasis on “collaboration,” which is essential to the development of their code. The problem that arises from this is “Following of Numerous Adaptations of Code Composed by Numerous Engineers at Distinctive Times.” Code changes must be planned for in order for the IT sector to successfully provide a final customer-centric item. IT works in this manner. The problem with centralized VCS is that it is centralized in nature and always depends upon developers to commit their changes on a central server. In the initial stages, it looks easy to set up and configure the one goes setup for centralized systems. In this work, various practical problems, from DevOps perspective, on using a centralized version control system are analyzed, and further approaches for migrating from a centralized version control system to a decentralized version control system are explored.

Green manufacturing is recent development with an aim to reduce pollution and safeguard the environment. The aim of this chapter is to find out, how green concept in automotive Industries is developing. In this chapter, Scopus data are used for the bibliometric analysis. On searching the database, 244 articles are found using the keyword Green manufacturing and Automobile sector. Keyword and co-occurrence analysis are performed by VOSviewer software for the network visualization. It is found that from 2015 onward, the topic is gaining momentum. Keywords like; automotive industry, green manufacturing and sustainable development have the maximum occurrences, means industries are working on these concepts. Co-occurrence analysis shows that Indian authors are jointly working with other countries authors on green manufacturing. This is very positive trend, as India is developing hub of automobile manufacturing of the world.

The increased public awareness, strict government regulations, and fast-paced technological advancements have made green supply chain research a crucial aspect of sustainability. To stay competitive in the global market, industries are adopting Industry 4.0 (I4.0) technologies. However, not many studies have explored the integration of I4.0 and Blockchain (BC) in the development of Green Supply Chain (GSC) and the emerging research themes in recent years. This study aims to fill that gap by identifying the research themes. Current study uses citation analysis to assess the most influential authors, journals, and countries, while keyword co-occurrence analysis is used to analyze the thematic evolution and popular topics in the green supply chain. The study provides a comprehensive and clear overview of the research themes that have emerged in the green supply chain from 2017 to February 11, 2023. The primary objective of the study is to conduct an in-depth bibliometric analysis of how Industry 4.0 and Blockchain technologies can enhance the green supply chain. The study highlights the potential of blockchain tools in solving issues related to the green supply chain. This study can benefit a wide range of stakeholders, including researchers, practitioners, policymakers, and organizations in the field of sustainability, green supply chain, Industry 4.0, and blockchain technology. Researchers can use the study's findings to identify new research themes, and potential collaborations, and understand the state of the art in the field.

Manufacturing industries face formidable challenges in reducing power consumption and maintaining product quality simultaneously in machining process. This is significant to remain competitive globally, comply with governmental regulations, and deal with rising electricity prices and customer awareness. Multi-objective optimization techniques have become an essential tool to deal with conflicting responses simultaneously. This chapter aims to simultaneously optimize power consumption and surface roughness in turning process. This is achieved by (i) developing statically significant regression models using the backward elimination technique; (ii) generating Pareto solutions using WSM and NSGA-II techniques for rough as well as finish turning operations. Results are compared with existing literature to validate the developed models. The scientific value of the present work lies in proposing a methodology to generate Pareto solutions that depend on the weight assigned to the response functions that would assist a practitioner in better decision-making depending on the managerial requirements.

In order to remove moisture from the air, sorbent is utilized. In most of industrial areas, such as food industry, drug and cold storage industries. Sorbent-based air cooling powered by solar energy is suggested. The solar power is used for heater of 1.5 KW power. It is investigated how solar energy is used to generate power and the effects of airflow rate and increases moisture removal rate (double sorbent wheel). The study’s anticipated findings will point to energy savings. To determine the eliminated moisture as a function of air flow rate at solar noon, an empirical equation is utilized. Additionally, an empirical calculation was employed to determine how well the wheels regenerate and absorb energy. For some applications, sorbent dehumidification systems’ capacity to dry the air to any desired humidity level is advantageous. The moisture removal rate is increased by this TSSD. This technique is limited by the requirement for high-grade heat (about 180°F) to replenish the sorbent. The sorbent wheel’s design clearly reflects customer health, safety and hygiene (rotor). Depending on the project’s characteristics, techniques like sorbent dehumidification is used, depending on the particulars of the project.

Assembly line balancing is an important practice in the manufacturing industry. It enables improvement in productivity by saving idle time and costs. This chapter presents productivity improvement of an assembly line in a refrigerator manufacturing unit in India. The study is carried out on the basis of observations made on the shop floor by using the technique of time and motion study. After collecting data, identification and reduction in the Muda (Waste), Mura (unevenness) and Muri (Overburden) was carried out. The Yamazumi charts were used to identify the stack of all the VAA, NVAA and SVAA in every activity. The main aim of the study was to improve productivity, fatigue reduction, cost optimization and safety using the 3M reduction and Line balancing method with the help of Yamazumi charts to ultimately achieve customer delight by manufacturing best quality products.

The population of India has peaked to a global high. Rapid urbanization is causing increased production of new materials. Having an impact on the health of the society and ecosystem, the need for proper waste treatment and management has always been an area of critical attention. The paper presents a new approach toward understanding waste and solving the critical problems of waste management in a different way. General understanding and misconceptions about waste, important concepts like ground-level organic waste processing, the new recycling trend, problems of waste accumulations, waste segregation, and their potential solutions are discussed in the paper.

This paper discusses the impact of Industry 4.0 on manufacturing industries and distribution networks, as well as the design principles that helped create it, including service orientation, virtualization, real-time capability, decentralization, modularity, and interoperability. Also, Industry 4.0 integrates information technology and operational technology systems, creating a network between autonomous manufacturing equipment and wider computer systems. The paper presents several case studies of successful implementations of Industry 4.0 technologies, including Renault's use of automation, data analytics, and artificial intelligence to improve its manufacturing processes, a case study of an industrial company's transformation into a smart factory through the adoption of technologies in Industry 4.0, and the benefits of digital technologies in transforming traditional supply chain operations into next-generation digital supply chains. The paper concludes with case study of Covid-19 and a discussion of the intense competition faced by aerospace and defense companies and how Industry 4.0 technologies could help them stay ahead.

In recent years, especially the post-pandemic era where labor force scarcity has emerged as a concern seeking rising issue, robotics and automation in agriculture sector have stood up as a solution for this problem. Also, to fulfill the demand of exponentially rising population in world, dependency on unskilled labor should be turned into usage of more technology in agriculture like robots, drones, etc., to avoid crop loss due to negligence and mishandling of harvest. In this paper, a novel design of robotic fruit plucking manipulator using sliding cutter for fruit plucking drones and robots is discussed. This manipulator is proposed to be used by robots and drones for fruit plucking using holding and peduncle cutting of fruit. Cutting peduncle of fruit instead of pulling and twisting of fruit by drones is beneficial in a way that it reduces the jerks faced by drones due to sudden break of abscission layer. For this design of manipulator for cutting peduncle of fruit, no specific identification of peduncle is necessary, making it easier to program for just fruit identification by computer vision system and not its stem. This will also minimize the impact of the damages caused to the fruit by the conventionally used multi-fingered grippers, which uses fruit plucking methods like pulling, twisting, bending, etc., because this method also offers peeling of skin of fruit, shaking tree branches which results in unnecessary fall of other fruits and leaves and other harms causing losses.

Machine learning and artificial intelligence have emerged as the most advanced technologies in today's society for solving issues as well as assessing and forecasting occurrences. The use of AI and ML in various organizations is to capitalize on the benefits of vast amounts of data based on scientific approaches, notably machine learning, which may identify patterns of decision-making and minimize the need for human intervention. The purpose of this research is to develop a suitable neural network model, which is a component of AI and ML, to assess and forecast crack propagation in a bearing with a seeded crack. The bearing was continually run for many hours, and data were retrieved at time intervals that might be utilized to forecast crack growth. The variables RMS, crest factor, SNR, skewness, kurtosis, and Shannon entropy are collected from the continuously running bearing and utilized as input parameters, with total crack area and crack width regarded as output parameters. Finally, utilizing several methodologies of the neural network tool in MATLAB, a realistic ANN model was trained to predict the crack area and crack width.

Revolution in traditional manufacturing paradigms has become prerequisite in the twenty-first century manufacturing environment. Globalization currently drives the personalization and customization of products. To be competitive, companies have to enhance with the changing market environments by producing low-cost products with high quality that efficiently respond to market unpredictability. Reconfigurable manufacturing system (RMS) characteristics have the competence to solve the difficulties rising due to changing market requirements. The potential of the RMS to quickly and effectively reconfigure capacity and functionality allows it to tackle this challenge. A systematic RMS design process is essential, as traditional manufacturing system design methodologies do not facilitate the design of an RMS. RMS can be reconfigured at the system level and the machine level. This study explores the concept of machine-level and system-level RMS design. A review of current RMS design methods is conducted, with a focus on significant theories and practices to analyze approaches for RMS system-level design. From this, a generic design approach for RMS system-level design approach is developed by integrating the reviewed methodologies.

In any industry, musculoskeletal risks are a common and can be detrimental to productivity and worker attendance. Building bus bodies involves a range of tasks with varying levels of physical exertion. The manufacture of roof sticks, in particular, requires different levels of human energy, making it important to assess ergonomics. This study aimed to identify risk factors associated with various work activities like marking, rolling, bending, cutting, hammering, drilling, and storage. Risks are analyzed by OWAS, REBA, and RULA methods. The study found significant differences between the results obtained from these three methods with varying degrees of risk to the musculoskeletal system. The development of appropriate workstations that consider workers’ anthropometry can help reduce the risk of WMSDs.

This paper provides an overview of the engine piston, a crucial component in internal combustion engines responsible for powering the combustion process. The piston is a cylindrical metal piece connected to the crankshaft, enabling reciprocating motion within the engine's cylinder. By moving up and down, it compresses the air–fuel mixture, leading to ignition and generating the force required for engine operation. The piston's failure can be attributed to thermal and mechanical stresses. Extensive research has been conducted on piston design, focusing on materials used and modifications to crown shapes. This review consolidates the existing knowledge on piston design, discussing advancements made in understanding the impact of different materials and crown shapes on performance and durability. By analysing the literature on piston research, this paper aims to contribute to the comprehension and improvement of piston design, ultimately enhancing the efficiency and reliability of internal combustion engines.

Rolling element bearings are the key component of every rotating machinery and are prone to various type of imperfections e.g. localized spalls, waviness, misalignment etc. Different rolling element bearings, such as deep-groove ball bearings, tapered bearings, needle bearings etc. are used in industry for specific applications. Spherical rolling-element bearings being one such category, are used to counter dynamic misalignment between two shafts. During misalignment, the stiffness characteristics of the bearing also change and thus characterize the system’s internal dynamics. In this study, a mathematical model is presented for a double-row self-aligning rolling-element bearing to analyze its nonlinear contact stiffness characteristics. A stiffness matrix of order three is constructed and the coefficients are evaluated for variation with respect to change in radial-load, axial-load and misalignment angle. Also, the time-varying stiffness coefficients are presented. The results show the variation of bearing’s contact stiffness with change in radial load and shaft speed. The time variations of contact stiffness have also been presented.

Solar energy is the demanding field in present era and keeping in view the limited resources like petroleum etc., a solar electric vehicle is proposed in this work. CAE analysis of chassis for the solar vehicle has been performed using Hypermesh V-13 under different boundary conditions. The frame of the vehicle under consideration is designed & analyzed with a chain sprocket system along with motor. As per previous works, the finite element analysis of automobile structural frames has been carried out and chassis is proven to be the most important part of the vehicle, which is not just the structural frame or a skeleton of a vehicle but also the passenger compartment which is responsible to protect the driver from acts like collisions. The current work is an attempt to analyze the loading, meshing and considering all boundary conditions to provide realistic environment to the roll cage under consideration. The roll cage is designed to accommodate two people at a time. It has been attempted to keep the design optimum one and to support various equipments which the vehicle will finally house in it. Flexible solar panels, chromyl pipes, aluminum knuckles, light weight and much efficient lithium ion battery etc. have been accommodated during the actual fabrication of vehicle in order to reduce the weight without hampering the strength.

Cellular manufacturing has shown to be an effective approach for enhancing productivity in recent years. The benefits of cellular manufacturing depend on design of machine cells, part families, and operating strategy that take advantage of cell features. This paper proposes a hybrid algorithm for manufacturing cell formation in Gear Box manufacturing industry. The proposed algorithm for cell formation combines the Rank Order Clustering-2 and similarity coefficient techniques. This array-based hybrid clustering algorithm is compared to Rank Order Clustering-2 and Void-Based Approach, for real-life example to demonstrate its effectiveness. Four performance measures, viz. exceptional elements, grouping efficiency, grouping efficacy, and machine utilization index are considered to evaluate these algorithms. The proposed method is found to be the most efficient method for cell formation and thus suggested for utilization in the Gear Box manufacturing industry under consideration.

The electric and electronic industry has experienced tremendous expansion in recent years, which has resulted in a high rate of obsolescence and a drop in the End of Life (EOL) or End of Use (EOU) of the product. E-waste is the word used to describe these unwanted electrical and electronic devices. The management of the e-waste produced in developing nations, like India, currently faces a number of challenges and the only workable solution is to limit waste production and engage in recycling. In order to achieve recycling, it is necessary to identify the barriers affecting the formal recycling for effective e-waste management. The aim of the article is to identify the barriers with previous literature and expert opinion, then analyses of barriers have been done by using interpretive structural modeling (ISM) technique. Eleven barriers have been identified and developed contextual relationship between them. The result shows that lack of incentives, lack of customer awareness toward recycling, and lack of government policies are the key barriers affecting formal recycling of e-waste in India.

Horizontal scalability is very crucial in cloud applications. For microservices applications that are installed in AWS cloud environment, auto-scaling feature will automatically scale applications based on the configurations. Amazon ECS can calculate service performance as per CPU and memory resources consumed at a point in time and it provides data to CloudWatch metrics, such as ECSServiceAverageCPUUtilization and ECSServiceAverageMemoryUtilization. This paper discusses how auto-scaling can be achieved using the metrics and applying the scaling policies proportionally. Details of how the metrics collection strategies can be used for Application Auto-Scaling to scale services installed in the AWS cloud environment are discussed. It is observed that auto-scaling not only supports scaling up the instances in peak hours but also scales down the instances when there is minimal or no load on the application. Auto-scaling keeps monitoring the instance metadata. It helps in identifying the health status of the instances. It is observed that in peak load situations, if there is a demand for instant user requests or there is a surprise increase in user transactions increase in user requests, auto-scaling automatically adds more resources to handle the situation, which makes the system fault tolerant.

Nowadays, there is a growing need to implement driver-drowsiness detection technologies to prevent potentially fatal accidents. To address this, a system has been developed utilizing components such as an Arduino Uno, an eye blink sensor, and a vibration motor module. The system aims to detect drowsiness in drivers and provide timely warnings. The functionality of the system relies on analyzing the driver’s average eye blink rate. Real-time data was collected and processed using a normal distribution to determine the average eye blink rate for different age groups. After careful analysis, it was determined that an average eye blink rate of 14.1400 blinks per minute would serve as the standard for the system to effectively operate across all age groups. The device incorporates a vibration motor module that emits vibration pulses to awaken the driver in the event that drowsiness is detected while driving. By leveraging the Internet of Things (IoT), the objective of this project is to develop a smart warning system for automobiles. The system aims to mitigate the consequences of drowsy driving, including potential fatalities and injuries, with the ultimate goal of preventing road accidents.

Since the last decade, the use of photovoltaic (PV) solar panels for power generation has been increasing in rural as well as urban areas in India. Most of these PV panels are susceptible to dust accumulation. Dust accumulation along with other factors drastically reduces the efficiency of output of energy from PV panels. The main purpose of this study is to design a semi-automatic cleaning of PV panels to improve its efficiency. For the study, a mid-range PV panel commonly utilized in commercial as well as domestic has been selected for the project. The conceptual design of the cleaning system was initially idealized and further developed a CAD model with the help of Creo software and Solid Work software. In the proposed system, a rechargeable battery, servo motor, roller, and pulleys are used for cleaning the solar panel. This system helps to clean the dust regularly which results in enhancing the PV panels’ overall effectiveness. This system also reduces human interference in the cleaning process. The battery used in this system so there is no extra power source is required because the solar panel instantly recharges the device.

The power generated by Photovoltaic (PV) system strongly depends upon the incident radiation. The uniform incident radiation across the PV panel is required for efficient operation of PV system. Partial shading of PV panel gives many local maximum power points and one global MPP in the power-voltage graph of PV system. The traditional maximum point tracking (MPPT) methods are incapable to achieve this global MPP when PV panel is shaded non-uniformly resulting in inefficient performance of PV system. Therefore, alternate methods for MPPT tracking must be designed for tracking global MPP in partially shaded conditions. Performance of PV system can also be evaluated for various configurations of PV arrays like series and series-parallel. This paper provides the solution for tracking global MPP when series configured PV panel is partially shaded. MATLAB-based simulation model gives improved results for partially shaded PV panel with different shading patterns.

This article aims to design a metal strip and bar twister machine that can be used to twist bars and metal strips in the maker's workshop for sliding grills. This project aims to create and build a portable twister. This machine can be used to twist steel strips into the desired curvature. Its size is extremely convenient for working from a portable location. It is entirely made of mild steel. It is also easy to transport and use anytime and anywhere. It makes it easier for humans to work and also requires less skills to operate the machine. We have designed a manually operated twister machine using a shaft, bearings, frame, and handle. The machine is manually operated therefore, our goal is to improve accuracy at an affordable cost without affecting the efficiency of twisting. This machine operates on a basic handle mechanism instead of a complex design. Because it is portable, it is able to be utilized in a small workshop or shop.

Invention of computer changed way of doing things and became part of human life. For better portability, mini versions of computers called personal computers (PCs) or laptops are developed. As electronics emit heat, the compact internal design of laptop challenges the thermal management of them. Development of powerful processors for better clock speed and thinner designs of laptop making heat management much more complicated. From the day of invention of laptop, new methods are being developed and a lot of research is going on for better heat management. This paper reviews the literature on various aspects of thermal management techniques dealing with laptops. Included papers on experimental work, numerical modeling, and advanced cooling methods.

Sustainable growth is needed of the current age. The enormous production of automobiles in India is posing a threat to environment, economy and health of its residents. This paper aims to study the required initiatives to develop green manufacturing in automobile sector. The major changes needed to go ahead with the continuous rise of production of automobile are green plantation, proper use of energy, changes in design of several components of engine, switching to electrical vehicles, strict compliance of emission norms, making green supply chain and maximize use of renewable source of energy. The paper explores the steps taken by the automotive giants of India toward sustainable development of this industry.

Stock outs in public distribution system (PDS) are a common issue causing negative impacts on availability and affordability of essential goods for vulnerable population. Poor supply chain management, government agency coordination, and funding cause PDS stock outs. The public distribution system in Kerala is facing a significant challenge of low warehouse capacity with respect to the corresponding high outlet demand, leading to frequent stock outs and inefficiencies in the stages of the food system. This can cause some consequences that include reduced accesses to essential goods for vulnerable population and decreased trust on government agencies. This study focuses on how Monte Carlo approach to determine stock outs for management decision. The monthly and seasonal simulation is carried out to analyze the probability on stock outs at different time scales. The probability of stock outs can be computed by Monte Carlo simulation, which leads in understanding the risk related with their current inventory and demand rates. Later the t-test is conducted. This helps in enhancing the performance of the PDS by minimizing stock out occurrence, improving customer satisfaction and reducing wastage of resources. By the effective use of Monte Carlo approach in reducing the uncertainty of stock outs, company mangers gained a valuable method in taking decisions useful for the managerial functions.

Today, the industry faces the test of agreeing with stringent ecological enactment due to the shortage of global warming, waste management, and natural resources issues. Expanding small and medium-sized firms (SMEs) in India is critical since they account for a large portion of total manufacturing output. In this age of globalization, SMEs are under pressure to maintain long-term profitability by cutting costs while still being environmentally sensitive. This study aims to recognize 12 drivers based on expert views through surveys and literature. In this article, the hierarchical and contextual relationship structures between SM drivers were addressed using the intergraded approach of “decision-making trial and evaluation laboratory (DEMATEL)” and “interpretive structural modeling (ISM).” After-effects of the study indicate that "social and environmental responsibility," "employees motivation, health, and safety," and "scarcity of resources and higher waste generation" are essential drivers, as illustrated in a new ISM-DEMATEL methodology. Thus, our research will help prioritize the critical drivers for SM practices in Indian SMEs.

Solar pumping system has been a need when it comes to irrigation in remote areas. Various factors come into the picture which affect the efficiency of solar pumping systems like solar irradiation, environmental and meteorological factors, etc. Efficiency always plays an important role in the solar pumping system as the cost of the whole system depends upon it. As the efficiency of the motor increases, it increases the overall efficiency of the system, which results in a lesser rating of solar installed for a particular power output. The scope of this paper is to design an integrated permanent magnet synchronous motor of 5 HP and compare it with the existing induction motor and BLDC motor. Also, cost analysis has been done by considering the complete cost of solar setup from erection and motor prototype. It was found that the IM has 78%, BLDC has 86%, and the designed IPMSM has 92% efficiency. For a 5 HP motor generally being used for water pumping, consequently, it has reduced the cost of the whole system. System cost analysis is done to prove the overall efficacy of the solar pumping system.

In India, the prime mode of long transport for passengers and freight is Indian railways which use ICF and LHB coaches. LHB coaches which use FIAT bogie are superior to ICF coaches due to their higher carrying capacity and being suitable for high-speed application. For comfortable and controlled ride, springs are essentials; however, due to dynamic loading, spring failures are observed. A similar type of failure is observed in outer springs of primary suspension in FIAT bogie during maintenance in Indian railways. To calculate dynamic deflection of spring, 4 DOF equivalent spring mass damper system is modelled and required technical data is collected from Indian railway publications and research papers. The state space approach is used to check the controllability of the system, and the system is found to be continuous and controllable. By considering track excitation, it is found that spring has static deflection 92.3 mm and 14 mm of dynamic deflection which can lead to dynamic failure.