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The scenario of the Automobile Industry has changed, not only in India but also for the world as a whole. Rise in demand for more sustainable mobility and vehicles with increased usage of renewable energies have given rise to a revolution. This revolution has not only been affecting mankind for better implementation of resources but has also shown tremendous greener and cleaner effects on the flora and fauna of the land as a whole. There has been electrification of vehicles, addition of superior systems to generate lesser harmful effluents and also changing the guidelines of emissions to cleaner and more sustainable ones. The purpose of this paper is to review and state the existence of different pieces of literature for the transition from Bharat Stage-IV (BS-IV) to Bharat Stage-VI (BS-VI). Much has been written about the transition to BS-VI, the difficulties, and the positive effects on vehicle reliability, economy and the environment. In particular, this work explores the parallels between BS-VI and the corresponding Euro norms and their sequence of implementation. The aforesaid transition has increased the number of oil refineries in India that produce BS-VI-compliant fuels, keeping in mind the increased need for it. This paper explores the current state of the literature and throws light on many of the questions regarding this significant transition. The authors have tried to study the ongoing issues including the stress of inventory management of dieselrun passenger cars, the different refinements required to process and manufacture the BS-VI-compliant vehicles, people's reaction on the changes brought in by the issuing bodies with respect to the norms, to name a few. The focus of the research has been on Bharat Stage emission norms, their implementation challenges and automobile pollution in India to name a few. The authors also discuss the needs, advantages and challenges faced by the industry, producers and customers and conclude that this change is one of the reasons for the decline in car sales in India as it may affect the buying power of the customers. [ABSTRACT FROM AUTHOR]

The Vision Guided Robotic systems (VGR) is an essential aspect of modern intelligent robotics. The VGR is rapidly transforming manufacturing processes by enabling robots to be highly adaptable and intelligent reducing the cost and com- plexity. For any sensor-based intelligent robots, vision-based planning is considered as one of the most prominent steps followed by controlled actions using visual feedback. To develop robust vision-based autonomous systems in robotic applica- tions, path-planning and localization can be implemented along with Visual Servoing (VS) for robust feedback control. In the available literature, most of the reviews are focused on a particular module of autonomous systems like path planning, motion planning strategies, or Visual Servoing techniques. In this paper overall review of different modules in vision-guided robotic systems is presented. So, this review provides researchers with broader in-depth knowledge about different modules that exist in the vision-guided autonomous system. The review also includes different vision sensors that are commonly used in industries covering their characteristics and applications. In this work, overall, 227 research papers in path planning and vision-based control algorithms are reviewed with recent intelligent techniques based on optimization and learning-based approaches. The graphical analysis illustrating the advancements of research in the field of vision-based robotics using Artificial Intelligence (AI) is also discussed. Lastly, this paper con- cludes by discussing some of the research gaps, challenges, and future directions existing in vision-based planning and control. [ABSTRACT FROM AUTHOR]

A lot of energy is released during the cement manufacturing process, and a large amount of carbon dioxide (CO2) is discharged into the environment. Presently, researchers are focusing on reducing CO2 emissions by researching sustainable alternatives to traditional Portland cement-based materials. A comparative study on the material characterization of both calcined agro-based waste ash (calcined ash) and uncalcined agro-based waste ash (uncalcined ash) and its utilization as a binder along with granulated blast furnace slag (GGBS) in the alkali activation process is carried out in this paper. The study regarding calcined and uncalcined ash would help in better understanding the variation in the properties of the material and its behavior during the alkali activation process. The tests conducted on both calcined and uncalcined ash along with GGBS include specific gravity, X-ray fluorescence, X-ray diffraction, scanning electron microscopy, reaction degree, Brunauer-Emmett-Teller (BET) analysis, particle size distribution, and the Puntke test for understanding its microstructural characteristics. Later, alkaliactivated mortars are prepared using 30% weight of agro-based waste ash (both calcined and uncalcined) and 70% weight of GGBS. A comparison of the compressive strength of alkali-activated mortars prepared using calcined and uncalcined ash was studied, which showed a promising increase in the strength by 11.02% of mortar prepared using calcined ash (23.46 MPa) for 28 days when compared to mortar prepared using uncalcined ash (21.13 MPa). The major finding from this study indicates that calcined agro-based ash with GGBS shows better results compared to uncalcined agro-based ash with GGBS. [ABSTRACT FROM AUTHOR]

Petri net (PN) is a well-known graphical and mathematical model that can be used to describe a system quickly and dynamically. Over the last decade, it has provided many applications for academia research and industrial practice, especially, for the system verification and performance evaluation. Compared with other system models, the PN model is easy to implement. Therefore, it can be recommended as an effective way for people to study the model checking and validation. For the purposes of reducing time span to get familiar with PN tools, it is necessary to have an informative guide, providing general information about a PN software tool for system developers. This paper aims to review HiPS, PIPE, and WoPeD tools which are simple, lightweight, and free of charge. Our proposed approach also compares their strengths and limitations with each other to help the system developers determine a suitable PN software tool and shorten the time to markets for their products. Thus, it is truly beneficial to fast develop a manufacturing system. [ABSTRACT FROM AUTHOR]

Although machine learning for quality prediction of manufacturing processes has attracted attention in the literature, there is a significant lack of case studies from industry, especially in medical sector. This paper proposes a data-driven approach to infer the batch quality of blood glucose test strips. Once the low quality of work in process is detected, unnecessary process waste could be eliminated. Starting from data pre-processing, which consists of Synthetic Minority Over-sampling TEchnique and Random Over-Sampling Example, this project tries to balance the illdistributed data first. Followed by machine learning aims to classify and predict the quality of blood glucose test strips. Different models are evaluated by the Receiver Operating Characteristic curve and Area Under Curve. Computational results show that the decision tree and random forest after SMOTE perform better than the counterpart of ROSE method. Ensemble learning, such as random forest, out-wins base learner decision tree. To sum up, random forest with SMOTE is the suggested model for accurately predicting the quality of blood glucose test strips. There is a 30% improvement in error rate under random forest and SMOTE for NG class that could be of top concern for prognosis. Several factors, including the direction of applying test reagent onto test strips and the position where the strips are located, that affect quality of test strips have been identified. Explanations in terms of inspection and manufacturing are discussed subsequently. Finally, the prognosis of quality can be attained through big data and statistical machine learning. [ABSTRACT FROM AUTHOR]

The lifecycle of a product is getting shorter in today’s market realities. Latest developments in the industry are heading towards achieving products that are easy to recycle, by developing further technological advances in raw materials ought to include input from End of Life (EOL) products so a reduction of natural harm could be achieved, hence reducing the overall production environmental footprint. Therefore, the approach taken as a design for environment, a key request nowadays in order to develop products that would ease the reverse manufacturing process leading to a more efficient element recycling for later use as spare parts or remanufacturing. The methodology proposed compares three probable disassembly sequences following a comparison of literature-found procedures between genetic algorithms and as a “state space search” problem, followed by a hybrid approach developed by the authors. Time and evaluation of these procedures reached to the best performing sequence. A subsequent augmented reality disassembly simulation was performed with the top-scored operation sequence with which the user is better able to familiarize himself with the assembly than a traditional paper manual, therefore enlightening the feasibility of the top performing sequence in the real world. [ABSTRACT FROM AUTHOR]

Sustainability issues are challenging the cement industry due to its high emission of greenhouse gas, intensive energy consumption, and depletion of resources. One of the strategies to mitigate the problem is to improve process control techniques and optimize resources. The objective of this paper is to survey the approach and evolution of statistical process control chart techniques and study their significance and limitations in the case of optimization of cement production. The main research question this study address is “What are the significances and limitations of statistical process control chart methods to the optimization of cement process?” The methodology of the study followed the literature survey with meta-analysis and focused on identifying the statistical process control chart design techniques and their application to cement industries. The result of the survey indicated that statistical and mathematical algorithms are encapsulated by advanced soft computing methods; however, still, it is the foundation for advanced process control methods. Moreover, it is found that statistical process control has a theoretical and technical gap in the application of the cement industry. The theoretical gap identified in the literature is that in the case of a complex produc- tion system the techniques recognize the occurrence of the out-of-control case in the production process but are not able to identify the cause of variation. The technical gap in the statistical process control techniques is that there are several important theoretical control chart techniques, but they are not researched well on how to apply to the real world. [ABSTRACT FROM AUTHOR]

This paper aimed at reviewing current researches on the use of Response Surface Methodology in the optimisation of Biogas processes. It explored the per- formance of RSM in biogas process optimization, the most effective technique and the attendant effective software used in such processes. It attempted to review literature in the area. 55 articles were systematically reviewed. The online data- bases included were Google Scholar, Scopus and other statistics-based optimization research databases with keywords from Response Surface Methodology in Biogas Optimization. The review finds that RSM proves to be an effective statistical tool. It has achieved optimum objectives for biogas production: increased biodegradability, optimum biogas yield and methane production, increased Total Solid and reduced Volatile Solids and an increased COD removal. The key advantage of RSM was found to be a reduced number of experimental trials, making it time and cost-effective. 37 process parameters have been optimised using RSM, over the last two decades. Five of these parameters are dominant. Namely,: Temperature, pH, Retention time, Pre- treatment and Loading rate. The major challenges associated with the use of RSM in biogas production process optimization are the limited experimental range. Techniques to combine RSM with other optimization methods such as the Taguchi, Kriging or the Artificial Neural Network (ANN) are being developed to address these challenges. Design Expert software is the most used software because of its low cost of use. However, Statistica offers a better efficiency. [ABSTRACT FROM AUTHOR]

Improving the productivity of a manufacturing firm is very imperative in order to obtain high profits in such a competitive market. Equipment is an important part of a manufacturing system and its efficiency directly affects the quality and cost of the product contributing to overall productivity of a manufacturing plant. This paper focuses on calculating the overall equipment effectiveness (OEE) at a local pharmaceutical industry and investigates the key factors which influence the overall equipment effectiveness. OEE is an important tool which identifies areas that may have bottleneck in the production line, covering three major aspects such as the availability, performance, and quality rate of the output of equipment. On the basis of OEE, six significant losses were identified. The preliminary results of the analysis showed that OEE for the dry suspension (D/S) section is only 23% which is substantially below the benchmark of OEE level i.e. 85%. The results also presented that prime cause involves a high difference between the performance level of world class and dry suspension (D/S) process which is due to unavailability of machines for a longer period of time and malfunctioned machines producing products in large quantities that need to be reworked or scraped. In order to achieve the world class level of OEE, the performance level of dry suspension (D/S) process needs to be enhanced to 95%. A preventive maintenance strategy could be implemented in the industry in order to avoid inefficient practices and enhance the performance of the whole plant. [ABSTRACT FROM AUTHOR]

Currently, the marketing strategy in the manufacturing industry is becoming more and more competitive. The emergence of lean production aims to reduce unnecessary wastes in the manufacturing processes. In order to achieve this goal, it is necessary to optimize manufacturing processes so as to reduce their costs. Therefore, reducing manufacturing costs has become the main developing direction people are now pursuing. The iPhone XS developed by Apple is now a popular product. To this end, this paper aims to describe its manufacturing system by using Petri nets, and to integrate the Prim algorithm with a minimum spanning tree (MST) to find the minimal cost path, achieving the goal of reducing the manufacturing costs. Two simulation software tools, WoPeD and PIPE, were applied to conduct the experiments. The experimental results have significantly indicated that the proposed approach is more feasible and acceptable than others. [ABSTRACT FROM AUTHOR]

In this paper, a fuzzy logic controller will be introduced and discussed that can be applied for manufacturing systems. Our proposed control system will utilize fuzzy logic principles to find the optimum number of machines and operators assignment by developing the heuristic relations between the inputs and the outputs of the controller. The project inputs are the desired daily production and the average product processing time by each machine. The production process is assumed to be automated and the routing of products to these machines will be automated and requires negligible time. All machines are also assumed identical in both the functionality (manufacturing process) and the service time. Given the inputs as the number of products and the average service time, the goal of this controller will be to determine the number of the machines and the number of the operators needed to meet the production demand. [ABSTRACT FROM AUTHOR]

Both economic and environmental aspects significantly influence the design process since the early phases of preliminary design. The total Life Cycle Assessment (LCA) and the End of Life (EoL) of products have to be defined in the early design phases too but, for industrial products that are not feasible to automatic production, they are hard issues. However, the EoL of products can be assessed by evaluating the disassembly of joints assembling the product, even when the production process is subject to an important contribute of workmanship. In this paper, a useful method is proposed to analyze the disassembly plant of products, in order to optimize the design process in the early preliminary phases. The method quantitatively evaluates a Disassembly Index that describes the attitude of a product to be disassembled. A case study describes the disassembly attitude of structural subassemblies of a sailboat. In order to test the applicability of the model described to both manual and automated disassembly, a further application of the method is proposed on a Computer CPU. As result, the model demonstrated good sensitiveness to the testing of products quite different for dimensions, number of components, manufacturing processes and, in all cases, it quantified the disassembly easiness with good relevance. [ABSTRACT FROM AUTHOR]

Traditional inventory policy was deeply investigated for various kinds of demand in different industrial sectors. More extensive explorations on inventory policy, including the combination with manufacturing process, detailed attributes of the purchased products, etc. was conducted by many researchers. During manufacturing process, lifespan of cutting tools have significant impact on both the quantity of inventory and production cost. In this paper, the impact of maximum allowable stopping time for cutting tools on production-inventory policy under general production demands was investigated. An optimal inventory policy with general demand (OIPGD) was developed with which the allowable stopping time for tools, order-up-to-level inventory, and order cycle can be optimally determined by an exhaustive searching algorithm. Examples with different distributions on tool lifespan and production demand is presented to show the implementation of the OIPGD model. The results and the sensitivity analysis about the parameters show that optimized combination of selection for tool allowable stopping time, order-up-to-level, and order cycle time can dramatically minimize the total cost of the whole inventory activity. [ABSTRACT FROM AUTHOR]

The main idea of lean manufacturing is actually simple and means relentlessly work on eliminating waste from the manufacturing process. However, after implementing these systems, many organizations find it difficult to achieve the desired results of them. Thus, the lean manufacturing remains an important area of research. For the importance of what was presented, this paper tries to investigate the critical issues affecting the success of implementing lean manufacturing programs in the iron and steel factories in the Kurdistan Region of Iraq. To achieve this end, the mixed methods represented in a questionnaire survey and semi-structured interviews to collect the data in the framework of the case study were chosen. The questionnaire instrument already has been tested. The results of the study revealed that the importance of management commitment and allocate resources, ethical train the workforce and ethical instructions as the critical success issues of lean implementation. Based on the findings, the organization managements are recommended to provide financial and moral support to employees to enable a successful implementation of lean manufacturing aimed at obtaining the desired results of them. [ABSTRACT FROM AUTHOR]

Micromechanical bending (M2B) is a newly developed micro patterning technique applied for vertically aligned carbon nanotubes (VACNTs) array, commonly known as CNT forest. This process is required to realize the various use of CNT forest in Micro-Electromechanical Systems (MEMS). There are various parameters involved in M2B process that controls the surface roughness of the processed structure of the CNT forest. However, there is no mathematical model available, yet that could predict the influence of these parameters on the surface roughness of the patterned CNT forest. In this paper, an empirical approach has been used to predict the surface roughness for the micromechanical bending (M2B) processed CNT forest. At first, several experiments were conducted by varying different process parameters such as tool rotational speed, lateral speed and a step size of the bend. Best optimized process parameters were identified at 2,000 rpm tool rotational speed, 1 mm/min lateral speed and 1 μm step size that produced a minimum surface roughness of Ra = 15 nm. Finally, a response surface methodology (RSM) based mathematical model was developed and validated with reasonable accuracy to understand the impact of different parameters on the M2B process. [ABSTRACT FROM AUTHOR]

Nowadays, considering inflation observed in most societies, it is important to investigate on effect of this phenomenon on inventory/production decisions. Consequently, the paper aims to study the influence of inflationary condition on a specific periodic review integrated vendor–buyer inventory system in the presence of vendor’s imperfect manufacturing process. The lead time crashing cost is represented as a piecewise linear function of reduced lead time. In practice, the identification of the protection interval demand distribution of restricted data is a difficult task. As a result, this investigation assumes that the information about the protection interval demand for buyer is limited to its mean and standard deviation. Buyer’s ordering cost can be reduced through further investment. For the discussed model, an effective solution procedure is developed to determine optimal policy. Theorems on conditional convexity and concavity of objective cost function in the decision variables of the respective inflationary integrated inventory system are shown and proved. A numerical example is presented to illustrate the results of the proposed model. [ABSTRACT FROM PUBLISHER]