Ebook: Advanced Production and Industrial Engineering
Things change rapidly in the field of engineering, and awareness of innovation in production techniques is essential for those working in the field if they are to utilise the best and most appropriate solutions available.
This book presents the proceedings of ICAPIE-22, the 7th International Conference on Advanced Production and Industrial Engineering, held on 11 and 12 June 2022 in Delhi, India. The aim of the conference was to explore new windows for discoveries in design, materials and manufacturing, which have an important role in all fields of scientific growth, and to provide an arena for the showcasing of advancements and research endeavours from around the world. The 102 peer-reviewed and revised papers in this book include a large number of technical papers with rich content, describing ground-breaking research from various institutes.
Covering a wide range of topics and promoting the contribution of production and industrial engineering and technology for a sustainable future, the book will be of interest to all those working in production and industrial engineering.
The Seventh International Conference on Advanced Production and Industrial Engineering (ICAPIE-22) was held during 11–12 June 2022. It was organized by Centre for Advanced Production and Industrial Engineering Research (CAPIER) – Delhi Technological University (DTU).
The prime motive of ICAPIE-22 was to seek new windows for discoveries and exploration in Design, Materials, and Manufacturing, which plays a major role in almost all fields of scientific growth, and to provide an arena for showcasing these advancements and research endeavours being undertaken in all parts of the world. We have the privilege to host a congregation of world-renowned scholars and experts as keynote speakers, who gathered for the conference and grace us with their words of wisdom in their fields of expertise. We feel proud in saying that in comparison to the last three years, we have received a larger number of technical papers with rich content, comprising path-breaking research from participants from various institutes.
We express our deep sense of gratitude to our Vice-Chancellor Prof. J. P. Saini, Chief patron for his keen interest in promoting research in the Delhi Technological University and for always been an inspiration for achieving greater heights.
The organizers are grateful to all authors and participants who have made significant contributions in the form of their papers.
We also thank Dr. Harish Kumar, HoD (Mechanical Engineering), National Institute of Technology Delhi, and the reviewers for their constructive suggestions to improve the manuscripts. We also thank IOS Press for their constructive comments and suggestions which helped in bringing the best quality work in the form of this publication, without which it would not have been possible to come up with this proceedings.
Prof. Ranganath M. Singari
HoD (Design) & Professor
Mechanical, Production & Industrial and Automobile Engineering
Delhi Technological University, Delhi
Conference Chair, ICAPIE-22
Prof. Pavan Kumar Kankar
Associate Professor, Department of Mechanical Engineering
Indian Institute of Technology Indore
Madhya Pradesh 453552, India
5G is the new wireless technology. In vehicles, it is used to support the network associates for sensor on the provide and roads V2X service to drivers and pedestrians. The 5G V2X (vehicle to everything) communication is a benefit to us, for low latency, high reliability and large communication coverage. In this novel, we survey how the 5G technology is used in autonomous vehicles. Mainly we review the architecture of 5G, V2X communication and its uses. And then we discuss mobile network evolution. we also provide the advantages of 5G technology in autonomous vehicles and 5G design principles. This paper also outlines how 5G technology is used in Artificial Intelligence (AI). Finally, for a future generation, there is predictable to pay more attentions and effort into developing 5G V2X services.
In this paper study about Hyper-Dimensional Computing (HDC) for different applications. The architectural supports energy-efficient, fast, and scalable search operations using three generally utilized design methodologies. To grasp underlying circumstances and interact with their settings, many Internets of Things (IoT) apps rely on human action recognition. Machine learning is a popular technique for identifying actions based on sensor data. The application of a brain-inspired hyperdimensional computing technology to because IoT systems have a hard time recognizing activities, making activity detection easier is a good solution. can improve accuracy and efficiency. The model training was sped up by up to 468 times when compared to advanced training of Neural network.
A “Finite Impulse Response (FIR)” filter’s impulse response has a finite period. Higher order FIR filter is used in numerous “Digital Signal Processing (DSP)” applications to attain accurate frequency specifications. With increasing filter length, there is a linear increase in the number of additions and multiplications, increasing computing complexity. This paper discusses a variety of FIR filter implementation techniques. The FIR filters are used to reduce the number of arithmetic operations required for inner product calculations is a predetermined number, whilst the “look up table (LUT)” design stores the pre-computed result to keep things simple. Filters are commonly used in a variety of applications; the end goal of using a filter is to create a form of frequency selectivity on the spectrum of the incoming signal. Any DSP subsystem’s FIR filter is regarded as one of the most important components. The major purpose of this project is to briefly examine numerous design strategies in order to aid future development.
Energy conversion, high efficiency, and longer product life batteries are required to allow the strong enabling and efficient integration of battery technology into our society. Lithium-ion cells are used for the formation of battery packs due to their good characteristics provided for a long time. In manufacturing industries, battery packs are produced at a massive rate. Pouch cell capacity inside the pack has to be balanced to make the output of the battery stable and efficient. Sorting pouch cells became a critical task to build a battery pack. So, the pouch cells have to be sorted into groups based on their capacity. A methodology was proposed in this paper for sorting pouch cells using the hybrid clustering algorithm. Results were compared on various clustering algorithms. Optimal clustering was selected by analyzing the results of the machine learning model.
The quick growth of easy communication technologies concluded the last several decades has led in the establishment of strict standards for the functioning of productive systems. The system execution is improved by reducing computation time with the “Residue Number System (RNS)”. It is extensively castoff in “signal processing” “numeral analysis”, and “cryptoanalysis”, and an exact graph-based technique for designing perfect converters from binary framework to RNS to “Quadratic RNS (QRNS)” as well as, on the other hand, employing complete adder as the primary building blocks are shown. The measured adder is a critical component of the RNS system. In this work, it tries to summarized possible prospect of converters by using RNS adder and QRNS adders.
Drowsy driving is one of the leading causes of traffic accidents all over the world. Driving in a monotonous manner for an extended amount of time without stopping causes tiredness and catastrophic accidents. Drowsiness has the potential to ruin many people’s lives. As a result, a real-time system that is simple to create and configure for early and accurate sleepiness detection is required. In this study, a real-time vision-based system called Driver Drowsiness Detection System has been developed utilizing machine learning. In this study, the Haar Cascade classifier was used to recognize the driver’s face characteristics and functions present in OpenCV library to detect the region of the face. The following step is to examine the open/close state of the eyes, followed by sluggishness depending on the sequence of ocular conditions. The non-intrusive and cost-effective nature of this vision-based driver tiredness detection is its distinguishing attribute.
Using virtual reality as an asset and a part of our future is discussed in this article. You can experience, feel, and even touch the past, as well as the present, and future, through it. As a means of creating our own reality, it serves as a medium for us to do so. A virtual reality experience could include everything from developing a video game to a virtual walk across the world, or even a virtual walk through our own ideal house. The most terrifying and gruelling experiences can be experienced in a safe and educational manner with virtual reality. As a result of our research, we’ve compiled this report on everything we’ve learned about virtual reality so far. It covers everything from levels of immersion to the components that go into creating a virtual environment, as well as the history, present, and future of the technology. As a result, both the technologies themselves and the prices and logistics required to implement them have a variety of restrictions. Nevertheless, these new technologies have their own set of problems, which is why we conclude this study with a number of fresh ways and possibilities for future researchers who want to apply these new technologies to education.
The Covid-19 pandemic has caused severe economic depression and has disrupted the supply chains of various industries. The automobile industry which contributes significantly to the Indian economy was gravely hit due to the lockdowns, semiconductor shortage and the uncertainty associated with the pandemic. This research paper analyses the effect of Covid-19 on the automobile sales in India using the time series modelling approach. The data recorded by SIAM from 2012 to 2019 was used to develop the Autoregressive Integrated Moving Average (ARIMA) model following the Box-Jenkins methodology. ARIMA model (2, 1, 3) was chosen as it had the lowest AIC and BIC criteria. This model was used to forecast the sales from 2020 to 2021 to give a picture of the expected automobile sales had the pandemic not occurred. The forecasted data from the model developed has then been compared with actual automobile sales data during the pandemic to gauge the level of impact Covid-19 had on the Indian automobile industry. The paper also explores the associated challenges that the automobile industry had to face due to the pandemic.
An ideal vibration isolation system should have low transmissibility, extensive isolation range, and stiffness to support any static load. Isolators are designed and developed for various applications where vibrations in the system become a bane to it, we find that most of the isolators use rubber as an electrostatic element while other designs also adopt wire rope and springs. The spring-type isolators are metallic and offer better flexibility in addition to the longevity of their performance as rubber loses its elasticity over the period. Helical springs are extensively used in many applications where the isolator is intended to perform for longer periods. In this paper, we present a detailed design and analysis of the metallic spring conical isolator that finds its application in airborne systems.
Electric discharge machining is extensively used to process difficult to cut materials such as superalloys, composite materials etc. The quality of machining can be improved by adding particles into the dielectric fluid which is commonly known as powder mixed electric discharge machining (PMEDM). In the present study, titanium and graphite powder are added to dielectric fluid and the effects of the powders have been analysed on material removal rate, surface roughness, microhardness and surface morphology analysis. Six experiments have been conducted considering each powder with three different levels of pulse current 4A, 8A and 12A. The analysis of the result indicates MRR, surface roughness and microhardness significantly increase with the increase of pulse current. Titanium mixed EDM improves the MRR and microhardness but reduces the surface quality. The SEM investigations elucidate that the samples machined with Titanium powder mixed with EDM have fewer surface defects, cracks, microholes and layer deposition compared to the sample machined with Graphite powder.
Heart disease has become a common cause of death worldwide in recent years. People’s way of living changes, dietary habits, office working cultures, and other factors have all played a role in this worrisome problem around the world. The best way to stop this disease is to develop a method that will detect early symptoms and hence save more lives. With the help Machine Learning (ML) algorithms, researchers can predict the likelihood of developing cardiovascular disease in people who are at risk. It is critical to develop a precise and dependable technique to have early disease prediction by automating the task and therefore achieving efficient disease management. Several academics have described their efforts to develop the best feasible technique for predicting heart disease in previous publications. The goal of this study is to compare alternative algorithms for predicting cardiac disease. The results of important data mining techniques are presented in this work, which can be utilized to construct a highly efficient and accurate prediction model that will aid doctors in minimizing the number of people killed by heart disease. This study compares the metrics for prediction of heart disease for 6 ML algorithms which are “Logistic Regression” (LR), “Decision Tree” (DT), “Random Forest” (RF), “Support Vector Machine” (SVM), “Gaussian Naïve Bayes” (GNB) and “k-Nearest Neighbor” (kNN).
This paper studies the nucleation and growth of vertical carbon nanotube array on 2-dimensional graphene and elaborates the impact of plasma on the same. It observes an analytical model of growth of Carbon Nanotubes (CNT) on the wonder material, Graphene has been developed. The model includes the growth of the CNTs on graphene Plasma Enhanced Chemical Vapour Deposition (PECVD) technique where the plasma consists of hydrocarbon and hydrogen radicals and neutral molecules using the gas mixture of Argon, Hydrogen and suitable hydrocarbons. Further, using MATHEMATICA, the paper examines various trends such as relationship between radius of catalyst particle and time for different RF powers, the time variations of thickness of Graphene sheet for different RF powers, etc. It also discusses the results obtained after running the equations, with the results obtained experimentally. Such hybrids are crucial for making materials with a diverse set of wondrous electrical, chemical, physical and optical properties.
The study aims to examine the effect of environment and economic variables on logistic performance in India. In order to study the long run and short run association between the variables the study employed auto regressive distribution lag (ARDL) approach on a time series data from 2007 to 2018. The result revealed that foreign direct investment (FDI) has a positive relation with LPI whereas fossil fuel consumption in both the short and long run has a negative relation. On the other hand, GDP per capita has a negative relation with LPI while total greenhouse gas emissions has a positive relation, which is a sign of concern for environment sustainability. In the recent report published by the World Bank India’s rank has slipped down from 35th to 44th position worldwide whereby all the six dimensions have shown a downward trend. India being one of the largest customer oriented market would negatively affect the world economy if its logistics operation are poorly driven. This study highlights few reason why India lacks behind in its logistics performance and provide suggestion how India can improve its logistic operation at global level.
This study aims to develop a simulation-based research to evaluate the influence of primary operational conditions on the efficiency of compression molding in processing thermoplastic parts in a mold design with no air venting. Moldflow Insight has been used to simulate the compression molding process to assess the quality of a range of semi-crystalline and amorphous thermoplastic products, to study the performance efficiency of the said mold. Simulation results were obtained by varying the mold surface temperature, melt temperature, charge geometry and mesh element size during processing thermoplastics like polypropylene, polyethylene, polystyrene, and polycarbonate. The findings showcased air entrapment at the corners of the mold cavity, which lowered the process efficiency when the mold operated with no venting under varied processing conditions. Simulation results indicated a need to introduce air vents on the mold’s parting line. The findings revealed that the compression cycle time and waste generation could be diminished when air vents were included in the mold design and optimal operating conditions were used. Hence, a compression molding mold design incorporating air vents in areas of high air entrapment was developed.
Image Captioning has gained tremendous spotlight in recent years. However, the captioning models generate captions in English language. In this paper, we present an image caption generator for our regional language that is Hindi using Resnet50 and LSTM with attention module. An experimental study is shown highlighting the effect of attention-based learning on generated Hindi captions. Flickr8k dataset in Hindi language is used to validate the performance of proposed work in terms of BLEU score.
One of the major problems faced by the oil industry is the problem of unwanted water production. High rates of unwanted water production in a well can make the well uneconomical and reduces the good lifespan. The paper studies the problems faced by a field experiencing a large amount of unwanted water production in the majority of its wells. The data gathered from one of the Indian Western Offshore Oil Fields have been analyzed to identify the problems faced in several wells. Also, the initiatives taken by the company to control high water cut has been discussed. Understanding the water shut-off methods used for mitigating the problem of high water cut and their efficiency, the availability of various Inflow Control Systems for well completion to prevent unwanted water production is studied. Studying the performance of these systems from numerous case studies and literature surveys for mitigating unwanted water production, the paper provides a complete strategy for water control in horizontal wells for different reservoir properties and for future redevelopment plan of the Indian Western Offshore Field followed by the conclusion.
Adaptive beamforming has been studied extensively from a simulation point of view. While existing works compare various techniques based on their simulation output performance, their emulation on hardware systems and the prerequisite analysis of firmware viability remain relatively unexplored. The work presented in this paper addresses two issues. One is the firmware implementation of adaptive beamforming and the analysis of the Hardware Description Language implementation of the Least Mean Squares (LMS) Algorithm. It begins with the development of the algorithm on MATLAB Simulation Environment and proceeds to the synthesis of Verilog code for the same on Xilinx’s Vivado platform. The optimization of the LMS algorithm has been presented as a detailed case study for the successful reduction of the number of hardware resources utilized by a synthesized design on a target Artix-7 FPGA board.
Fractional order Chebyshev high pass filters have been designed in this paper for orders (1+α), (2+α) and (3+α), α representing the fractional component. The work in this paper has been done by using the nature-inspired evolutionary metaheuristic technique called particle swarm optimization. MATLAB simulations for these filters have been shown with α varying from 0.1 to 0.9 at a step of 0.1. The attenuation values are compared with the ideal values. The canonical forms of the (1+α) order filters have been realized using OTA based KHN filter. Circuit element values are calculated for orders 1.2, 1.5 and 1.8 using factional order capacitors. These capacitors are approximated as Foster 1 form.
In this paper studied artificial intelligence (AI). Because AI is a living, evolving system, it has become an integral part of our daily routines. Because of this, no two people or robots are the same. Artificial intelligence (AI) has grown steadily in the previous few years, affecting a wide range of industries like machine learning, robotics, and neural networks, among others. The intelligence displayed by computers is known as artificial intelligence. The term artificial refers to something that is neither natural nor man-made, whereas intelligence refers to the capacity to learn and use information and skills. Artificial intelligence has a wide range of research areas, each with a distinct set of implementation approaches and implications. It is the goal of this paper to provide an overview of artificial intelligence (AI) and the various applications and research areas that surround it. It contributes to our automation, which opens up a world of opportunity and a promising future.
This research report showcases various data mining (DM) techniques such as Classification, Regression, and Clustering in brief and also discusses the aptest framework method for the healthcare industry, CRISP-DM. This report also explores the various data mining applications in the healthcare industry. DM is utilized to extract the data from a lot of information. DM includes two models, predictive and descriptive. Classifying data is to form classes either with the final objective of learning new antiques or searching new areas. This is why specialists have for many years tried to locate the enshrouded examples in the knowledge that can be classified and contrasted as well as other concepts which are the result of common principles.
The Hyperloop vehicle is a means of transport in which the pod advances at a fast velocity inside a low-pressure tube to reduce the coefficient of drag. Aerodynamic drag is one of the most significant elements to consider while studying such systems. The design was created on ANSYS SpaceClaim and it consists of a Two-Dimensional model with tapered ends to diminish the drag. Since both ends are tapered, the drag is reduced significantly as compared to the other designs. In this paper, the pod design is analyzed at different speeds, namely 60 ms-1, 120 ms-1, 180 ms-1, 260 ms-1, and 300 ms-1 on ANSYS FLUENT and numerical simulations were performed keeping the blockage ratio fixed. Appropriate conditions, equations and k-epsilon turbulence model were applied to produce the desired result. All the drag coefficient values were analyzed, and hence, the graph was plotted. It was observed that the drag coefficient of the pod decreases with an increase in velocity.
In this paper study for users have unquestionably profited from the rapidly changing paradigm of cloud computing. It employs virtual machines instead of actual equipment to host, store, and network multiple components, and it charges per use. As the amount of data being stored grows, so does the importance of load balancing as a research area. One of the most difficult problems in cloud computing is distributing the ever-changing burden among the nodes in an efficient manner known as load balancing. There are numerous load balancing methods in use today that considerably improve the efficiency and management of resources. We use cloud analyst as a simulator in this article to compare techniques with a single broker policy that helped boost cloud and related application performance.
Tube flaring process involves a conical tool of a certain length which is displaced to get an end flared tube, which is utilized to form a tight seal between pipes or tubes. Tube flaring refers to a kind of forging which is often a cold working operation. The tube forming process is widely used in several industries to form condenser pipes, car seat structures, exhaust piping, etc. Thin-walled tubes were used in automobiles to reduce the total weight for better performance. A conical tool is used for tube end forming in which the tool is moved into the tube known as conventional tube flaring. In this work, a numerical study was directed using FEA software ANSYS/Implicit which is used to analyze the stress conditions involved in tube flaring. Here, the tool is considered rigid and displaced by using displacement control by 25 mm, and other parameters were studied at this displacement. Effects of different semi-cone angles were also considered for the study.
This paper provides a comparative evaluation of different topologies of Vienna rectifiers. Vienna rectifier is used for AC-DC conversion and has a reduced number of switches. The PWM-based control mechanism of the rectifier reduces the losses of the capacitor and provides better efficiency. There is a different type of topologies for Vienna rectifiers and this paper evaluates different topologies. Three-phase converters are available in a number of different topologies. The major advantage of a three-level power electronic converter is the reduction in the number of switches required, as well as the reduction in overall harmonic distortion, voltage stress, and AC side voltage ripple. The Vienna rectifier is one of the most effective devices for unidirectional power flow and boosts type power factor correction (PFC) circuits. The Vienna rectifier is modular in design, but its space and weight are restricted.