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Introduction
Published in Chinmay K. Maiti, Fabless Semiconductor Manufacturing, 2023
Industry 4.0 is a German government initiative that encourages the fourth generation of manufacturing by incorporating concepts like cyber-physical structures, virtual copies of actual equipment and processes, and decentralized decision-making to build a smart factory [85]. Industry 4.0 aims to resolve the dynamic and demanding manufacturing landscape characterized by a wide range of demands, short order lead times and product life cycles, restricted capacities, and increasingly complex process technologies. A smart manufacturing ecosystem built on Industry 4.0 technologies, like advanced automation, AI, ML, big data analytics, and IoT, can monitor and optimize manufacturing processes in real-time in a variety of ways, from high-level strategic resource and production planning to real-time equipment-level smart dispatching and predictive maintenance. Smart manufacturing and Industry 4.0 offer major cost-cutting, productivity-boosting, and quality-improving prospects in microelectronics manufacturing. However, it also poses fresh and special obstacles to the industry. Deep learning models with process conditions and doping profile/electrical characteristics as input and output are trained using AI algorithms that can characterize the semiconductor domain. Emerging technologies, such as the IoT [86, 87], wireless sensor networks [88, 89], big data [90], cloud computing [91, 92], embedded systems [93], and mobile internet [94], are being integrated into the semiconductor manufacturing, ushering in the fourth industrial revolution.
An Effective E-Learning Mechanism to Meet the Learning Demand of Industry 4.0
Published in G. Rajesh, X. Mercilin Raajini, Hien Dang, Industry 4.0 Interoperability, Analytics, Security, and Case Studies, 2021
A. Jaya, P. Sheik Abdul Khader, A. Abdul Azeez Khan, K. Javubar Sathick, L. Arun Raj, Ho Chiung Ching
In the era of Industry 4.0, the technologies drive towards the automation of manufacturing processes. To accomplish the automation process, knowledge transfer plays a major role among the people. For any kind of upgradation of their skills & knowledge, they need an independent learning system. To learn any kind of new technology, there are a lot of video lectures available on the Web. There is no standard procedure to check the quality of video lectures. Due to this, the learners either may be dissatisfied or may not possess the required knowledge or skills. There is a need for a system to improve the quality of the content and content providers. The level of learners may vary from person to person, which requires a different level of lecture delivery. Due to the long lockdown period of Coronavirus, e-learning plays a major role in the academic structure. E-learning is the transfer of skills and knowledge through computers, and learners can learn anywhere in the world at any time. Their feedback is vital for maintaining the quality of the video control. Many academic institutions have implemented a learning management system (LMS) to support teaching and learning processes for the students too. The role of e-learning plays a vital role in technology upgradation in the Industry 4.0.
Application of technology
Published in Mike Tooley, Engineering GCSE, 2012
Automation has been widely introduced into the engineering industry and can be defined as the use of mechanical and electrical/electronic systems to carry out processes and functions without requiring direct human control. Automation is often associated with the use of robots but it can also be applied to manufacturing processes that operate under computer (programmed) control. Our first case study introduces a particular example of automation in the development of remotely operated vehicles (ROVs).
An efficient industry 4.0 architecture for energy conservation using an automatic machine monitor and control in the foundry
Published in Automatika, 2022
M. Dinesh, C. Arvind, K. Srihari
In recent days, the automation of equipment or machinery is preferred in industry to speed up the production in an eco-friendly manner and to increase its efficiency. However, the foundry environment and its automation are at an elementary stage. Industry 4.0 is envisioned to evolve a smart factory supported by the Internet of things and computing systems. It conceptualizes a dynamic scenario in industrial automation for data exchange for cost-effective monitoring and control mechanisms. It should be a robust mechanism that ensures the healthy condition of the machinery and its components. The heart of the revolutionary Industry 4.0 is the machine monitoring mechanism, which is mandatory in any real-time industrial environment. Industry 4.0 ensures (i) precautionary measures are taken in hazardous areas during machine operation, (ii) preventive maintenance to avoid sudden equipment failure, (iii) long life for machinery and its components and (iv) less manpower and cost-effectiveness in industrial process. Similarly, one such real-time scenario is evident in industrial electronics; moreover, in the automation industry, monitoring and controlling the equipment or machinery parameters without human intervention is desired. Parameter or data-driven monitoring and controlling are mandatory in any automation process, and many researchers have contributed to the evolution of an environment-friendly industrial automation process.
Modelling the interrelationship between factors for adoption of sustainable lean manufacturing: a business case from the Indian automobile industry
Published in International Journal of Sustainable Engineering, 2020
Naveen Kumar, K. Mathiyazhagan, Deepak Mathivathanan
Supply chain management (SCM) plays a vital role in implementing sustainable manufacturing and to improve organisation performance (Shi, Wu, and Tseng 2017; Wu et al. 2016) in relation to the dynamic growth and development encouraged by the automobile industry particularly the small- and medium-sized enterprises (SMEs). SMEs are having more than 90% market share of the total automobile industry and exploring 6% employment, arguably the strongest part of economies worldwide IEA (2015). In India, the automobile industry has maximum share in the overall nation’s growth rate. Reportedly, 7.1% growth has been calculated in total country’s Gross domestic product (ET Auto Report). The automobile industry in India is the world’s 4th largest manufacturer of cars and 7th largest manufacturer of commercial vehicles in 2018. Two-wheeler segments are leading with 80% market share in the Indian automobile market by having a focus on young population. Passenger vehicle segment and commercial vehicle segment are having growth rate of around 16.2% and 4.99%, respectively, over the last financial year. Government initiatives are playing a major role in the growth of automobile leaders and expected to make India a leader in the 2 W and 4 W market by 2020 (Media Reports like Society of Indian automobile manufacturer (SIAM), Confederation of Indian industries (CII), etc.). The report shows the significant contribution of the automobile industry in the Indian economic growth.
Manufacturing system sustainability through lean and agile initiatives
Published in International Journal of Sustainable Engineering, 2019
Vinay Venugopal, P. G. Saleeshya
The wide extensive literature review, a field study conducted helped us to identify an industry to do the case study. Industry selected for the study is an Ayurveda Pharmaceutical industry – situated in South India. In this industry, a need was felt to improve the operational efficiency. For this, the strategies such as leanness and agility are identified suitable. Also, sustainability is crucial in the field of Ayurveda pharmaceutical industry. The products should be manufactured with accurate precision and quality so that it should not be harmful to human beings. We have developed a questionnaire to capture information from the industry. The industry produces 100 types of drugs which involve arishtas, asavas, ghritham, kashayam, choornam, lehyam, gulikas, oils and thylams. The industry follows Good Manufacturing Practices (GMP) which is designed to minimise the risks involved in any pharmaceutical production that cannot be eliminated through testing the final product. It follows batch production to convert a set of raw materials into drugs through stage by stage over a series of workstations. The raw materials should be mixed and processed as per specifications so that the drug does not have any side effects to both human being and environment. For doing the pairwise comparison of enablers and inhibitors we have conducted a brainstorming section including managers from different hierarchical levels. A combined lean and agile manufacturing model is developed with the pharmaceutical industry under consideration. Lean manufacturing practices can be applied in the industry to reduce the non-value added activities and eliminate different causes of wastes. Agile manufacturing practices can be applied to respond to the dynamic requirements of the customers and to sustain in the market. Both practices contribute to the sustainability of the system Singh et al., (2016). The details of the model are explained in the next section.