International Journal of Research Publication and Reviews

Metaverse, Asia Pacific Academy of Science Pte. Ltd. (APACSCI), 2024

Artificial intelligence (AI) stands as a potent catalyst for revolutionizing manufacturing, promising unprecedented efficiency, agility, and resilience. This research embarks on an investigative journey to dissect the multifaceted landscape of AI in manufacturing, aiming to unravel its current status, intrinsic challenges, and prospective pathways. This research unveils the intricate relationship between AI technologies and manufacturing processes across diverse domains. Examining various domains, including system-level analysis, human-robot collaboration, process monitoring, diagnostics, prognostics, and material-property modeling. The research also reveals AI’s transformative potential in optimizing manufacturing operations, enhancing decision-making, and fostering innovation. By dissecting each domain, the research illuminates how AI empowers manufacturers to adapt to dynamic market demands and technological advancements, ultimately driving sustainable growth and competitiveness. Moreover, it also examines the evolving dynamics of human-robot collaboration within manufacturing settings, recognizing AI’s pivotal role in facilitating seamless communication, shared understanding, and dynamic adaptation between humans and machines. Through an exploration of AI-enabled human-robot collaboration, this research underscores the transformative power of symbiotic relationships in reshaping the future of manufacturing. While highlighting opportunities, it acknowledges the myriad challenges accompanying AI integration in manufacturing, such as data quality issues, interpretability of AI models, and knowledge transfer across domains. By addressing these challenges, the research aims to pave the way for more resilient AI-driven manufacturing systems capable of navigating complex market landscapes and technological disruptions. This research sheds light on AI’s transformative potential in manufacturing, inspiring collaborative efforts and innovative solutions that will propel the industry forward into a new era of possibility and prosperity.

Journal of Ubiquitous Systems and Pervasive Networks, 2021

Production activities is generating a large amount of data in different types (i.e., text, images), that is not well exploited. This data can be translated easily to knowledge that can help to predict all the risks that can impact the business, solve problems, promote efficiency of the manufacture to the maximum, make the production more flexible and improving the quality of making smart decisions, however, implementing the Smart Manufacturing(SM) concept provides this opportunity supported by the new generation of the technologies. Internet Of Things (IoT) for more connectivity and getting data in real time, Big Data to store the huge volume of data and Deep Learning algorithms(DL) to learn from the historical and real time data to generate knowledge, that can be used, predict all the risks, problem solving, and better decision-making. In this paper, we will introduce SM and the main technologies to success the implementation, the benefits, and the challenges.

IEEE Transactions on Emerging Topics in Computing, 2022

Sustainability

Adaptation and innovation are extremely important to the manufacturing industry. This development should lead to sustainable manufacturing using new technologies. To promote sustainability, smart production requires global perspectives of smart production application technology. In this regard, thanks to intensive research efforts in the field of artificial intelligence (AI), a number of AI-based techniques, such as machine learning, have already been established in the industry to achieve sustainable manufacturing. Thus, the aim of the present research was to analyze, systematically, the scientific literature relating to the application of artificial intelligence and machine learning (ML) in industry. In fact, with the introduction of the Industry 4.0, artificial intelligence and machine learning are considered the driving force of smart factory revolution. The purpose of this review was to classify the literature, including publication year, authors, scientific sector, country, in...

International Journal of Science and Research Archive, 2025

Industry 4.0, also known as the Fourth Industrial Revolution, represents a transformative shift in manufacturing, driven by the integration of digital technologies, automation, data exchange, and the Internet of Things (IoT). This paradigm shift is reshaping traditional manufacturing processes by fostering interconnected, smart systems that enable real-time monitoring, predictive analytics, and enhanced operational efficiency. Through the adoption of cyber-physical systems, cloud computing, advanced robotics, artificial intelligence (AI), and big data analytics, manufacturers can optimize production processes, reduce costs, improve quality, and create flexible, agile production environments. The concept of smart manufacturing, a core component of Industry 4.0, revolves around the seamless interaction between machines, systems, and human operators to enhance decision-making and drive innovation. This paper explores the key technologies, benefits, and challenges associated with Industry 4.0, highlighting its potential to revolutionize the manufacturing landscape and pave the way for more sustainable, efficient, and customized production. The shift toward smart manufacturing presents an opportunity for manufacturers to not only enhance competitiveness but also to address the challenges of an increasingly complex global market.

Al-Bahir Journal for Engineering and Pure Sciences

In human culture, machine learning has long been used to address complicated issues. Machine learning is successful because of the help provided by computational power and sensing technology. Data-driven strategies and the development of artificial intelligence will soon have a significant impact on the industry. Common examples include search engines, picture recognition, biometrics, speech and handwriting recognition, natural language processing, as well as medical diagnostics and credit scores. It is obvious that when artificial intelligence permeates our globe and, more precisely, our lives, numerous challenges will become public. According to predictions, Industry 4.0 or Smart Manufacturing will be the next Industrial Revolution. It all has to do with technology connectivity and improvements in the contextualization of data, as with many other advancements in recent years. Smart, however, cannot be realised without either the support of intelligent systems or the support of data science technologies.

International Journal of Production Research , 2018

Manufacturing has evolved and become more automated, computerised and complex. In this paper, the origin, current status and the future developments in manufacturing are disused. Smart manufacturing is an emerging form of production integrating manufacturing assets of today and tomorrow with sensors, computing platforms, communication technology , control, simulation, data intensive modelling and predictive engineering. It utilises the concepts of cyber-physical systems spearheaded by the internet of things, cloud computing, service-oriented computing, artificial intelligence and data science. Once implemented, these concepts and technologies would make smart manufacturing the hallmark of the next industrial revolution. The essence of smart manufacturing is captured in six pillars, manufacturing technology and processes, materials, data, predictive engineering, sustainability and resource sharing and networking. Material handling and supply chains have been an integral part of manufacturing. The anticipated developments in material handling and transportation and their integration with manufacturing driven by sustainability, shared services and service quality and are outlined. The future trends in smart manufacturing are captured in ten conjectures ranging from manufacturing digitisation and material-product-process phenomenon to enterprise dichotomy and standardisation.

Discover Artificial Intelligence, 2023

Driven by the ongoing migration towards Industry 4.0, the increasing adoption of artificial intelligence (AI) has empowered smart manufacturing and digital transformation. AI enhances the migration towards industry 4.0 through AI-based decision-making by analyzing real-time data to optimize different processes such as production planning, predictive maintenance, quality control etc., thus guaranteeing reduced costs, high precision, efficiency and accuracy. This paper explores AI-driven smart manufacturing, revolutionizing traditional approaches and unlocking new possibilities throughout the major phases of the industrial equipment lifecycle. Through a comprehensive review, we delve into a wide range of AI techniques employed to tackle challenges such as optimizing process control, machining parameters, facilitating decision-making, and elevating maintenance strategies within the major phases of an industrial equipment lifecycle. These phases encompass design, manufacturing, maintenance, and recycling/ retrofitting. As reported in the 2022 McKinsey Global Survey (. mckin sey. com/ capab iliti es/ quant umbla ck/ our-insig hts/ the-state-of-ai-in-2022-and-a-half-decade-in-review), the adoption of AI has witnessed more than a two-fold increase since 2017. This has contributed to an increase in AI research within the last six years. Therefore, from a meticulous search of relevant electronic databases, we carefully selected and synthesized 42 articles spanning from 01 January 2017 to 20 May 2023 to highlight and review the most recent research, adhering to specific inclusion and exclusion criteria, and shedding light on the latest trends and popular AI techniques adopted by researchers. This includes AI techniques such as Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), Bayesian Networks, Support Vector Machines (SVM) etc., which are extensively discussed in this paper. Additionally, we provide insights into the advantages (e.g., enhanced decision making) and challenges (e.g., AI integration with legacy systems due to technical complexities and compatibilities) of integrating AI across the major stages of industrial equipment operations. Strategically implementing AI techniques in each phase enables industries to achieve enhanced productivity, improved product quality, cost-effectiveness, and sustainability. This exploration of the potential of AI in smart manufacturing fosters agile and resilient processes, keeping industries at the forefront of technological advancements and harnessing the full potential of AI-driven solutions to improve manufacturing processes and products.

Mukt Shabd Journal Volume XIV, Issue I, JANUARY/2025 ISSN NO : 2347-3150, 2025

The rapid evolution of Artificial Intelligence (AI) has revolutionized various sectors, with smart manufacturing being at the forefront of its transformation. Predictive maintenance, powered by AI, plays a pivotal role in maximizing economic gains and ensuring sustainability in manufacturing operations. This paper explores the

International Journal of Recent Technology and Engineering (IJRTE), 2019

This paper discusses the basic concept Machine learning and its techniques, algorithms as well the impact of Machine Learning in Manufacturing processes and Industrial Production. There has been an unprecedented increase in the data available in the last couple of decades. This has enabled machine learning to be applied in various fields. Machine learning is field of study which enables the computer system to learn automatically as well as improve from experience and perform various tasks without explicit instructions. The primary aim of machine learning is to allow the computers learn automatically without human assistance or intervention and adjust actions accordingly It is being employed in many fields. Manufacturing one area where machine learning is very useful. This paper discusses the various areas where machine learning can improve the process of manufacturing like predictive maintenance, process optimisation, quality control, scheduling of resources among others. This can b...