Artificial Intelligence In Manufacturing Research Articles

Explainable AI in Manufacturing and Industrial Cyber–Physical Systems: A Survey

This survey explores applications of explainable artificial intelligence in manufacturing and industrial cyber–physical systems. As technological advancements continue to integrate artificial intelligence into critical infrastructure and industrial processes, the necessity for clear and understandable intelligent models becomes crucial. Explainable artificial intelligence techniques play a pivotal role in enhancing the trustworthiness and reliability of intelligent systems applied to industrial systems, ensuring human operators can comprehend and validate the decisions made by these intelligent systems. This review paper begins by highlighting the imperative need for explainable artificial intelligence, and, subsequently, classifies explainable artificial intelligence techniques systematically. The paper then investigates diverse explainable artificial-intelligence-related works within a wide range of industrial applications, such as predictive maintenance, cyber-security, fault detection and diagnosis, process control, product development, inventory management, and product quality. The study contributes to a comprehensive understanding of the diverse strategies and methodologies employed in integrating explainable artificial intelligence within industrial contexts.

Application of Artificial Intelligence to Improve Production Process Efficiency in Manufacturing Industry

The rapid advancement of Artificial Intelligence (AI) has profoundly impacted the manufacturing industry, offering transformative potential to enhance production process efficiency. This paper presents a systematic literature review of AI applications in the manufacturing sector, focusing on key AI technologies such as machine learning, robotics, predictive analytics, and natural language processing. The review highlights how these technologies have improved quality control, resource management, and overall operational performance. However, the adoption of AI also presents challenges, including significant investment costs, the need for a skilled workforce, and concerns over data security and privacy. Despite these challenges, the integration of AI in manufacturing presents numerous opportunities for future research and innovation, particularly in the areas of sustainable manufacturing and the convergence of AI with other emerging technologies. This study concludes that while AI offers substantial benefits for production efficiency, its successful implementation requires careful strategic planning and investment in both technology and human resources.

Industry 5.0 in Manufacturing: Enhancing Resilience and Responsibility through AI-Driven Predictive Maintenance, Quality Control, and Supply Chain Optimization

This integrative literature review investigates the transformative impact of artificial intelligence (AI) on manufacturing, focusing on AI-driven predictive maintenance, machine learning-based quality control, and AI-driven supply chain optimization. By examining current literature, the study highlights AI's potential to automate and revolutionize manufacturing operations, enhancing efficiency, resilience, and transparency. The study's conceptual framework is grounded in three primary pillars: AI-driven supply chain optimization, predictive analytics, and machine learning-based quality control, each contributing to the overall enhancement of manufacturing efficiency, resilience, and transparency. The methodology involves a comprehensive review of scholarly articles, reports, and academic publications, focusing on AI applications in predictive maintenance, quality control, and supply chain optimization. The analysis reveals significant improvements in operational efficiency and resilience due to AI, alongside concerns about biases, transparency, and implementation issues. The findings confirm AI's transformative potential in manufacturing but emphasize the necessity for ongoing supervision, regular audits, and the development of AI models capable of detecting and rectifying operational anomalies. The study proposes creating jobs such as AI Manufacturing Oversight Officer (AIMOO), AI Manufacturing Compliance Officer (AIMCO), and AI Manufacturing Quality Assurance Officer (AIMQAO) to ensure responsible AI utilization, maintaining the integrity and efficiency of manufacturing operations while addressing implementation challenges. The review concludes that AI is promising for transforming manufacturing; however, careful implementation is crucial to uphold operational integrity and resilience. Future research should prioritize longitudinal studies to evaluate AI's long-term impact, focus on addressing implementation concerns, and ensure fair and transparent integration of AI technologies. These findings have significant implications for practice and policy, underscoring the need for robust frameworks and regulatory measures to guide the effective use of AI in manufacturing.

Generative artificial intelligence in manufacturing: opportunities for actualizing Industry 5.0 sustainability goals

PurposeThis study offers practical insights into how generative artificial intelligence (AI) can enhance responsible manufacturing within the context of Industry 5.0. It explores how manufacturers can strategically maximize the potential benefits of generative AI through a synergistic approach.Design/methodology/approachThe study developed a strategic roadmap by employing a mixed qualitative-quantitative research method involving case studies, interviews and interpretive structural modeling (ISM). This roadmap visualizes and elucidates the mechanisms through which generative AI can contribute to advancing the sustainability goals of Industry 5.0.FindingsGenerative AI has demonstrated the capability to promote various sustainability objectives within Industry 5.0 through ten distinct functions. These multifaceted functions address multiple facets of manufacturing, ranging from providing data-driven production insights to enhancing the resilience of manufacturing operations.Practical implicationsWhile each identified generative AI function independently contributes to responsible manufacturing under Industry 5.0, leveraging them individually is a viable strategy. However, they synergistically enhance each other when systematically employed in a specific order. Manufacturers are advised to strategically leverage these functions, drawing on their complementarities to maximize their benefits.Originality/valueThis study pioneers by providing early practical insights into how generative AI enhances the sustainability performance of manufacturers within the Industry 5.0 framework. The proposed strategic roadmap suggests prioritization orders, guiding manufacturers in decision-making processes regarding where and for what purpose to integrate generative AI.

Retrosynthetic Life Cycle Assessment: A Short Perspective on the Sustainability of Integrating Thermoplastics and Artificial Intelligence Into Composite Systems

AbstractOver the past 30 years, the polymer composite industry has flourished, producing advanced structural materials for the aviation, energy, and transportation sectors. However, the use of crosslinked thermoset matrices has been linked to significant end‐of‐life challenges, presenting a critical issue for the industry. Moreover, the industry is characterized by numerous labor‐intensive processes. In alignment with Industry 4.0 principles, two major routes have been identified to enhance sustainability: the utilization of high‐performance thermoplastic matrices and the integration of artificial intelligence in manufacturing. Nevertheless, there are substantial concerns regarding the life cycle assessment of these technologies, which are not accounted for in the initial calculations, including the environmental footprint of polymer synthesis and energy requirements for training AI. This perspective aims to address potential and significant CO2 emissions from chemical feedstocks and the high computing requirements of these new technologies.

A Study on Artificial Intelligence – Driven Inventory Solutions and Enhancing Manufacturing Efficiency

Artificial Intelligence is pivotal in transforming Manufacturing and Inventory management practices and maintaining this delicate inventory balance. AI-driven predictive forecasting, for instance, considers historical data, seasonality, and market dynamics to provide exceptionally accurate demand forecasts. Managing inventory has become a complex puzzle in today’s fast-paced business world. As companies expand their reach and cater to a global audience, tracking inventory and maintaining an accurate “available-to-promise” status in real time has become a challenge. In the current competitive landscape, retailers prioritise a seamless approach to inventory management, especially with the growing importance of omnichannel fulfilment. The art of balancing supply and demand while keeping costs in check has become a high-stakes game where every piece of the inventory puzzle must fit perfectly. In this article, we explore the remarkable role of Artificial intelligence in Manufacturing and Inventory management. We will discuss how businesses can harness the power of AI to optimize their efficiency in Manufacturing and Inventory processes.

Artificial Intelligence in manufacturing: State of the art, perspectives, and future directions

Inspired by the natural intelligence of humans and bio-evolution, Artificial Intelligence (AI) has seen accelerated growth since the beginning of the 21st century. Successful AI applications have been broadly reported, with Industry 4.0 providing a thematic platform for AI-related research and development in manufacturing. This paper highlights applications of AI in manufacturing, ranging from production system design and planning to process modeling, optimization, quality assurance, maintenance, automated assembly and disassembly. In addition, the paper presents an overview of representative manufacturing problems and matching AI solutions, and a perspective of future research to leverage AI towards the realization of smart manufacturing.

Developing a constraint model for using artificial intelligence on existing, limited hardware in manufacturing machines

In the rapidly evolving field of Artificial Intelligence (AI), its application in industrial settings, particularly for anomaly detection in time series data, poses unique challenges. Current methods often lack a comprehensive understanding of the trade-offs involved in achieving optimal model performance, data preparation effort, and prediction quality. To bridge this gap, this paper presents an adaptive approach to address these challenges, focusing on making conscious decisions about mentioned trade-offs. Inspired by the principles of the Iron Triangle from product engineering, our methodology introduces a novel ”AI triangle” with dimensions of Speed, Effort, and Quality. We applied this methodology to a real-world case study involving anomaly detection in a constrained hardware environment in the context of a forming production process. The results highlight the effectiveness of our approach in achieving a practical balance between speed, effort, and quality constraints for implementing AI in an industrial setting. This paper provides valuable insights into the considerations and trade-offs necessary for the successful deployment of AI in manufacturing and other similar industrial applications.

Realising the promises of artificial intelligence in manufacturing by enhancing CRISP-DM

To support manufacturing firms in realising the value of Artificial Intelligence (AI), we embarked on a six-year process of research and practice to enhance the popular and widely used CRISP-DM methodology. We extend CRISP-DM into a continuous, active, and iterative life-cycle of AI solutions by adding the phase of ‘Operation and Maintenance’ as well as embedding a task-based framework for linking tasks to skills. Our key findings relate to the difficult trade-offs and hidden costs of operating and maintaining AI solutions and managing AI drift, as well as ensuring the presence of domain, data science, and data engineering competence throughout the CRISP-DM phases. Further, we show how data engineering is an essential but often neglected part of the AI workflow, provide novel insights into the trajectory of involvement of the three competences, and illustrate how the enhanced CRISP-DM methodology can be used as a management tool in AI projects.

Applied Artificial Intelligence in Manufacturing and Industrial Production Systems: PEST Considerations for Engineering Managers

Presently, artificial intelligence (AI) is playing a leading role in our contemporary world via numerous applications. Despite its many advantages, analytical frameworks highlighting the implications of AI applications are still evolving. Particularly, in manufacturing and industrial production where novel technologies are continuously being harnessed. Consequently, AI and the implications of its applications have relatively remained a grey area for many engineering managers who are key players in the gravitation of manufacturing and industrial production towards the fourth industrial revolution and more recently, the fifth industrial revolution, generally termed as Industry 4.0 (I4.0) and Industry 5.0 (I5.0), respectively. In this study, the implications of AI applications in the general context of manufacturing and industrial production, are presented to provide insight for engineering managers. These implications are discussed via PEST ( <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</u> olitical, <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</u> conomic, <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</u> ocial, and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</u> echnological) considerations of the broad implications of the adoption of AI techniques in manufacturing and industrial production systems. A new engineering management model has not been proposed in this paper. Rather, a discussion aimed at serving as a tool for the appraisal of the implications of the general applications of AI by engineering managers, who may not be AI specialists or data science experts is presented.

ПРЕИМУЩЕСТВА И ПРОБЛЕМЫ ПРИМЕНЕНИЯ ИЗМЕРИТЕЛЬНЫХ СИСТЕМ НА ОСНОВЕ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА

The development of artificial intelligence (AI) technologies opens up broad prospects for solving problems in areas that, until recently, seemed accessible only to humans. This trend is forcing a rethinking of fundamental philosophical questions about the material and the ideal. Improving AIbased measurement systems will lead to mass production of different types of such systems with relatively «weak» AI and with «strong» AI. Together these systems will create changes in society, but they will generate social problems. The article shows the effectiveness of measuring system application based on AI in manufacturing, medicine, transport, for assessing the quality of products and services, as well as determining human abilities, etc. Areas of activities are demonstrated that can accelerate the positive results of the new development stage, delay the emergence of related social problems, and facilitate their resolution.

APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN MANUFACTURING : A REVIEW

In recent days, It’s popularly acknowledged that Artificial Intelligence (AI) is going to play a major role in influencing a large number of fields and is going to be a major source of automation and innovation. In this review paper, we present an analytical and structured literature review of the current avant-garde of AI in manufacturing and its influence. We particularly analyzed and exemplified papers from a holistic manufacturing perspective. This review provides an overall perspective on how Artificial Intelligence has bolstered Manufacturing domains and how contributions over the years have shaped the role AI has played in manufacturing. AI applications are promising and tend to act as a game-changer in tackling hindrances in metallurgy. Future research can be expected towards developing advanced combined AI applications.

Versatile IT-system architecture for smart manufacturing solutions: the example for green manufacturing

ABSTRACT In the last decade, a rising number of opportunities for IT-based solutions in manufacturing have emerged. Often these opportunities are linked to terms like Industry 4.0, Industrial Internet of Things, Cyber-Physical Production Systems or Artificial Intelligence in Manufacturing and address different fields of action to achieve improvements in manufacturing. These include increased productivity, improved energy and resource efficiency, predictive maintenance or quality management support. However, current implementations of corresponding improvement measures often lack a common structure of the underlying IT systems, so that each of the measures is often planned and implemented as a stand-alone solution. Possible consequences include redundancies within the IT infrastructure and software functions as well as high efforts for later extensions. Against this background, an approach for a system architecture to implement different IT-based support measures based on a shared versatile system has been developed in order to gain synergy potentials and foster the reuse of software functions. Furthermore, an approach for the planning and implementation is given. This includes the design of the system architecture as well as the conception and implementation of the IT-based improvement measures. An exemplary application case within a factory for pneumatic valves demonstrates the applicability of the approaches.

Prerequisites for the adoption of AI technologies in manufacturing – Evidence from a worldwide sample of manufacturing companies

In current discussions, Artificial Intelligence (AI) is ascribed great influence on production processes. Research on AI has seen tremendous growth in recent years. However, most of the research has focused primarily on various AI technologies, and less on prerequisites and enablers for adoption of AI at firm-level. This is surprising, given the fact that many companies are still struggling to establish AI in their production and to drive their AI adoption forward.To close this gap, this study analyses the impact of various technological, organizational and environmental (TOE) prerequisites for a successful adoption of AI technologies in manufacturing. Based on a cross-national survey of 655 company representatives from the manufacturing industry, our results contribute to a better understanding of why some companies are more determined than others when it comes to implementing AI in their production. We find evidence that organizational factors, such as digital skills, company size, and R&D intensity, have the greatest impact on the adoption of AI in manufacturing. Furthermore, in order to gain new insights into the interplay of new technology adoption and global production strategies, this paper addresses the question of which factors explain a primarily domestic or globally oriented technology adoption. We find that especially research-intensive, knowledge-based and service-oriented companies tend to roll out AI technologies not only at their domestic but also at their foreign production sites.

Technology mining: Artificial intelligence in manufacturing

The period of the fourth industrial revolution, called Industry 4.0, is characterized by new, innovative technologies such as: Cloud Computing; the Internet of Things; the Industrial Internet of Things; Big Data; Blockchain; Cyber-Physical Systems; Artificial Intelligence, and so on. Artificial Intelligence technology plays a significant role in modern manufacturing, particularly in the context of the Industry 4.0 paradigm. This article offers a visual and a comprehensive study of the application of Artificial Intelligence in manufacturing. Existing scholarly literature on Artificial Intelligence in manufacturing, within the Web of Science Core Collection databases, is examined in two periods: 1979-2010 and 2011-2019. These periods are viewed, respectively, as before and after the emergence of the term Industry 4.0. Bibliometric and content analysis of relevant literature is conducted and key findings are subsequently identified. The results indicate that the most important topics today are: cyber-physical systems and smart manufacturing; deep learning and big data; and real-time scheduling algorithms.

Expert System for the Machine Learning Pipeline in Manufacturing

Abstract Many success stories already exist with regard to the implementation of Machine Learning (ML) and Artificial Intelligence (AI) in manufacturing. However, companies with traditional focus on production technologies face challenges in conducting AI-projects successfully and lack knowledge of which obstacles may occur and how to decide in the implementation phase. In this paper, we develop an approach that focuses on the methodological necessary steps for the successful application of ML and AI in manufacturing. Optimization potentials and decisions to be made are outlined in every step. A main focus is put on optimizing hyperparameters of ML-models as one promising approach for improving overall ML-model performance. An expert system is presented that enables the selection of suitable hyperparameter optimization techniques. The concept is validated based on manufacturing of compressor components of a turbofan engine.

Data-driven bearing fault detection using hybrid autoencoder-LSTM deep learning approach

Artificial intelligence (AI) and its sub-domains of machine learning and deep learning have kindled the interests of both industry practitioners and academicians. Its contribution to the manufacturing industry in making intelligent predictions about a machinery's health and its working has seen a huge surge in the research carried in recent years. Nowadays, AI in manufacturing is popularly applied for the efficient fault detection of machinery using data analytics. Traditional fault predictive classification and further diagnosis have pitfalls such as low prediction accuracy, poor feature extraction and susceptibility to noise. To overcome these disadvantages, this paper proposes the deep-learning-based hybrid autoencoders (AE) - long-short-term memory (LSTM) framework for fault detection. The dimensionality reduction with automatic latent feature extraction by autoencoders and temporal feature consideration by LSTM help to achieve high fault diagnosis accuracy. The empirical results show that fault detection of roll bearings based on the proposed hybrid AE-LSTM deep learning technique achieved superior results in comparison to the traditional K-means clustering technique.

Impact of Artificial Intelligence in Manufacturing and Logistics: an exploratory study

Impact of Artificial Intelligence in Manufacturing and Logistics: an exploratory study

Big data driven jobs remaining time prediction in discrete manufacturing system: a deep learning-based approach

Implementing advanced big data (BD) analytic is significant for successful incorporation of artificial intelligence in manufacturing. With the widespread deployment of smart sensors and internet of things (IOT) in the job shop, there is an increasing need for handling manufacturing BD for predictive manufacturing. In this study, we conceive the jobs remaining time (JRT) prediction during manufacturing execution based on deep learning (DL) with production BD. We developed a procedure for JRT prediction that includes three parts: raw data collection, candidate dataset design and predictive modelling. First, the historical production data are collected by the widely deployed IOT in the job shop. Then, the candidate dataset is formalised to capture various contributory factors for JRT prediction. Further, a DL model named stacked sparse autoencoder (S-SAE) is constructed to learn representative features from high dimensional manufacturing BD to make robust and accurate JRT prediction. Our work represents the first DL model for the JRT prediction at run time during production. The proposed methods are applied in a large-scale job shop that is equipped with 44 machine tools and produces 13 types of parts. Lastly, the experimental results show the S-SAE model has higher accuracy than previous linear regression, back-propagation network, multi-layer network and deep belief network in JRT prediction.

Artificial Intelligence in Manufacturing and Robotics

The success of Computer-Aided Engineering (CAE) technology in industry can be measured by the growing number of practical applications, the number of new releases of hardware and software products, and the number of publications focusing on the integration of CAE techniques. The set of articles presented in this special issue originated from the 5th International Symposium on Artificial In­ telligence held in Cancun, Mexico, December 7-11, 1992. Eighty papers were submitted from 21 coun­ tries for possible presentation. Out of these, 45 pa­ pers were accepted for presentation at the sympo­ sium and publication in the proceedings of the sym­ posium. On the basis of originality and innovation, authors of20 papers were invited to submit extended papers for possible publication in this special issue. Based on peer reviewing of the extended manu­ scripts, six papers were finally selected for inclusion in this special issue. The papers cover both the prac­ tical and theoretical aspects of AI applications in manufacturing and robotics and as such should be of great interest to ICAE readers throughout the world.