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... AI helps to develop efficient systems, while robotics is necessary for better accuracy [14], [18], [19]. The widespread impact and benefits of AI and robotics on green industrialization can be seen in Fig. 3 [20]. ...
... A Foundational and general purpose: general purpose, In addition, the Study on the application framework and standardization demands of AI in intelligent manufacturing [154] highlighted the crucial role of AI in the future development of China's next generation intelligent manufacturing system with a rather limited number of existing AI standards for industrial implementation, thus leading to new standards to meet current demands for AI technology. ...
Recent technological advances encompassed by the smart factory concept have fundamentally changed industrial control systems in the way they are structured and how they operate. Majority of these changes affect Supervisory Control And Data Acquisition (SCADA) systems, shifting them to a higher level of interoperability, heterogeneous networks, big data and toward internet technologies and services in general. However, this transformation does not affect all SCADA systems equally. The immediate industrial environment and controlled processes have a significant impact as well. This paper presents a holistic approach to SCADA systems implemented in continuous flow production control within the steel industry production environment. We outline the multi-layer architecture of the SCADA control framework and the aspects of interoperability and interconnection within the architecture reference models, together with the research challenges and opportunities arising from the recent rapid increasement of the industrial control systems complexity and digital transformation under the Industry 4.0 paradigm, resulting in disrupting levels of the traditional automation pyramid based on Purdue model toward a higher level of integration and interoperability enabling cross-level data exchange empowered by the Industrial Internet of Things. Furthermore, the paper addresses the problem of proprietary SCADA systems and elaborates the causal correlation between SCADA quality requirements and adoption of new technology in relation to the specific industrial environment of the steel manufacturing process.
... These applications can be considered as the simplest if compared with the ones included in the other groups, but managers willing to onboard AI in this way should consider the high variability associated with them. Indeed, within the literature, different possible classifications have been found: Classification by application type [8], by manufacturing process, by industry [9], by impact or application stage [10], and other classifications [11]. These classifications can drive managers and give them a comprehensive overview of the different AI applications available today. ...
The research aims to understand how the implementation of Artificial Intelligence AI in Manufacturing Operations takes place. This paper will feed wider research on the interaction between Lean Production and AI, after understanding the implementation process of AI. A Systematic Literature Review (SLR) has been performed. A set of more than 2300 documents has been extracted and screened to produce a list of 90 highly selected and classified articles and conference papers dealing with the research question. After a first meta-level analysis, a structured discussion has been presented over the documents. Three macro use-cases for implementing AI into manufacturing systems have been identified. The first two use cases have been deeply analyzed by the SLR, while the third one has been left for further researches. For the first two use cases, the main applications have been presented through a comprehensive categorization (for stand-alone solution) and a clear explanation of the different paradigms (for I4.0 related implementation). Furthermore, for each case, the available frameworks have been presented. The main challenges and issues that managers should consider while implementing this kind of technology were presented. Possible consequences that AI innovations might have were also indicated. The article ends with insights for Lean production and future research.
The term metaverse was coined by author Neal Stephenson in 1992 in his science fiction novel “Snow Crash.”1 Metaverse is a conjunction of the Greek prefix “meta,” which means beyond, and the stem “verse,” which implies universe, hence the meaning “beyond the universe.” It is a futuristic, hyperrealistic virtual world where humans will spend time performing their day-to-day activities, such as entertaining, socializing, playing, working, and shopping. This requires that a metaverse offers a real-time virtual representation of the physical world with its entities, relationships, events, states, processes, and activities. According to the Gartner forecast report, the metaverse is among the top five emerging trends and technologies. Gartner predicts that by 2026 25% of people will spend at least one hour every day in the metaverse and 30% of organizations will have products and services developed for metaverse platforms.2 The metaverse is in an early developmental stage but has a considerable promise of occupying prominent space in the next phase of the Internet.
Intelligent manufacturing is a general concept that is under continuous development. It can be categorized into three basic paradigms: digital manufacturing, digital-networked manufacturing, and new-generation intelligent manufacturing. New-generation intelligent manufacturing represents an in-depth integration of new-generation artificial intelligence (AI) technology and advanced manufacturing technology. It runs through every link in the full life-cycle of design, production, product, and service. The concept also relates to the optimization and integration of corresponding systems; the continuous improvement of enterprises' product quality, performance, and service levels; and reduction in resources consumption. New-generation intelligent manufacturing acts as the core driving force of the new industrial revolution and will continue to be the main pathway for the transformation and upgrading of the manufacturing industry in the decades to come. Human-cyber-physical systems (HCPSs) reveal the technological mechanisms of new-generation intelligent manufacturing and can effectively guide related theoretical research and engineering practice. Given the sequential development, cross interaction, and iterative upgrading characteristics of the three basic paradigms of intelligent manufacturing, a technology roadmap for ''parallel promotion and integrated development" should be developed in order to drive forward the intelligent transformation of the manufacturing industry in China.
The process of troubleshooting an aircraft engine requires highly skilled and trained personnel who must be able to respond effectively to any circumstance; therefore, new methods of training to accelerate the cognitive processes of technicians must be integrated in the industry. In this matter the Augmented Reality technology represents an innovative tool that can ensure the efficient and correct transfer of knowledge. The numbers of errors during maintenance tasks can be reduced, AR provides information that is generally not easily available during maintenance operations because, in general, the troubleshooting process for airplane engine is a highly complex task and the diagnosis of a failure is critical for the passengers’ safety. This research focuses on training and execution of tasks where an aviation technician must be familiarized with a wide variety of technical data, physical components of mechanical systems and the regulations that must be followed to release an airplane for flight, the specialist must develop a correct mind map of the system and should be able to troubleshoot if necessary. The case of study is the 737 Engine Bleed Air System that is designed to provide engine compressed air to air conditioning pack with the purpose of air pressurization during flight; engine air from the compressor is used, from the 5° and the 9° stage in a safe an economical way, knowledge of the correct function of the components will increase safety and considerably reduce cost of maintenance operations. The purpose of the investigation was to develop an ergonomic tool than improves the cognitive process of technician during training for the troubleshooting techniques of the aircraft, but it also can be used to the everyday task by capturing the know-how and helpful tips from more experienced operators. A mobile solution that functions on regular tablets was delivered to enhance the troubleshooting techniques and maintenance procedures of the Engine Air Bleed System, the software can function on two aspects for training and in situ operations. A commercial aeronautical training kit was used to validate the Fault Isolation Software; the results showed that the augmented reality technique takes 17% less time and a quality increment of 24% for this complex assembly system.
In order to inspect the appearance of QFP chip lead, an automatic inspection system based on machine vision was designed. Hardware structure and software system were introduced herein. In the software system, Canny algorithm was successfully applied to detect the edge of the QFP chip lead. After that, the inspection method of standoff and the detecting method based on three-point seating plane of co-planarity were discussed. Finally, the testing experiments with respect to the QFP chip visual inspection system were carried out, which achieves satisfactory results.
The effects of rotating speed and load on screw life were investigated to study the performance degradation of screws of NC (numerical control) machine tools under different machining conditions. The real time data of vibration and cutting forces were collected. The key effects on the screw life were identified by analyses on time and frequency domains and wavelet analysis following filtering of the collected data with an empirical mode decomposition. A screw life prediction model was proposed using a multi-model fusion and a B-spline fuzzy neural network. Experimental results show that the maximal error of life prediction is 846 h, and the proposed system meet the need of active maintenance of screw.
Improving production performance requires the definition of global production objectives with a proper implementation strategy and suitable closed-loop control for their achievement. Closed-loop control structures for simple systems like temperature or velocity control are well defined, but a synthesis of plant-wide control structures is still recognised as the most crucial production management design problem in process industries. One vital issue to be resolved is how to translate implicit operating objectives, such as the minimisation of production costs into a set of measurable variables that can be then used in a feedback-control. A promising solution is the use of the key performance indicator (KPI) approach. To verify the idea of production feedback control using production KPIs as referenced controlled variables, a procedural model of a production process for a polymerisation plant has been developed. The model has been used during a number of simulation runs performed with the aim of developing and verifying the idea of KPI-based production control
Review on the research and development of maintenance engineering management
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Augmented reality training system for aerospace product assembly process guidance and its application
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Augmented reality training system for aerospace product assembly process guidance and its application
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