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A Digital Twin Model for an Educational Turbocharger Demonstrator
This paper presents a comprehensive review of the use of Digital twins in engineering education among various disciplines. A total of 83 research papers were analyzed, spanning the last decade from 2012 to 2022. Almost all publications were reported after the year 2018, indicating a recent surge in interest and development in this area. The review reveals that digital twin technology offers students an interactive experience with virtual models of real-world products and systems, significantly enhancing the effectiveness of engineering education. It also improves industrial competitiveness through predictive maintenance and fault diagnosis. Digital twins can be used in various engineering disciplines and for personalized learning. However, challenges such as model accuracy and data transfer must be considered when implementing them. Overall, this technology can improve student learning outcomes, increase education accessibility and cost-effectiveness, and improve production systems' safety, visibility, and accessibility. Future requirements of the field are also discussed in this paper.
A semiconductor or photoelectric manufacturer faces a more competitive market with small quantities of many products. These products require hundreds of processes for production, thereby generating huge manufacturing data. With the help of the Internet of Things (IoT) technology, the manufacturer can collect manufacturing process data in a timely manner. Due to the massive quantities of manufacturing process data, it has become difficult for manufacturers to determine the causes of product defects, by which machine, and by what manufacturing process (or recipe) parameters. This research proposes a six-step data-driven solution to this problem. The chi-square test of independence, the Apriori algorithm, and the decision tree method identify the process that is generating the defective products and extract rules to identify the lot identification of product defects and their associated manufacturing process parameters. An empirical study was conducted at an optical thin-film filter (TFF) company in Taiwan. Based on the data of the optical TFF production lines, the coating process was identified as the source of the defective products, and the extracted rules were validated and implemented. The product defect rate decreased from 20% to 5%. Hence, the proposed data-driven solution was found to be capable of helping manufacturers enhance their product yield.
The fourth industrial revolution (Industry 4.0) offers enhanced processing efficiencies and reduced downtime, however there is a lack of manufacturing training equipment for those seeking practical training in the key underpinning digital technologies. This paper reports on the development of a digital manufacturing training demonstrator, called the PERFORM turbine demonstrator, which incorporates four aspects of Industry 4.0, that of the Internet of Things (IoT), Augmented Reality (AR), the digital twin and additive manufacturing (3D printing). The program involves the printing of a polymer turbine and its testing using a turbine demonstrator system. This incorporates the use of an Arduino microcontroller, and sensors to monitor the systems variable pump speed, turbine speed, temperature, and humidity. A cloud-based platform allows for data logging and passing to the AR application, visualization, and system control. The design and development of the system is presented, alongside the results of pilot training programme, which involved undergraduate engineering students and industry trainees, on the practical application of Industry 4.0. This paper demonstrated the effectiveness of the PERFORM demonstrator in successfully integrating different digital technologies in the same system.
Current trends in industrial systems opt for the use of different big-data engines as a mean to process huge amounts of data that cannot be processed with an ordinary infrastructure. The number of issues an industrial infrastructure has to face is large and includes challenges such as the definition of different efficient architecture setups for different applications, and the definition of specific models for industrial analytics. In this context, the article explores the development of a medium size big-data engine (i.e. implementation) able to improve performance in map-reduce computing by splitting the analytic into different segments that may be processed by the engine in parallel using a hierarchical model. This type of facility reduces end-to-end computation time for all segments with their results then merged with other information from other segments after their processing in parallel. This type of setup increases performance of current clusters improving I/O operations remarkably as empirical results revealed.
This book deals with rotordynamics of automotive turbochargers while encompassing the analysis of the dynamics of rotating machines at very high rotor speeds of 300,000 rpm and above. This interdisciplinary field involves 1. thermodynamics and turbo-matching knowledge to compute working conditions of turbochargers, 2. fluid and bearing dynamics to calculate various operating thrust loads and to design the rotating floating ring bearings (two-oil-film bearings), and 3. tribology to improve the rotor stability and to reduce the bearing friction. Mathematical background in modeling and simulation methods is necessary; however, the prerequisites have been kept to a minimum. The book addresses both practitioners working in the field of rotordynamics of automotive turbochargers and graduate students in mechanical engineering. © Springer-Verlag Berlin Heidelberg 2012. All rights are reserved.
Support vector machine (SVM) is based on structural risk minimum theory, which special study the discipline for small sample cases. First learn support vector machine fault diagnosis theory, Then analysis the common failures of the exhaust gas turbocharger, Finally using support vector machine, research the application on fault diagnosis of the turbocharger, and then through the simulation verify that the support vector machine has excellent fitting ability on fault diagnosis of the turbocharger. The research results can enrich fault diagnosis method of the exhaust gas turbocharger, and then push the further development on fault diagnosis technology of the exhaust gas turbocharger.
Defect classification in the liquid crystal display (LCD) manufacturing process is one of the most crucial issues for quality control. To resolve this constraint, an automatic defect classification (ADC) method based on machine learning is proposed. Key features of LCD micro-defects are defined and extracted, and support vector machine is used for classification. The classification performance is presented through several experimental results.