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56th CIRP international conference on manufacturing systems, CIRP CMS 2023

作者： González Chávez, Clarissa A. Chari, Arpita Ito, Adriana Bärring, Maja Friis, Martin Mörstram, Magnus Lopes, Paulo Víctor Stahre, Johan Chalmers University of Technology Hörsalsvägen 7a Gothenburg41296 Sweden Teknikforetagen Storgatan 5 Stockholm114 85 Sweden Intelliger Ringblomsvägen 27 Växjö352 50 Sweden Aeronautics Institute of Technology SJC Pça M. Eduardo Gomes 50 12228-900 Brazil Federal University of Sao Paulo SJC Avenida CMG Lattes 1201 12247-014 Brazil

Transforming firms towards sustainability is an imperative posed by urgent environmental concerns and governmental regulations, requiring the redesign of traditionally linear and non-transparent value chains. Digital platforms can support the twin transition to digitalization and sustainability, converting the abstract into the tangible (i.e., data flows, information, and knowledge), and providing an industrial testbed to collaborate and challenge the willingness to share data, co-create and deliver value through sustainability-oriented digital services. This study presents a research collaboration with industrial actors in the context of Swedish manufacturing, aiming to showcase the potential of platforms in Swedish value chains. Awareness of the digital maturity as-is status from the individual actors within a value chain, and purposeful development strategies can lead to improved digital performance, an increased sense of collaboration and sustainable behaviour. Through a case study approach, this article illustrates the potential of digital platforms by presenting three manufacturing value chains where prioritised industrial challenges are addressed through the capabilities of such platforms: (1) a CO2 visualisation platform that allows transparency, communication and data-sharing between the stakeholders of the value chain and aims to connect different industrial partners and aims to reduce sustainability impact through collaborative efforts;(2) a collaborative root-cause analysis digital platform that allows virtual commissioning of sustainability experts, and technical support;(3) the re-development of a productivity-centred digital service into a sustainability-informing solution that provides relevant production data for SMEs, who are customers of the machine tool sector. The preliminary findings include: (1) digital maturity of individual firms in a value chain impacts the feasibility of platform development, (2) increased digital maturity allows companies

关键词： digital maturity digital platforms manufacturing resilience root-cause analysis servitization sustainability value chains

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Simulation of Thermal Stresses in Multiplayer Plates of Non-canonical Shape 7th

Simulation of Thermal Stresses in Multiplayer Plates of Non-...

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7th international conference on Design, Simulation, manufacturing: The Innovation Exchange, DSMIE 2024

作者： Smetankina, Natalia Misiura, Serhii Misiura, Ievgeniia Sychova, Tetiana Sychov, Andrii Anatolii Pidgornyi Institute of Mechanical Engineering Problems of the National Academy of Sciences of Ukraine 2/10 Pozharskogo Street Kharkiv61046 Ukraine Simon Kuznets Kharkiv National University of Economics 9a Nauky Avenue Kharkiv61166 Ukraine State Biotechnological University 44 Alchevskih Street Kharkiv61002 Ukraine

ISBN: (纸本)9783031637193

Based on the embedding method, the problem of stationary thermoelectricity for multilayer plates of non-canonical shape in the plan is solved under the influence of interlayer heat sources. The solution is presented in analytical form. The plate with constant thickness isotropic layers and various physical properties is considered. The structure of the layer package is arbitrary. Convective heat transfer is considered on the surfaces of the plates. No additional hypotheses are introduced on the temperature distribution. Due to this, the temperature fields obtained to solve the unsteady heat conduction problem are very close to real ones. An improved plate theory is used to describe the behavior of plates. Due to this, the temperature fields obtained from the solution of the unsteady heat conduction problem are very close to the real ones. A comparison of numerical results with the experimental data showed that they agreed. For example, the stressed state of a five-layer glazing of a cockpit canopy under thermal influence is analyzed. The offered method can be implemented in developing aviation and ground transportation de-icing systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Thermal stress

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Anamoly Detection in Very Large Scale System using Big data

Anamoly Detection in Very Large Scale System using Big Data

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2022 IEEE international conference on knowledge engineering and Communication systems, ICKES 2022

作者： Gupta, Sushil Kumar Lanke, Govinda Rajulu Pareek, Manoj Mittal, Monika Dhabliya, Dharmesh Venkatesh, T. Chakraborty, Subhra Dr. Vishwanath Karad Mit World Peace University Pune India Pilani India Birla Institute of Management Technology Greater Noida India Vishwakarma Institute of Information Technology Pune India Kpr Institute of Engineering and Technology Coimbatore India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9781665456371

Big data refers to a term that is used to describe vast amounts of data that have multiple kinds of Vs: velocity, variety, and volume. It could be semi-structured, unstructured, or even structured, making data analysis difficult. New architecture, methodologies, algorithms, and analytics are needed to extract hidden data and identify assaults on enormous amounts of data. It is quite challenging to identify assaults using conventional methods. This study provides a thorough analysis of malware detection in several sectors using deep learning and provides an overview of deep learning data. In networked computers, there have been more attacks. To protect a network, a strong intrusion detection system (IDS) is necessary. Reviewing the literature reveals that while some research has been conducted in this area, a thorough and in-depth investigation has not yet been carried out. For unanticipated and unpredictable assaults, many academics suggested an IDS employing deep learning, but not for big data. The present research design is based on three ensemble methods, Randam Forest, Decision tree regression, and Gradient Boosting Tree, as well as a deep learning-based intrusion detection system for large datasets named RNN that runs for 1,000 epochs with a learning rate complexity and diversity [0.01-0.5]. It is employed in the creation of the hybrid, safe, and scalable, which is based on big data and deep learning methods. In comparison to using just one classifier, the suggested classifiers provide a more accurate classification. Detection rate (99 percentage), false positive rate (1.5 percent), accuracy (99 percentage), and F-Measure (99.03%) are the experimental results. The results show that new anomaly detection methods work better in the big data context. © 2022 IEEE.

关键词： Deep learning

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SMARTREC: A Conversational Recommendation System Using Semantic knowledge Graphs 8

SMARTREC: A Conversational Recommendation System Using Seman...

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8th IEEE international conference on Big data Computing Service and Applications, BigdataService 2022

作者： Vijayakumar, Sudha Jetcheva, Jorjeta G. TigerGraph Inc. Redwood CityCA United States San Jose State University Department of Computer Engineering San JoseCA United States

ISBN: (数字)9781665458900

ISBN: (纸本)9781665458900

Conversational Recommendation systems (CRSes) are emerging as the next-generation enabler of e-commerce services. Conversational interactions enable an interactive and engaging experience for the user and provide personalization capabilities unmatched in traditional recommendation system scenarios. In this paper, we propose a novel real-time CRS based on an intelligent domain knowledge graph with semantic network extensions, and demonstrate its effectiveness, scalability, and real-time performance relative to state-of-the-art approaches. We utilize a novel vacation booking use case, which we illustrate using a large Airbnb dataset, a conversational Artificial Intelligence (AI) component based on the open-source RASA [1] framework, and commercial-grade Neo4j [2] knowledge graph and graph-based recommendation algorithms. © 2022 IEEE.

关键词： knowledge graph

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Application of Big data Science in High Reliability Automotive Wafer Yield Management System and Failure Analysis 29

Application of Big Data Science in High Reliability Automoti...

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29th international Symposium on Semiconductor manufacturing, ISSM 2022

作者： Kuo, Chia-Cheng Chen, Po-Chih Tseng, Chang-Tsun Taiwan Semiconductor Co. Ltd. Equipment Engineering Department Yilan Taiwan Taiwan Semiconductor Co. Ltd. Product Engineering Department Yilan Taiwan

ISBN: (纸本)9781665471336

The system automatically produces yield reports, shipping reports, real-time yield monitoring and statistics, abnormal cause statistics, Yield Chart, etc., to assist manufacturing, process, integration and other personnel to quickly Obtain the finished product/work in process yield report, and grasp the product yield information in real time, and find possible yield problems in real time. With the yield analysis tool provided by this system, we can quickly find the possible abnormal reasons to reduce the abnormal yield rate and the impact of production lines and shipments. © 2022 IEEE.

关键词： Intelligent systems

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Systemising data-driven Methods for Predicting Throughput Time within Production Planning & Control

Systemising Data-driven Methods for Predicting Throughput Ti...

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2022 IEEE international conference on Industrial engineering and engineering Management, IEEM 2022

作者： Hiller, T. Deipenwisch, L. Nyhuis, P. Leibniz University Hannover Institute of Production Systems and Logistics Germany

ISBN: (数字)9781665486873

ISBN: (纸本)9781665486873

Predicting throughput times is of particular interest to production planners to schedule the production flow or communicate reliable delivery times to customers. Most established prediction methods are based on general assumptions, expert knowledge or simple statistical techniques. With the increasing use of data mining in production management, it is possible to provide more sophisticated predictions of throughput time. However, current research often does not describe the application or locate the particular prediction approach within the time and task structure of Production Planning and Control (PPC). Therefore, this paper aims to develop a systematisation approach to classify prediction models within the PPC task structure. To this end, applications along the order fulfilment process are first defined and then elaborated. A systematic literature review is conducted to classify current throughput time prediction approaches within the previously described application domains. In a case study, the application possibilities of throughput time predictions based on the provided systematisation are demonstrated, and differences in data availability and prediction quality are highlighted. © 2022 IEEE.

关键词： data mining

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A Study on Controlling and Monitoring Algorithms of Tele-Manipulator System 7

A Study on Controlling and Monitoring Algorithms of Tele-Man...

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7th international conference on Green Technology and Sustainable Development, GTSD 2024

作者： Dinh, Thien An Tran, Tuan Anh Phung, So Nam Phung, Tri Cong Ho Chi Minh City University of Technology Faculty of Mechanical Engineering Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350386073

In recent years, work-related diseases have emerged as a pressing issue in society. Tele-manipulator robot can replace human to work well in toxic and dangerous environment such as painting, welding, poisonous chemical, and radioactive. Therefore, applying robots in manufacturing can reduce the rate of occupational disease. Furthermore, it would increase productivity, efficiency and quality of products. In the research, we utilize the ability of remote controlling robot using WIFI to replace human working in dangerous environments. The system works based on master - slave principle including 2 robotics arm with 3 degrees of freedom (DOF) and the geometrical configuration is made up of base, shoulder, elbow and wrist in correspondence with the human arm joints. For the electrical circuit, the Master Robot is controlled by a microcontroller and communicate with the Slave Robot by WIFI module. The Analog Rotation Angle Sensor with low level of noise is chosen to be placed in each joint of the Master Robot in order to control the position of the Slave robot. When the system is operational, the master robot collects data from its sensors and subsequently transmits it to the slave robot. Following this data transfer, the slave robot utilizes pulse width modulation (PWM) to the motors, thereby controlling the position of the robot. We tested and adjusted this algorithm in orther to smoothen the motion of the robot and reduce the steady state error of the system. Therefore, we figured out that the overal delay of the program and the noise of the sensor highly affect the error of the robots. Finally, with these adjustments, the robot operates effectively. © 2024 IEEE.

关键词： Slave robots

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AutoML with Focal Loss for Defect Diagnosis and Prognosis in Smart manufacturing

AutoML with Focal Loss for Defect Diagnosis and Prognosis in...

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IEEE conference on Cybernetics and Intelligent systems

作者： Qing Han Yu Xia Xiupeng Shi Zeng Zeng School of Microelectronics Shanghai University (SHU) Shanghai China

Reliable defect diagnosis and prognosis for production lines are of great benefit in a range of advanced solutions towards smart manufacturing and yield optimization. However, inherent challenges exist, especially imbalanced data, missing value, and knowledge-related feature engineering. This study proposes a domain-specific automated machine learning (AutoML) method to self-learn the optimal models for defect diagnosis and prognosis. Firstly, a tree-based AutoML with focal loss (termed AutoGBM) is proposed based on XGBoost and Bayesian Optimization to improve imbalanced learning performance. Secondly, the AutoGBM system includes a hybrid feature learning mechanism based on fast feature filtering and recursive elimination, by which the key factors for defect diagnosis can be unveiled. Meanwhile, the proposed feature learning can identify the optimal interpolation preprocess to tackle missing data. Thirdly, based on Bayesian optimization, hyperparameter autotuning is conducted to generate the best prognostic results. Herein, SECOM data is used for test-bedding. Experimental results have demonstrated that AutoGBM is reliable and promising to diagnose key factors and predict defects in an automated way, as well as achieve adaptive model updates according to changes in manufacturing lines.

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A method for constructing a machining knowledge graph using an improved transformer

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EXPERT systems WITH APPLICATIONS 2024年第PartA期237卷

作者： Guo, Liang Li, Xinling Yan, Fu Lu, Yuqian Shen, Wenping Southwest Petr Univ Sch Mechatron Engn Chengdu Peoples R China Sichuan Shared Platform Oil & Gas Equipment Techn Chengdu 610059 Peoples R China China West Construct Acad Bldg Mat Chengdu 610221 Peoples R China Univ Auckland Dept Mech & Mechatron Engn Auckland New Zealand

Process knowledge base is a core component in the intelligent process, which determines the intelligent degree of product manufacturing and directly affects the production efficiency of products. However, traditional process knowledge base is often constructed manually, which is difficult and time-consuming. In addition, in the field of machining, there is a large amount of unstructured invisible process knowledge, which is not effectively orga-nized and managed. To make use of this knowledge and provide knowledge support for downstream production and maintenance, a process knowledge base construction framework is proposed by using knowledge Graph (KG) technology. Firstly, the ontology rules of process knowledge are designed from the perspective of the processing method of process characteristics according to the particularity of knowledge in the machining field. The process KG schema layer is then constructed. Secondly, a neural network BERT-Improved TRANSFORMER-CRF (BITC) model is proposed for the machining knowledge extraction task, and the data layer is constructed. Then, entity linking and knowledge fusion are performed by using the word vector cosine similarity algorithm and stored in Neo4j. The process KG is then constructed. Finally, the effectiveness of the proposed method is verified by using an aero-engine casing of an enterprise as an example. Under the same dataset, the BITC model scored higher than several other classical models. The Precision, Recall, and F1-score were 85.27%, 86.40%, and 85.83 %, respectively.

关键词： knowledge engineering Machining Transformer knowledge Graph knowledge engineering Machining Transformer knowledge Graph knowledge engineering Machining Transformer knowledge Graph

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A Study on DC Breakdown Properties According to Additive Contents of Insulating Materials for HVDC Eco-Friendly Power Cables 10

A Study on DC Breakdown Properties According to Additive Con...

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10th international conference on Condition Monitoring and Diagnosis, CMD 2024

作者： Park, Ji-Ho Kim, Chul-Ho Hong, Hye-Gyo Lee, June-Ho Electrical Engineering Hoseo University Korea Republic of

ISBN: (纸本)9788986510225

Recently, as power grids between countries and power generation using renewable energy increase, large-capacity long distance transmission is increasing. In long distance transmission, HVDC(High Voltage Direct Current) transmission, which is more advantageous than the existing HVAC(High Voltage Alternating Current) transmission, has emerged, increasing the need for research on HVDC systems[1]. In addition, cross-linked polyethylene(XLPE) has the advantages of excellent insulating ability, easy installation and connection work, and simplicity of maintenance. However, if it goes through a cross-linking process during the manufacturing process, it becomes thermosetting, making it difficult to replace or recycle after demolition and requiring high processing costs. Therefore, considering the recent trend of increasing social awareness of eco-friendliness and recycling, interest in the need for and research on thermoplastic insulation materials that can replace XLPE is increasing[2]. In order to ensure the performance of insulating materials applied to power cables, they must satisfy both physical and electrical properties. To achieve this, it is very important to select an appropriate resin as well as appropriately select and apply additives that add functions within the resin[3]. In this study, DC breakdown tests were conducted according to additive content in thermoplastic eco-friendly insulation materials. And obtained data were compared and analyzed to study the effect of additive content on DC insulation breakdown properties. © 2024 The Korean Institute of Electrical Engineers (KIEE).

关键词： HVDC power transmission

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