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检索条件"丛书名=Lecture Notes in Mechanical Engineering"

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Application of Lean Office Concepts in the Management of Labor Grievances and Commercial Matters: A Case Study in a Retailers' Network 32nd

Application of Lean Office Concepts in the Management of Lab...

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32nd International Conference on Flexible Automation and Intelligent Manufacturing

作者： de Jesus Puppi, Rafael Gustavo Alves Junior, Paulo Nocera Queiroz, Geandra Alves Melo, Isotilia Costa do Nascimento Rebelatto, Daisy Aparecida Univ Sao Paulo Sao Carlos Sch Engn EESC Dept Prod Engn BR-13566590 Sao Carlos SP Brazil Univ Catolica Norte UCN Escuela Ingn Coquimbo EIC Coquimbo 1781421 Chile ICTIN Fed Univ Lavras Inst Sci Technol & Innovat Lavras Brazil Univ Lorraine CEREFIGE ICN Business Sch Paris France

ISBN: (纸本)9783031381645;9783031381652

Although Lean was developed for manufacturing, it was quickly expanded to other areas, thus resulting in Lean Office (LO), which can be defined as a system of standardized service operations. It consists of activities to generate value for the customer and am to meet their expectations of quality and price while focusing on intangibles. This study evaluated the applicability of LO concepts in the management of labor grievances and commercial matters in the legal department of a network of retailers. The data was collected through unstructured interviews, observations, and database analysis. The study was divided into diagnosis, application of Lean tools, and the implementation of indicators. As a result, the internal mapping was developed, and new flows were established, eliminating waste and activities that had not added value. Following the Lean philosophy, a new management system was defined and implemented, which increased processes efficiency and reduced the expenses in lawsuits.

关键词： Lean Office Lean Service Management Labor Grievances and Commercial matters

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Self-tuning of the Virtual-Bike Control for a Human-Powered Electric Bike with Series Architecture 16th

Self-tuning of the Virtual-Bike Control for a Human-Powered ...

引用

16th International Symposium on Advanced Vehicle Control

作者： Radrizzani, Stefano Panzani, Giulio Savaresi, Sergio Matte Politecn Milan Dipartimento Elettron Informaz & Bioingn Via G Ponzio 34-5 I-20133 Milan Italy

ISBN: (纸本)9783031703942;9783031703928;9783031703911

Motivated by environmental awareness, electric bikes (e-Bikes) diffusion as a means of transport has significantly increased in cities, thanks to their low emission and footprint. Among the different alternatives, series-parallel e-Bike architectures are interesting because they merge the advantages of the most common parallel bikes and the series ones, which can be exploited to improve the user experience. When an e-Bike is operating in series mode, a specific control action is needed to handle the absence of a mechanical transmission and so the chain-less nature of series or series-parallel e-Bikes. To this aim, the virtual-chain control law has been proposed and recently extended to a virtual-bike approach, respectively aiming at emulating the experience of the chain or an entire bike, whose parameters are user-chosen. In this work, a self-tuning strategy for the control parameters in the virtual-bike approach is formulated, making it independent of the specific bike and rider. Experimental results showed the advantages and limitations of the proposed solution.

关键词： Chains

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A Learning-Based Model Predictive Contouring Control for Vehicle Evasive Manoeuvres 16th

A Learning-Based Model Predictive Contouring Control for Veh...

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16th International Symposium on Advanced Vehicle Control

作者： Bertipaglia, Alberto Alirezaei, Mohsen Happee, Riender Shyroka, Barys Delft Univ Technol NL-2628 CD Delft Netherlands Siemens PLM Software NL-5708 JZ Helmond Netherlands

ISBN: (纸本)9783031703942;9783031703928;9783031703911

This paper presents a novel Learning-based Model Predictive Contouring Control (L-MPCC) algorithm for evasive manoeuvres at the limit of handling. The algorithm uses the Student-t Process (STP) to minimise model mismatches and uncertainties online. The proposed STP captures the mismatches between the prediction model and the measured lateral tyre forces and yaw rate. The mismatches correspond to the posterior means provided to the prediction model to improve its accuracy. Simultaneously, the posterior covariances are propagated to the vehicle lateral velocity and yaw rate along the prediction horizon. The STP posterior covariance directly depends on the variance of observed data, so its variance is more significant when the online measurements differ from the recorded ones in the training set and smaller in the opposite case. Thus, these covariances can be utilised in the L-MPCC's cost function to minimise the vehicle state uncertainties. In a high-fidelity simulation environment, we demonstrate that the proposed L-MPCC can successfully avoid obstacles, keeping the vehicle stable while driving a double lane change manoeuvre at a higher velocity than an MPCC without STP. Furthermore, the proposed controller yields a significantly lower peak sideslip angle, improving the vehicle's manoeuvrability compared to an L-MPCC with a Gaussian Process.

关键词： Learning-based model predictive control Student-t process Evasive manoeuvre Obstacle avoidance Limit of handling

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Compact Convolutional Transformer for Bearing Remaining Useful Life Prediction 1

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7th International Workshop on Autonomous Remanufacturing (IWAR)

作者： Jin, Zhongtian Chen, Chong Liu, Qingtao Syntetos, Aris Liu, Ying Cardiff Univ Sch Engn Dept Mech Engn Cardiff CF24 3AA Wales Guangdong Univ Technol Guangdong Prov Key Lab Cyber Phys Syst Guangzhou 510006 Peoples R China Changan Univ Key Lab Rd Construct Technol & Equipment MOE Xian 710064 Peoples R China Cardiff Univ Cardiff Business Sch PARC Inst Mfg Logist & Inventory Cardiff CF10 3EU Wales

ISBN: (数字)9783031526497

ISBN: (纸本)9783031526480;9783031526497

An accurate prediction of bearing remaining useful life (RUL) has become increasingly important for equipment maintenance with the development of monitoring technology and deep learning (DL). Although Transformers are currently the most commonly used unique learning algorithms for sequential data, concerns about their computational efficiency and cost exist. In this regard, Compact Convolutional Transformers (CCT) have emerged as a promising alternative that employs sequence pooling and replaces patch embedding with convolutional embedding to enhance computational efficiency while maintaining high prediction accuracy with smaller model sizes. This study proposes an RUL prediction modeling approach that utilizes the Continuous Wavelet Transform (CWT) to transform time-frequency domain features into images, subsequently fed into CCT to establish a highly accurate prediction model for the RUL of bearings. This study conducted experiments using the XJTU-SY rolling bearing dataset. The performance was evaluated in terms of root mean square error (RMSE) and maximum absolute error (MAE) by modifying the layer configuration and comparing with other state-of-the-art algorithms.

关键词： Deep learning Remaining useful life Prognostic and health management Transformer network

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Asset Administration Shell Modelling and Implementation Enabling Plug and Produce Capabilities for Modular Production 32nd

Asset Administration Shell Modelling and Implementation Enab...

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32nd International Conference on Flexible Automation and Intelligent Manufacturing

作者： Alonso, Lucia Barja, Lara Lodeiro, Baltasar Xanthakis, Evangelos Broechler, Raimund AIMEN Technol Ctr O Porrino 36400 Spain Netcompany Instrasoft 2B Rue Nicolas Bove L-1253 Luxembourg Luxembourg

ISBN: (纸本)9783031381645;9783031381652

Mass customization approach requires low-volume, high-mix production schemes, leading to the creation and industrial uptake of highly flexible and modular production lines capable of processing a wide range of products. Towards that end, flexibility of reconfiguration and responsiveness of production plants needs to be increased. Digital technologies can make a significant contribution in making production systems more flexible for modular production. Reconfigurability and interoperability between automated industrial systems and Cyber Physical Systems (CPS) can be achieved by implementing a Digital Twin based on Asset Administration Shell (AAS) and formal definitions found in the I4.0, IIoT and IDTA standards. The AAS modelling containing a machine-readable, semantically unambiguous self-description metamodels needs to be supported by a software infrastructure capable to orchestrate the different assets. This paper presents an implementation of AAS modelling based on the relation of the main production assets -Product, Process and Resources (PPR model)- allowing the dynamic creation of process chains and reconfigurations, as well as up-to-date virtual representation of the Product by deploying a Digital Thread able to trace and orchestrate the data generated along the product lifecycle.

关键词： Asset Administration Shell Plug and Produce modular production

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The Role of Lean Practices in Successful Warehouse Management System Implementation 32nd

The Role of Lean Practices in Successful Warehouse Managemen...

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32nd International Conference on Flexible Automation and Intelligent Manufacturing

作者： Frecassetti, Stefano Ahmadi, Alireza Khamidov, Ibrokhimjon Staudacher, Alberto Portioli Politecn Milan Dept Management Econ & Ind Engn Piazza Leonardo da Vinci 32 I-20133 Milan Italy

ISBN: (纸本)9783031381645;9783031381652

Industry 4.0 opens new business opportunities and allows companies to gain a competitive advantage by introducing digital technologies in their processes. However, the digitalization process can be critical and, if poorly executed, could lead to worse performance than at the beginning. It requires a great deal of analysis, but most importantly, it must be seen by all stakeholders to be successful and valuable to the firms. The following study explains how Lean Management practices can facilitate the success of Digitalization projects. Overall, Lean practices have been recognized as a fundamental driver for driving the process of digitalization. In particular, hard Lean practices facilitate the removal of waste from the process before the digitalization and a fair selection of the investments. On the other hand, soft practices such as the operators' involvement are critical for overcoming the barriers to digital transformation and achieving the overarching company's goal.

关键词： Lean practices Digitalization Lean Digital Transformation Operators involvement

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A Model Identification Forensics Approach for Signal-Based Condition Monitoring 32nd

A Model Identification Forensics Approach for Signal-Based C...

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32nd International Conference on Flexible Automation and Intelligent Manufacturing

作者： Jalayer, Masoud Shojaeinasab, Ardeshir Najjaran, Homayoun Politecn Milan Dept Management Econ & Ind Engn Via Lambruschini 24 b I-20156 Milan Italy Univ Victoria Dept Elect & Comp Engn Victoria BC V8P 5C2 Canada Univ Victoria Dept Mech Engn Victoria BC V8P 5C2 Canada

ISBN: (纸本)9783031381645;9783031381652

Condition monitoring (CM) of machines and robots is vital to improve operational reliability and to avoid occupational incidents. Recently, deep learning (DL) has become popular in CM literature for its outstanding ability of learning fault patterns. However, due to the black box and non-intuitive nature of its layers, the logic behind its decisions is hard to understand. This shortcoming hinders the DL implementation in many critical applications where the user needs to ensure the reliability of the classifier. Hence, in this paper, a new framework for DL-based CM systems is proposed, which consists of four steps (1) Feature extraction (2) Fault diagnosis (3) eXplainable Artificial Intelligence (XAI)-based model optimization (4) Interpretation system. The experimental evaluations on two real-world datasets demonstrate that the proposed XAI interpreter was able to visualize the contributing patterns to fault types. The feature engineering block not only makes it easier for the operator to only observe the contributing features, but also it helps the model optimizer to speed up the runtime. The results show that the proposed model achieved a slightly better accuracy than the other state-of-the-art models.

关键词： Explainable artificial intelligence Feature engineering Fault Detection Fault Diagnosis Interpretable artificial intelligence

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Surface Defect Detection of Remanufactured Products by Using the Improved Yolov5 1

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7th International Workshop on Autonomous Remanufacturing (IWAR)

作者： Sun, Weice Liu, Zhengqing Wang, Qiucheng Zhu, Bingbin Zhejiang Univ Technol Coll Mech Engn Hangzhou 310023 Peoples R China Zhejiang Jiuzhou New Energy Technol Co Ltd Taizhou 317523 Peoples R China

ISBN: (数字)9783031526497

ISBN: (纸本)9783031526480;9783031526497

This paper presented a machine learning method to achieve accurate surface defect detection and classification of remanufactured products. An improved You Only Look Once (YOLO) network was proposed for training and testing an image detection model on a steel surface defect dataset. The results show that the proposed YOLO model has high accuracy in detecting surface defects for the remanufacturing quality detection, and the accuracy of the modified YOLO model was improved by 2.4% when compared to the original model in mAP0.5. Moreover, the improved model reasonably reduced the simulation calculation. The improved YOLO model has less operation, higher precision, and higher speed, which has practical application value in the surface defect detection of remanufactured products.

关键词： Remanufacturing Machine learning YOLO algorithm Defect detection

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Techno-Economic Model for Hydrogen Production Using Advanced Nuclear Power Plants in Saudi Arabia

Techno-Economic Model for Hydrogen Production Using Advanced...

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Saudi International Conference on Nuclear Power engineering (SCOPE)

作者： Al Shikh, Saud A. Al-Ammar, Essam A. Alomari, Abdullah S. King Saud Univ Coll Engn Nucl Engn Program Riyadh Saudi Arabia King Abdullah City Renewable & Atom Energy KACARE Atom Energy Sect Riyadh 11451 Saudi Arabia King Abdulaziz City Sci & Technol KACST Nucl Technol Inst Riyadh 11442 Saudi Arabia

ISBN: (纸本)9783031643613;9783031643620

The Kingdom of Saudi Arabia is exploring the use of advanced nuclear power plants for hydrogen production as a sustainable and clean alternative to conventional hydrogen production methods. In this paper, a techno-economic model for hydrogen production using advanced nuclear power plant technologies in Saudi Arabia is presented. In this model, the capital and operating costs of advanced nuclear reactor technology, as well as the costs of hydrogen production plant were considered. Two distinct hydrogen production approaches are evaluated;a standalone electrolysis process called Conventional Electrolysis (CE) and an integrated system that combines high-temperature steam electrolysis (HTSE) with nuclear cogeneration. The cogeneration process involves the production of both hydrogen and electricity from the same advanced nuclear power plant, utilizing electricity and high-temperature steam produced by the reactor. In this study, the Hydrogen Economic Evaluation Program (HEEP) was employed to evaluate various scenarios, including variations in the capital cost of the advanced nuclear power plants, the hydrogen production cost, and other factors such as the cost of nuclear fuel, operation and maintenance costs, and efficiency. To ensure the safe and efficient integration of advanced nuclear power plant-based hydrogen production into Saudi Arabia's energy system, the technical and financial implications of factors such as the design and operation of the nuclear reactor, the compatibility of the HTSE unit with the reactor, the balance of plant requirements for hydrogen production, and national financial parameters were analyzed. The study provides valuable data-driven insights into the feasibility and economic viability of utilizing advanced nuclear power plants for hydrogen production in Saudi Arabia and can support in the decision-making process regarding investments in this emerging field.

关键词： Advance Nuclear Power Plant Hydrogen HEEP Conventional Electrolysis HTSE

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Human in the Data-Driven Zero Defect Manufacturing Loop: Case Examples from Manufacturing Companies 32nd

Human in the Data-Driven Zero Defect Manufacturing Loop: Cas...

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32nd International Conference on Flexible Automation and Intelligent Manufacturing

作者： Arica, Emrah Oliveira, Manuel Pedersen, Torbjorn Mannhardt, Felix Myklebust, Odd SINTEF Mfg Trondheim Norway KIT AR London England IDLETECHS Trondheim Norway

ISBN: (纸本)9783031381645;9783031381652

Data-driven Zero Defect Manufacturing (ZDM) system gathers and organizes data from different sources, integrating and analyzing the data using different tools, with the purpose to react on potential quality issues before they happen with adequate levels of data accuracy and precision. This paper discusses the role of humans in the data driven ZDM loop, considering the context of four manufacturing companies, from the EU H2020 project DAT4Zero which has also funded this study. These companies represent distinct manufacturing environments, each with specific industrial challenges and requirements, which were studied to map, analyze, and exemplify the potential role of humans in the data driven ZDM framework in real manufacturing environments.

关键词： Zero defect manufacturing Human in the loop Quality control

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