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Methodology for Developing a Quality Management System for Cylinder Sleeves Manufacturing 1

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8th International Scientific-Technical Conference (MANUFACTURING)

作者： Prykhodko, Olexandr Denysenko, Yuliia Dynnyk, Oksana Sumy State Univ Class Profess Coll 12M Nemolota St UA-41600 Konotop Ukraine Sumy State Univ 2 Rymskogo Korsakova St UA-40007 Sumy Ukraine Poznan Univ Tech 3 Piotrowo St PL-61001 Poznan Poland

ISBN: (数字)9783031564741

ISBN: (纸本)9783031564765;9783031564741

The article describes the features and specifics of building a quality management system for an engineering enterprise based on the requirements of ISO 9001:2015. The author proposes amethodology for the design and production management system of engineering products, according to which the following stages of creating and processing quality information should be carried out. The first stage consists in collecting data during the design and production processes and is based on the theory of matrix calculus. The second stage involves FMEA analysis (risks of potential product and process failures), as well as researching the level of product quality in order to maintain its competitiveness. It is proposed to use Ishikawa diagrams to identify the causes of product and process quality deterioration and the factors that contribute to it. During the third stage, the primary statistical analysis of the data obtained is carried out using Pareto diagrams, which help to establish the necessary corrective actions. The described methodology was tested on the processes of machining the cylinder sleeve of an internal combustion engine.

关键词： Quality Matrix FMEA-Analysis Single Quality Indicator Priority Risk Number Quality Vector Pareto Diagram Sustainability

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Using Process Mining as a Tool for Process' Digital Twin to Perform Strategic Maintenance Decisions 32nd

Using Process Mining as a Tool for Process' Digital Twin to ...

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32nd International Conference on Flexible Automation and Intelligent Manufacturing

作者： dos Santos, Cleiton Ferreira de Oliveira, Alef Berg Micosky, Andre Luiz Rocha Loures, Eduardo de Freitas Portela Santos, Eduardo Alves Pontif Catholic Univ Parana PUC PR Curitiba Brazil Fed Univ Parana UFPR Curitiba Brazil

ISBN: (纸本)9783031381645;9783031381652

Process mining (PM) and digital twin (DT) present synergy in the context of Industry 4.0 era, as data-driven approaches are leading the way. With this relation, some design principles of DT are accomplished by applying PM techniques in order to extract knowledge from industrial processes. This knowledge can range from singular metrics of indicators through complex process analyzes (comprehension of paths, deviations, frequency, simulation and among others). In this context, the motivation of the work comes from using factory floor process data in a process' digital twin environment as inputs in the decision layer to make assertive choices in the industrial and/or maintenance environment considering different scenarios of actions. Therefore, the main goal of this paper aims at integrating those fields of study (i.e., process mining, process' digital twin and decision-making) in an industrial maintenance area to choose or indicate a strategic maintenance action that best fits the evaluated scenario.

关键词： Process mining Process' digital twin Decision-making Smart industrial maintenance Data-driven decisions

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Product-Service Integrated Maintenance System: A Framework for Railcar Brake System 1

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33rd International Conference on Flexible Automation and Intelligent Manufacturing

作者： Shihundla, Bongani Trevor Mpofu, Khumbulani Adenuga, Olukorede Tijani Tshwane Univ Technol Staartsartillirie Rd ZA-0001 Pretoria South Africa

ISBN: (数字)9783031744853

ISBN: (纸本)9783031744846;9783031744853

Modern train brakes design incorporates a sensor installation to measure pressure, speed, force, displacement for analysis of the degradation of the braking system. Optimizing maintenance has led to the creation, transmission, and storage of enhanced data analysis tools for increased security and efficiency. The paper aims to investigate transmitted data from the product for railcar brake maintenance using Abaqus simulation model for different components of the railcar bogie with the physical structure being analysed. The results demonstrate the analysis of determinate nodes and elements, which delineate the fundamental geometry of the modeled physical structure. Product Service Systems enable the creation of sustainable solutions tailored to evolving customer needs, including modifying existing solutions to meet new demands, even amidst changes in the industry.

关键词： Sensors Smart products Train braking Finite element analysis

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Deploying Optical Character Recognition to Improve Material Handling and Processing 32nd

Deploying Optical Character Recognition to Improve Material ...

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32nd International Conference on Flexible Automation and Intelligent Manufacturing

作者： Shahin, Mohammad Hosseinzadeh, Ali Chen, F. Frank Davis, Marvin Rashidifar, Rasoul Shahin, Awni Univ Texas San Antonio Mech Engn Dept San Antonio TX 78249 USA Mutah Univ Fac Educ Al Karak Jordan

ISBN: (纸本)9783031381645;9783031381652

This paper assesses the supporting function of a Machine-based Identification system (MBID) via Optical Character Recognition (OCR) in a Lean manufacturing paradigm. The objective of this paper is to also explore the use of MBID to enable a competitive manufacturing process in a Lean 4.0 environment. Furthermore, a MBID via OCR model is proposed to extract the printed identification number of packages from images captured by a fixed camera in an industrial environment. The method considers different digital image processing techniques to deal with the significant lighting and printing variation observed, followed by a segmentation process that extracts and aligns the characters. Experiments were carried out on a data set consisting of 200 images and achieved an overall detection accuracy of 95% with a very low Character Error Rate (CER) value of 0.0041, clearly supporting the validity and effectiveness of the proposed method.

关键词： Lean Manufacturing Big Data Waste Reduction Machine Learning Industry 4.0 Optical Character Recognition

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Development of Photocatalytic Self-cleaning 316 Stainless Steel Surface 3rd

Development of Photocatalytic Self-cleaning 316 Stainless St...

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3rd International Conference on Advanced Surface Enhancement (INCASE)

作者： Ong, Jun Hui Yan, Guangxu Maharjan, Niroj Chen, Zhong Nanyang Technol Univ 50 Nanyang Ave Singapore 639798 Singapore Agcy Sci Technol & Res Adv Remfg & Technol Ctr 3 Cleantech Loop Singapore 637143 Singapore

ISBN: (纸本)9789819986422;9789819986439

Surfaces that can keep themselves clean are described as self-cleaning surfaces. Such surfaces have drawn a lot of attention due to their numerous applications in food, aerospace, manufacturing and other industries. Self-cleaning for metals and alloys is usually achieved by creating hydrophobic textured surfaces. However, such surfaces obtained by texturing are unstable as their wettability changes over time. An alternative to creating self-cleaning surfaces is through the photocatalytic effect where a semiconductor material can generate radicals to remove organic contaminants upon the illumination of UV light. We studied the photocatalytic efficiency of TiO2 coating deposited on smooth and textured 316 stainless steel surfaces. Thermal oxidation at different temperatures was explored to study how it affects the photocatalytic efficiency of TiO2 coating. The results showed that the textured surfaces achieved a good bonding of TiO2 coating and a better self-cleaning effect as compared to the smooth samples. Generally, a better self-cleaning effect could be observed for TiO2 films that were obtained by thermally oxidising Ti films at around 500 degrees C.

关键词： Self-cleaning Photocatalytic coating Titanium dioxide

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Predictive Modeling of Part Quality in the WAAM Process Using PCA Model Reduction and Machine Learning 10th

Predictive Modeling of Part Quality in the WAAM Process Usin...

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10th International Conference on Design and Modeling of mechanical Systems (CMSM)

作者： Guizani, Amir Hammadi, Moncef Yousfi, Larbi Haddar, Mohamed Univ Sfax Natl Engn Sch Sfax LA2MP Sfax 3038 Tunisia ISAE SUPMECA Quartz Lab EA7393 3 Rue Fernand Hainau F-93400 St Ouen France

ISBN: (纸本)9783031650062;9783031650079

The Wire Arc Additive Manufacturing (WAAM) technology is a directed energy deposition process that uses an arc welding equipment to melt and continuously deposit material in wire form. This work focuses on the study of the WAAM process using an approach based on machine learning and statistical data analysis to solve the problem of weld quality classification. In this study, experiments were carried out to explore the essential parameters of this process, such as welding speed, wire feed rate and electric current, with the aim of producing high-quality weld layers in terms of geometry. The resulting data from the experiments were rich in information. In order to efficiently manage the data collected during these experiments, Principal Component Analysis (PCA) was applied to reduce the dimensionality of the data while preserving its relevance. Then, several machine learning models, including Random Forest, Logistic Regression, Decision Tree, Naive Bayes Gaussian and SVM, were used to predict welding quality based on parameter data generated by PCA. To evaluate and compare the performance of these models, evaluation metrics such as precision, recall, F1-score and confusion matrix were used. Following this comparative evaluation, we identified that the Random Forest model is the most accurate, capable of reliably predicting part quality based on various combinations of parameters. This approach was implemented in python and was also successfully applied to accurately predict the results of the WAAM process.

关键词： WAAM process PCA Machine Learning Statistical Data Analysis Weld Quality Classification

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Recurrence Analysis and Feed Force in Drilling of Polymer Composites 8th

Recurrence Analysis and Feed Force in Drilling of Polymer Co...

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8th International Scientific-Technical Conference (MANUFACTURING)

作者： Ciecielag, Krzysztof Skoczylas, Agnieszka Matuszak, Jakub Lublin Univ Technol Dept Prod Engn Fac Mech Engn Lublin Poland

ISBN: (纸本)9783031564659;9783031564635

This study relates to drilling of polymer composites. Composite drilling results from the impossibility of making precise holes in the first stage of composite production, i.e. in the molds. Making holes is associated with the problem of delamination, i.e. the fracture of the fiber reinforcement at the tool exit. A drill with a TiAlN coating applied by the PVD technique was used in the study. The paper presents the results of feed force measurement made in the drilling of polymer composites with glass, carbon and aramid fibers. The results showed that the highest feed force values occurred in the drilling of aramid fiber-reinforced plastics (820 N), while the lowest ones when drilling glass fiber-reinforced plastics (130 N). It was found that feed per revolution had the most significant impact on the feed force values. Each type of fiber of composite cause the feed force to increase, but the increase is most observed for drilling aramid fiber reinforced plastics. For composites reinforced with glass fibers, the feed force values are in the range of 130-400 N, with carbon fibers 420-650 N, with aramid fibers 480820 N. The measured feed force values were also used to determine recurrence quantifications sensitive to changes in the technological parameters of drilling. An analysis of the drilling process by the recurrencemethod showed that determinism, longest diagonal line, entropy and laminarity were the most sensitive to variations in the drilling parameters. These indicators would change their values (decrease or increase) with changes in the values of feed per revolution and cutting speed.

关键词： machining drilling polymer composites feed force recurrence quantifications

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Experimental Research on the Dimensional and Geometrical Deviations of Features-of-Size Produced by Material Extrusion Processes 32nd

Experimental Research on the Dimensional and Geometrical Dev...

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32nd International Conference on Flexible Automation and Intelligent Manufacturing

作者： Vakouftsis, Christos Kaisarlis, Georgios Spitas, Vasilios Provatidis, Christopher G. Natl Tech Univ Athens Sch Mech Engn Heroon Polytech 9 Athens 15780 Greece

ISBN: (纸本)9783031382406;9783031382413

A major advantage of Additive Manufacturing (AM) processes is their ability to produce components with complex surfaces and sophisticated structures. However, several functional requirements of end-use AM parts, related to interchangeability and assemblability, are still strongly dependent on tightly toleranced Features-of-Size (FoS), as defined in current ISO-GPS standards. The paper presents an experimental approach for evaluating the dimensional and geometrical deviations of FoS produced by AM Material Extrusion processes (MEX) where the material is joined by thermal reaction bonding (MEX-TRB), such as Fused Deposition Modeling (FDM). A set of custom, primitive based test components are manufactured by low-cost and professional MEX-TRB printers, in different materials and orientations. The FoS linear and form deviations, as per ISO 14405-1 and ISO 1101, of the produced components are then evaluated by a Coordinate Measuring Machine (CMM). With the use of regression analysis, a statistical model is created, presenting and categorizing those defects. Finally, the development of a hybrid experimental/theoretical model for the prediction of linear and form deviations in MEX is presented and discussed.

关键词： Additive Manufacturing MEX-TRB FDM Tolerance Accuracy Linear size ISO GPS standards DfAM

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Recent Trends in Artificial Intelligence and Machine Learning Methods Applied to Water Jet Machining 8th

Recent Trends in Artificial Intelligence and Machine Learnin...

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8th International Scientific-Technical Conference (MANUFACTURING)

作者： Khan, Rehan Wieczorowski, Michal Qureshi, Ariba Ammar, Muhammad Ahmed, Tauseef Khan, Umair Natl Univ Sci & Technol Islamabad Pakistan Poznan Univ Tech Poznan Poland Univ Teknol PETRONAS Seri Iskandar Darul Ridzuan P Malaysia

ISBN: (纸本)9783031564468;9783031564444

Abrasive Water Jet Machining is a revolutionary unconventional cutting technology that has a wide range of applications in the machining of difficult-to-machine materials. Process parameters are critical in determining the efficiency and economics of a high-quality machining process. As a consequence of advancements in sensor technology, machining operations may now be automated, and the massive amounts of data generated can be used to model and monitor the processes using Artificial Intelligence (AI) and Machine Learning (ML) approaches. This paper presents an overview of the current research trends linking the application of AI and ML methods to AWJM processes for enhanced performance metrics, process monitoring and control, and improved variable optimization. Overcoming challenges related to data quality, model interpretability, and system integration will be essential for the successful implementation of AI and ML in the field of water jet machining. The potential future directions in the ever-expanding field of AI and machining processes, particularly AWJM, are also discussed.

关键词： Abrasive Waterjet modelling artificial neural network process parameters

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Safeguarding Biodiversity and Future Generations: An Application of RESRAD Codes for Nuclear Emergency Planning and Response

Safeguarding Biodiversity and Future Generations: An Applica...

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Saudi International Conference on Nuclear Power engineering (SCOPE)

作者： Bello, S. Simon, J. Ahmad, U. F. Aliyu, A. S. Godwin, I Nasiru, A. M. Yusuf, J. A. Umaru Musa Yaradua Univ Dept Phys Katsina Nigeria Ahmadu Bello Univ Dept Phys Zaria Nigeria Bayero Univ Kano Ctr Renewable Energy Res Kano Nigeria Tomsk Polytech Univ Lenin Ave 30 Tomsk Russia Nigerian Nucl Regulatory Author Dept Radiol Safety Abuja Nigeria Ahmadu Bello Univ Ctr Energy Res & Training Hlth Phys & Radiat Biophys Sect Zaria Nigeria

ISBN: (纸本)9783031643613;9783031643620

The Chernobyl and Fukushima nuclear accidents have significantly impacted the public's perception of nuclear energy and its potential benefits. To fully harness nuclear technology's potential for contributing to the Sustainable Development Goals (SDGs), as highlighted by the IAEA bulletin of September 2016, we must restore public confidence in this energy source. Emergency preparedness and response is a cardinal principle of radiation protection, and the literature review indicates that most research and energy reactors worldwide have modelled hypothetical accident release scenarios. However, most of these models only considered respirable gaseous radionuclides released from the reactor core and failed to consider other possible exposure media, routes, and scenarios. In this paper, we propose a framework for using the RESRAD family of codes to improve emergency preparedness and response planning, especially in Africa, where site-specific data is lacking. By critically analyzing related literature through a desk review, we demonstrate the application of these codes in biodiversity conservation, protecting people and the environment, and safeguarding future generations. Our proposed frameworks, if implemented, will build public confidence in nuclear energy projects in Africa, restoring public confidence and consequently help in solving the lingering energy crisis on the continent.

关键词： Nuclear accident RESRAD Nuclear power radiological assessment Africa

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