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نتیجه جستجو - Additive Manufacturing

تعداد مقالات یافته شده: 18
ردیف عنوان نوع
1 In-situ optimization of thermoset composite additive manufacturing via deep learning and computer vision
بهینه سازی درجای تولید افزودنی کامپوزیت ترموست از طریق یادگیری عمیق و بینایی کامپیوتری-2022
With the advent of extrusion additive manufacturing (AM), fabrication of high-performance thermoset com- posites without the need of tooling has become a reality. However, finding an optimal set of printing parameters for these thermoset composites during extrusion requires tedious experimentation as composite ink properties can vary significantly with respect to environmental parameters such as temperature and relative humidity. Addressing this challenge, this study presents a novel optimization framework that utilizes computer vision and deep learning (DL) to optimize the calibration and printing processes of thermoset composite AM. Unlike traditional DL models where printing parameters are determined prior to printing, our proposed framework dynamically and autonomously adjusts the printing parameters during extrusion. A novel DL integrated extrusion AM system is developed to determine the optimal printing parameters including print speed, road width, and layer height for a given composite ink. This closed loop system is consisted of a computer communicating with an extrusion AM system, a camera to perform in-situ imaging and several high accuracy convolution neural net- works (CNNs) selecting the ideal process parameters for composite AM. The results show that our proposed process optimization framework was able to autonomously determine these parameters for a carbon fiber- composite ink. Consequently, specimens with complex geometries could be fabricated without visible defects and with maximum fiber alignment and thus enhancing the mechanical performance of the specimen’s com- posite material. Moreover, our proposed framework minimizes a labor-intensive procedure required to additively manufacture thermoset composites by optimizing the extrusion process without any user intervention.
keywords: یادگیری عمیق | بینایی کامپیوتر | اکستروژن | پرینت سه بعدی کامپوزیت | Deep learning | Computer vision | Extrusion | Composite 3D printing
مقاله انگلیسی
2 Monitoring process stability in GTA additive manufacturing based on vision sensing of arc length
نظارت بر ثبات فرآیند در تولید افزودنی GTA بر اساس حس بینایی طول قوس-2021
Gas tungsten arc additive manufacturing (GTA-AM) is a highly promising technology to produce large-scale components. Monitoring and control of process stability are key challenges that hinder the wholesale industrialization of this technology and lack thorough investigation. A vision sensor is employed to directly monitor the arc length which can reflect the forming height stability with little detection hysteresis. Arc images are captured under two different brightnesses, and corresponding image processing algorithms are designed to detect the arc length under strong arc and weak arc. Thin walls are deposited to reveal the effectiveness of both detection methods. The results show that the detected arc length under weak arc fluctuates less than strong arc and is almost the same as the actual value, and the maximum error is less than 0.03 mm. This study will lay a solid foundation for the future control of process stability in GTA-AM.
Keywords: Additive manufacturing | Gas tungsten arc | Process stability | Arc length | Vision sensing
مقاله انگلیسی
3 Improving supply chain resilience through industry 4:0: A systematic literature review under the impressions of the COVID-19 pandemic
بهبود انعطاف پذیری زنجیره تأمین از طریق صنعت 4:0: بررسی ادبیات سیستماتیک تحت تأثیر همه گیری COVID-19-2021
The COVID-19 pandemic is one of the most severe supply chain disruptions in history and has challenged practitioners and scholars to improve the resilience of supply chains. Recent technological progress, especially industry 4.0, indicates promising possibilities to mitigate supply chain risks such as the COVID-19 pandemic. However, the literature lacks a comprehensive analysis of the link between industry 4.0 and supply chain resilience. To close this research gap, we present evidence from a systematic literature review, including 62 papers from high-quality journals. Based on a categorization of industry 4.0 enabler technologies and supply chain resilience antecedents, we introduce a holistic framework depicting the relationship between both areas while exploring the current state-of-the-art. To verify industry 4.0’s resilience opportunities in a severe supply chain disruption, we apply our framework to a use case, the COVID-19-affected automotive industry. Overall, our results reveal that big data analytics is particularly suitable for improving supply chain resilience, while other industry 4.0 enabler technologies, including additive manufacturing and cyber-physical systems, still lack proof of effectiveness. Moreover, we demonstrate that visibility and velocity are the resilience antecedents that benefit most from industry 4.0 implementation. We also establish that industry 4.0 holistically supports pre-disruption resilience measures, enabling more effective proactive risk management. Both research and practice can benefit from this study. While scholars may analyze resilience potentials of under-explored enabler technologies, practitioners can use our findings to guide industry 4.0 investment decisions.
Keywords: Industry 4.0 | Supply chain risk management | Supply chain resilience | Supply chain disruption | Digital supply chain | Literature review
مقاله انگلیسی
4 A Machine Vision Based Automated Quality Control System for Product Dimensional Analysis
سیستم کنترل کیفیت خودکار مبتنی بر بینایی ماشین برای تجزیه و تحلیل ابعاد محصول-2021
Quality control (QC) in manufacturing processes is critical to ensuring consumers receive products with proper functionality and reliability. Faulty products can lead to additional costs for the manufacturer and damage trust in a brand. A growing trend in QC is the use of machine vision (MV) systems because of their noncontact inspection, high repeatability, and relatively low cost. This paper presents a robust MV system developed to perform comparative dimensional inspection on diversely shaped samples, including additive manufacturing products. The algorithm used performs dimensional inspection on a base product considered to have acceptable dimensions. The perimeter, area, rectangularity, and circularity of the base product are determined using blob analysis on a calibrated camera. These parameters are then used as the standard with which to judge additional products. Each product following is similarly inspected and compared to the base product parameters. A likeness score is calculated for each product, which provides a single value tracking all parameter differences. Finally, the likeness score is considered on whether it is within a threshold, and the product is considered to be acceptable or defective. The proposed MV system has achieved satisfactory results, as discussed in the results section, that would allow it to serve as a dependable and accurate QC inspection system in industrial settings.© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)Peer-review under responsibility of the scientific committee of the Complex Adaptive Systems Conference, June 2021.
Keywords: Machine Vision | Quality Control | Dimensional Analysis | Digital Quality | Rectangularity | Circularity | Production | Manufacturing
مقاله انگلیسی
5 Digital Twin-enabled Collaborative Data Management for Metal Additive Manufacturing Systems
مدیریت داده های همکاری مشترک زوج دیجیتال برای سیستم های تولید مواد افزودنی فلز-2020
Metal Additive Manufacturing (AM) has been attracting a continuously increasing attention due to its great advantages compared to traditional subtractive manufacturing in terms of higher design flexibility, shorter development time, lower tooling cost, and fewer production wastes. However, the lack of process robustness, stability and repeatability caused by the unsolved complex relationships between material properties, product design, process parameters, process signatures, post AM processes and product quality has significantly impeded its broad acceptance in the industry. To facilitate efficient implementation of advanced data analytics in metal AM, which would support the development of intelligent process monitoring, control and optimisation, this paper proposes a novel Digital Twin (DT)-enabled collaborative data management framework for metal AM systems, where a Cloud DT communicates with distributed Edge DTs in different product lifecycle stages. A metal AM product data model that contains a comprehensive list of specific product lifecycle data is developed to support the collaborative data management. The feasibility and advantages of the proposed framework are validated through the practical implementation in a distributed metal AM system developed in the project MANUELA. A representative application scenario of cloud-based and deep learning-enabled metal AM layer defect analysis is also presented. The proposed DT-enabled collaborative data management has shown great potential in enhancing fundamental understanding of metal AM processes, developing simulation and prediction models, reducing development times and costs, and improving product quality and production efficiency.
Keywords: Metal Additive Manufacturing | Digital Twin | data management | data model | machine learning | product lifecycle management
مقاله انگلیسی
6 ساخت و جامعه - یک دانشجوی مقدماتی دوره مهندسی با مشارکت ساخت و علوم اجتماعی
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 10 - تعداد صفحات فایل doc فارسی: 38
برنامه درسی و آموزش مقدماتی دانشجوی دوره اول مهندسی در زمینه تولید و جامعه ارائه شده است. این دوره برای استفاده از پهپاد کوادکوپتر به عنوان نمونه برای آموزش دانش در فرایندها و سیستم های تولید ، نشان دادن تأثیرات بالقوه ساخت به جامعه و تحریک یادگیری در نوآوری ، کار گروهی و ارتباطات در یک محیط مهندسی دنیای واقعی وهمچنین برای یادگیری و نحوه نتیجه گیری بهتر دانش آموزان طراحی شده است. این دوره بر اساس دو پروژه تیمی ساخته شده است. دانشجویان مونتاژ یک هواپیمای بدون سرنشین پیشرفته ، DJI Spark را تمرین و زمان بندی کردند و یک ارائه پروژه میان مدت و گزارش برنامه خود را برای راه اندازی کارخانه مونتاژ در میشیگان برای این پهپاد ایجاد کردند. دانش آموزان طراحی خط مونتاژ ، تعادل زمان چرخه ، مطالعه زمان ، اتوماسیون ، رباتیک ، ارگونومی را انجام دادند. پروژه نهایی ، طراحی و ساخت پیوست به یک هواپیمای بدون سرنشین DJI F330 ، برای مأموریتی است که به نفع جامعه خواهد بود. دانش آموزان جنبه های اجتماعی ساخت ، طراحی به کمک رایانه ، ساخت مواد افزودنی ، مهارت های ساخت و فرایندهای ساخت را فرا گرفتند. هر تیم یک نیاز اجتماعی را شناسایی کرده ، ضمیمه ای را برای پهپاد DJI F330 ، با مهندسان باتجربه طراحی کرده ، قطعات ساخته شده ، پیوست را مونتاژ کرده و آنها را در پروازهای آزمایشی پهپاد ارزیابی کرده است. این دوره با همکاری نزدیک با یک کالج جامعه محلی برای به اشتراک گذاشتن سخنرانی و مواد آزمایشگاهی و همچنین آموزش در ساخت برای استفاده از همان گروه فارغ التحصیلان دبیرستان اجرا شد. این رویکرد علوم اجتماعی یکپارچه ، تولید ، و ارتباطات فنی برای آموزش ساخت با استفاده از هواپیماهای بدون سرنشین و ارتباط ساخت و جامعه نشان داده شده است که برای یک دوره مقدماتی مهندسی کارآمد است. دانشجویان مهندسی دوره اول اغلب در ترم اول خود تغییرات و فشار فوق العاده ای را تجربه می کنند. جلسه سخنرانی ، آزمایشگاه و بحث برای ارتباطات برای کمک به انتقال دانش آموزان در ترم اول مطالعه مهندسی تنظیم شد.
کلمات کلیدی: آموزش تولید | علوم اجتماعی | آموزش مبتنی بر نوآوری
مقاله ترجمه شده
7 Adoption and Diffusion of Disruptive Technologies: The Case of Additive Manufacturing in Medical Technology Industry in Australia
تصویب و انتشار فن آوری های مخرب: مورد تولید مواد افزودنی در صنعت فناوری پزشکی در استرالیا-2020
This paper provides the preliminary findings of a newly granted two-year project investigating the adoption of disruptive technologies, by focusing on the case of additive manufacturing (AM) in the medical technology (MedTech) industry, particularly implant applications. This is done by (I) stakeholder mapping of the industry in Australia. This included members of industry, researchers, academics, regulatory experts and MedTech consultants. (II) Identifying the top four major opportunity areas in which innovation can foster the adoption of AM implants, them being developments in Materials Science, Technology, Business Models, and Regulation & Quality Management. (III) Identifying and discussing the barriers in realizing such opportunity areas in practice, and finally (IV) recommending solutions based on the discussion and understanding of the proposed barriers that are hindering the widespread adoption and diffusion of 3-D printed medical implants. The impact of the project will be to unlock the potential of AM applications in the medical technology, which will benefit potential new entrants to the industry, incumbent firms, health care system, and patients in Australia.© 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)Peer review under the responsibility of the scientific committee of the Global Conference on Sustainable Manufacturing.
Keywords: Innovation adoption | Additive manufacturing | 3D printing | Medical device industry | Stakeholder
مقاله انگلیسی
8 Application of a fuzzy-logic based model for risk assessment in additive manufacturing r&d projects
استفاده از یک مدل مبتنی بر منطق فازی برای ارزیابی ریسک در پروژه های تحقیق و توسعه افزودنی-2020
Experts from industry and academics have highlighted Additive Manufacturing (AM) as a technology that is revolutionizing manufacturing. AM is a process that consists of creating a three-dimensional object by incorporating layers of a material such as metal or polymer. This research studies risks associated with AM R&D Project Management. A significant set of risks with a potential negative impact on project objectives in terms of scope, schedule, cost and quality are identified through an extensive literature review. These risks are assessed through a survey answered by ninety academics and professionals with noteworthy sector expertise. This process is made by the measurement of two parameters: likelihood of occurrence and impact on project objectives. According to the responses of the experts, the level of relevance of each risk is calculated, innovatively, through a fuzzy logic-based model, specifically developed for this study, implemented in MATLAB Fuzzy Logic Toolbox. The results of this study show that the risks “Defects occurring during the manufacturing process”, “Defective design”, “Poor communication in the project team” and “Insufficient financing” are determined as the most critical in AM R&D Project Management. The proposed model is presented as a powerful new tool for organizations and academics, to prioritize the risks that are more critical to develop appropriate response strategies to achieve the success of their projects.
Keywords: Additive Manufacturing | 3D printing | Risk Assessment | Project Management | Fuzzy Logic
مقاله انگلیسی
9 A survey of feature modeling methods: Historical evolution and new development
مرور روشهای مدل سازی ویژگی ها: تکامل تاریخی و توسعه جدید-2020
Initially developed for geometric representation, feature modeling has been applied in product design and manufacturing with great success. With the growth of computer-aided engineering (CAE), computer-aided process planning (CAPP), computer-aided manufacturing (CAM), and other applications for product engineering, the definitions of features have been mostly application-driven. This survey briefly reviews feature modeling historical evolution first. Subsequently, various approaches to resolving the interoperability issues during product lifecycle management are reviewed. In view of the recent progress of emerging technologies, such as Internet of Things (IoT), big data, social manufacturing, and additive manufacturing (AM), the focus of this survey is on the state of the art application of features in the emerging research fields. The interactions among these trending techniques constitute the socio-cyber-physical system (SCPS)-based manufacturing which demands for feature interoperability across heterogeneous domains. Future efforts required to extend feature capability in SCPS-based manufacturing system modeling are discussed at the end of this survey.
Keywords: Feature modeling | Feature ontology | Feature interoperability | Engineering informatics | Socio-cyber-physical system
مقاله انگلیسی
10 Process mining-based anomaly detection of additive manufacturing process activities using a game theory modeling approach
تشخیص ناهنجاری مبتنی بر استخراج فرآیند از فعالیت های فرآیند تولید مواد افزودنی با استفاده از رویکرد مدل سازی تئوری بازی-2020
As a new production procedure Additive Manufacturing will present a time-effective production system when adopted in distributed 3D printing mode. In this case, the distributed manufacturing leads to different challenges such as control between production sites. Based on the cloud infrastructure usage for distributed production systems, the product reliability handling is vital. Moreover, AM is used to produce safety–critical systems components and this product type defines AM as an interesting attack target. This study presents a new extension of uncertain Business Process Management System (uncertain BPMS) architecture for detecting anomaly using this extension capability. This extension has a new component as event-based anomaly detector, where intrusion detection can take place through an integration of process mining and game theory techniques. The proposed component could operate based on pre-processor, conformance checker, and anomaly detection optimizer modules. These modules can intelligently control the AM process activities between expected behavior and actual behavior using distributed event logs, a hybrid of highly accurate algorithms such as Improved Particle Swarm Optimization (IPSO), firefly, and AdaBoost algorithms inside the game theory modeling approach. In this case, the game theory technique as an optimizer provides optimal selection strategies for the proposed component to detect untrusted behaviors. The results of the new extension execution on a case study and its evaluation using Nash Equilibrium (NE) solution indicate that the proposed anomaly detector component is highly accurate in anomaly detection for AM process activities and can detect more attacks successfully through guidance of the game theory framework in the system.
Keywords: Event-based anomaly detection | Additive manufacturing | Business process management system | Process mining technique | Game theory modeling | Distributed production system
مقاله انگلیسی
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