Embedded vision device integration via OPC UA: Design and evaluation of a neural network-based monitoring system for Industry 4.0

Oks, Sascha Julian; Zöllner, Sebastian; Jalowski, Max; Fuchs, Jonathan; Möslein, Kathrin M.

doi:10.1016/j.procir.2021.05.007

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…In addition, OPC UA securely allows data exchange, and MySQL allows the storing of historical data. Finally, Oks et al [8] discuss the retrofit activity to be implemented on a PID4CPS (Portable Industrial Demonstrator for Cyber-Physical Systems). They equipped the PID4CPS with a camera, a Raspberry Pi system, UPC UA communication protocols, imaging software and neural networks.…”

Section: Technologies For Retrofittingmentioning

confidence: 99%

“…Smart retrofitting focuses on the software side much more than retrofitting. Artificial intelligence, machine learning algorithms, the use of neural networks [7,8], Digital Twins [2], cloud systems and programs for remote information management [9] are implemented. Combining all these technologies and adding human knowledge, it is possible to realise the "digital triplet" that is another intelligent solution to realise smart retrofit actions.…”

Section: Introductionmentioning

confidence: 99%

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Smart Retrofit: An Innovative and Sustainable Solution

et al. 2023

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Recovering old machinery, once it reaches its end of life, allows companies to be sustainable. Several strategies are available for this purpose, both from the point of view of hardware and software modifications. Especially in the industrial sector, these strategies are classified as revamping, remanufacturing and retrofitting. Machinery revamping, retrofitting and remanufacturing are all used to improve industrial equipment performance, efficiency and sustainability. Each approach has unique benefits and trade-offs, depending on the specific needs and requirements of the equipment and business. Moreover, according to Industry 4.0 principles, it is also possible to talk about smart retrofitting, involving the integration of various technologies such as sensors, automation systems, Digital Twins, artificial intelligence and data analytics software to control and optimise the operation of the machinery. Digital Twins, in particular, have been widely used among smart retrofit solutions and can integrate several innovative aspects of dated systems. However, a literature review needs to clarify their meaning and specific characteristics. For this reason, this paper aims to distinguish different strategies and find a correct definition of smart retrofitting, highlighting its relevance, benefits and sustainability in the industrial sector, focusing more on Digital Twin solutions for smart retrofitting.

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Section: Technologies For Retrofittingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Smart Retrofit: An Innovative and Sustainable Solution

et al. 2023

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“…Industrial robot [17], Assembly line [77], Metal forming [97], [112], Modular Production System [56], Candy packaging [52], Lab experiment [113], [119] Python-OPC UA High-bay shelf warehouse [94] OPC DA Steel Mill [20]…”

Section: Communication Protocolmentioning

confidence: 99%

Retrofitting-Based Development of Brownfield Industry 4.0 and Industry 5.0 Solutions

Tran

Ruppert

Eigner³

et al. 2022

IEEE Access

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The ongoing Industry 4.0 is characterized by the connectivity between components in the manufacturing system. For modern machines, the Internet of Things is a built-in function. In contrast, there are legacy machines in deployment functioning without digital communication. The need to connect them became popular to improve overall production efficiency. As building a new smart factory as a greenfield investment is a capital-intensive choice, retrofitting the existing infrastructure with IoT capability is more reasonable than replacing them. However, this so-called brownfield development, or retrofitting, requires specific prerequisites, e.g., digitization status assessment, technical and connectivity development, management requirement, and operational need, representing a significant disadvantage: lack of scalability. In the meantime, Industry 5.0 is under human-centric priority, which poses new challenges to the retrofitted system. Aware of the challenge, this paper provides a systematic overview of brownfield development regarding technical difficulties, supporting technologies, and possible applications for the legacy system. The research scope focuses on available Industry 4.0 advancements but considers preparing for the forthcoming Industry 5.0. The proposed retrofitting project approach can be a guideline for manufacturers to transform their factories into intelligent spaces with minimal cost and effort but still gain the most applicable solution for management needs. The future direction for other research in brownfield development for Industry 5.0 is also discussed.

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A comprehensive literature review of the applications of AI techniques through the lifecycle of industrial equipment

Elahi,

Afolaranmi,

Martinez Lastra

et al. 2023

Discov Artif Intell

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Driven by the ongoing migration towards Industry 4.0, the increasing adoption of artificial intelligence (AI) has empowered smart manufacturing and digital transformation. AI enhances the migration towards industry 4.0 through AI-based decision-making by analyzing real-time data to optimize different processes such as production planning, predictive maintenance, quality control etc., thus guaranteeing reduced costs, high precision, efficiency and accuracy. This paper explores AI-driven smart manufacturing, revolutionizing traditional approaches and unlocking new possibilities throughout the major phases of the industrial equipment lifecycle. Through a comprehensive review, we delve into a wide range of AI techniques employed to tackle challenges such as optimizing process control, machining parameters, facilitating decision-making, and elevating maintenance strategies within the major phases of an industrial equipment lifecycle. These phases encompass design, manufacturing, maintenance, and recycling/retrofitting. As reported in the 2022 McKinsey Global Survey (https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2022-and-a-half-decade-in-review), the adoption of AI has witnessed more than a two-fold increase since 2017. This has contributed to an increase in AI research within the last six years. Therefore, from a meticulous search of relevant electronic databases, we carefully selected and synthesized 42 articles spanning from 01 January 2017 to 20 May 2023 to highlight and review the most recent research, adhering to specific inclusion and exclusion criteria, and shedding light on the latest trends and popular AI techniques adopted by researchers. This includes AI techniques such as Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), Bayesian Networks, Support Vector Machines (SVM) etc., which are extensively discussed in this paper. Additionally, we provide insights into the advantages (e.g., enhanced decision making) and challenges (e.g., AI integration with legacy systems due to technical complexities and compatibilities) of integrating AI across the major stages of industrial equipment operations. Strategically implementing AI techniques in each phase enables industries to achieve enhanced productivity, improved product quality, cost-effectiveness, and sustainability. This exploration of the potential of AI in smart manufacturing fosters agile and resilient processes, keeping industries at the forefront of technological advancements and harnessing the full potential of AI-driven solutions to improve manufacturing processes and products.

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Embedded vision device integration via OPC UA: Design and evaluation of a neural network-based monitoring system for Industry 4.0

Cited by 4 publications

References 23 publications

Smart Retrofit: An Innovative and Sustainable Solution

Smart Retrofit: An Innovative and Sustainable Solution

Retrofitting-Based Development of Brownfield Industry 4.0 and Industry 5.0 Solutions

A comprehensive literature review of the applications of AI techniques through the lifecycle of industrial equipment

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