IoT-enabled computer vision-based parts inspection system for SME 4.0

Ullah, Ubaid; Bhatti, Farrukh Aziz; Maud, Abdur Rahman; Asim, Muhammad Ibbtisam; Khurshid, Khurram; Maqsood, Moazam

doi:10.1016/j.micpro.2021.104354

Cited by 10 publications

(7 citation statements)

References 17 publications

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“…The device measured voltage and current on one phase, with planned expansion to threephase measurement. Microcontroller devices are also used in small and medium-sized manufacturing companies, for instance, to measure multiple dimensions of 3D objects using multiple cameras connected to a Raspberry Pi, presented as a low-cost solution [14]. A similar application has also been deployed in the food industry, using Arduino to collect data affecting the final product's quality to determine the product's correct quality, such as temperature, pH, or weight.…”

Section: Methodsmentioning

confidence: 99%

Advancing Small and Medium-Sized Enterprise Manufacturing: Framework for IoT-Based Data Collection in Industry 4.0 Concept

Barton,

Budjac,

Tanuska

et al. 2024

Electronics

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Currently, industrial engineers are witnessing a rapid development of innovation in modern industry and the integration of critical elements of Industry 4.0 into production processes in order to remain competitive. Such changes are characterized by a large amount of effort and financial resources. To successfully deploy these changes requires not only the purchase of hardware and software but also the training of employees and the modification of the company’s organizational structure. The main objective of the article is to propose a framework for the modernization of SMEs to a level close to the Smart Factory by using the necessary attributes of Industry 4.0. The framework design is based on the initial state of a standard SME and consists of the design of fitting new IoT devices for efficient data collection, the design of a data warehouse for storing process data using Hadoop, and the integration of process- and operational-level data into the prepared data warehouse. The resulting design is developed in the form of a methodology and is generalized for use in manufacturing enterprises. The universal design is independent of the initial state of the enterprise.

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Section: Methodsmentioning

confidence: 99%

Advancing Small and Medium-Sized Enterprise Manufacturing: Framework for IoT-Based Data Collection in Industry 4.0 Concept

Barton,

Budjac,

Tanuska

et al. 2024

Electronics

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show abstract

“…However, higher-dimensional data are also relevant in the context of smart manufacturing. Vision techniques can be used to inspect manufactured products [87]. Three-dimensional vision techniques are also useful in inspection environments, such as the analysis of the quality of an assembly process [88].…”

Section: Types Of Sensors and Variables Measuredmentioning

confidence: 99%

A Review of Multisensor Data Fusion Solutions in Smart Manufacturing: Systems and Trends

Tsanousa

Bektsis

Kyriakopoulos

et al. 2022

Sensors

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Manufacturing companies increasingly become “smarter” as a result of the Industry 4.0 revolution. Multiple sensors are used for industrial monitoring of machines and workers in order to detect events and consequently improve the manufacturing processes, lower the respective costs, and increase safety. Multisensor systems produce big amounts of heterogeneous data. Data fusion techniques address the issue of multimodality by combining data from different sources and improving the results of monitoring systems. The current paper presents a detailed review of state-of-the-art data fusion solutions, on data storage and indexing from various types of sensors, feature engineering, and multimodal data integration. The review aims to serve as a guide for the early stages of an analytic pipeline of manufacturing prognosis. The reviewed literature showed that in fusion and in preprocessing, the methods chosen to be applied in this sector are beyond the state-of-the-art. Existing weaknesses and gaps that lead to future research goals were also identified.

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“…The amalgamation of circular economy practices and Industry 4.0 technology are becoming increasingly necessary in the industrial context. According to [28], data discrepancies from various sources impede the transformation of a linear supply chain into a circular supply chain. [61] explored that today's businesses require the discernibility, resilience, and flexibility that Industry 4.0 provides in order to avoid the failure of sustainable recycling, reusing, remanufacturing, and operations.…”

Section: Industry 40 and Circular Economymentioning

confidence: 99%

“…In industrial papers and recent literature [11,[25][26][27][28], Industry 4.0 has been primarily discussed from the standpoint of production as an operational outlook. On the other hand, Industry 4.0 and its supporting technology are thought to have the potential to affect every aspect of organizations and factories [29] and significantly improve managerial disciplines such as logistics management and supply chain [30,31].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Criteria Decision-Making Framework for Sustainable Supplier Selection in the Circular Economy and Industry 4.0 Era

2022

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Supplier selection is a difficult and important issue in sustainable supply chain management. This research proposes a managerial framework based on Industry 4.0, a plan for evaluating and choosing sustainable suppliers to implement circular economy practices. Green supplier selection (GSS), the circular economy, and Industry 4.0 have become hot topics in recent operations management discussions. Three main categories (e.g., economic, environmental, and social) and 16 subcategories related to supplier selection decisions were identified using a hybrid approach combining literature reviews and industry expert opinions. In the fuzzy environment of Pythagorean, this paper proposes comprehensive techniques for the selection of green suppliers based on entropy, stepwise weighted assessment ratio analysis (SWARA), and complex proportional assessment (COPRAS) methods. To calculate the standard weight, this technique first merges the objective weight found by the entropy method and the subjective weight found by the SWARA method. The findings show that access to finance and financial availability for implementing Industry 4.0 within the circular economy (ECO5) and R&D in environmental issues using Industry 4.0 technologies (ENV7), Information technology (IT) facilities (ECO6), and Product cost/price (ECO1) showed highest ranking among sub-criteria. Moreover, Supplier 5 was listed as the best sustainable supplier when they started making such a decision. The results of the proposed method help decision-makers make effective and efficient sustainable supplier selection.

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IoT-enabled computer vision-based parts inspection system for SME 4.0

Cited by 10 publications

References 17 publications

Advancing Small and Medium-Sized Enterprise Manufacturing: Framework for IoT-Based Data Collection in Industry 4.0 Concept

Advancing Small and Medium-Sized Enterprise Manufacturing: Framework for IoT-Based Data Collection in Industry 4.0 Concept

A Review of Multisensor Data Fusion Solutions in Smart Manufacturing: Systems and Trends

A Multi-Criteria Decision-Making Framework for Sustainable Supplier Selection in the Circular Economy and Industry 4.0 Era

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