Inspection by exception: A new machine learning-based approach for multistage manufacturing

Papananias, Moschos; McLeay, Thomas E.; Obajemu, Olusayo; Mahfouf, Mahdi; Kadirkamanathan, Visakan

doi:10.1016/j.asoc.2020.106787

Cited by 17 publications

(17 citation statements)

References 18 publications

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“…In a similar manner, ANN can accurately predict various problems in manufacturing setting, such as inspection by exception, identifying specific process with 100% accuracy, detecting haptic feedback forecasting events with 99% accuracy, or detecting production faults and machine wear with 99.82% accuracy [39,47,73,93]. Moreover, Papananias et al showed that neural network models outperform regression models for manufacturing related pattern detections, which can explain the preference of them [47]. Aside from ML/DL solutions, six papers discuss the use of blockchain.…”

Section: Discussionmentioning

confidence: 99%

“…Table 3 details a summary of the total 317 unique features within the articles included in the SLR, which have been grouped into five subcategories (fault detection, predictive maintenance, communication, virtualization, human machine interference (HMI)). Various Industry 4.0 IT solutions are used in fault detection [45][46][47], predictive maintenance [48][49][50], communication [51][52][53], virtualization [42,54,55], and human-machine interference (HMI) [56][57][58].…”

Section: Featuresmentioning

confidence: 99%

“…Various ML and AI solutions are key drivers of data-driven decision making are discussed. For example, big data analysis can be adopted for equipment reliability analysis and predictive maintenance, as discussed by several articles within the SLR process including Lee et al, Chen et [45,47,48,74,90,91]. ML algorithms can be used for predicting machine failures or abnormalities in advance, leading to better maintenance planning possibilities and cost reduction [45].…”

Section: Data-driven Decision Makingmentioning

confidence: 99%

“…However, as big data analysis can be computationally costly, ways to solve that are required. For instance, the "inspection by exception" method by Papananias et al, that focuses on inspection of only the parts which quality is considered uncertain [47]. In fact, using unsupervised Fuzzy C-Means clustering algorithm and PCA reduced the amount of inspections by 82% and PCA-based supervised ANN by 93% [47].…”

Section: Data-driven Decision Makingmentioning

confidence: 99%

“…As CNN is popular choice for image pattern classification, it can be used for detection of user actions in assembly lines as in Ji et al and Sarivan et al, or applied to timeseries problems, such as done in the works by Kiangla et al, Essien et al, or to multi-sensor information fusion that is then converted to image, as in the work by Wang et al[48,87,91,95,108]. In a similar manner, ANN can accurately predict various problems in manufacturing setting, such as inspection by exception, identifying specific process with 100% accuracy, detecting haptic feedback forecasting events with 99% accuracy, or detecting production faults and machine wear with 99.82% accuracy[39,47,73,93]. Moreover, Papananias et al showed that neural network models outperform regression models for manufacturing related pattern detections, which can explain the preference of them[47].…”

mentioning

confidence: 99%

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A Systematic Literature Review of Industry 4.0 Technologies within Medical Device Manufacturing

Lepasepp

Hurst

2021

Future Internet

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Ever since the emergence of Industry 4.0 as the synonymous term for the fourth industrial revolution, its applications have been widely discussed and used in many business scenarios. This concept is derived from the advantages of internet and technology, and it describes the efficient synchronicity of humans and computers in smart factories. By leveraging big data analysis, machine learning and robotics, the end-to-end supply chain is optimized in many ways. However, these implementations are more challenging in heavily regulated fields, such as medical device manufacturing, as incorporating new technologies into factories is restricted by the regulations in place. Moreover, the production of medical devices requires an elaborate quality analysis process to assure the best possible outcome to the patient. Therefore, this article reflects on the benefits (features) and limitations (obstacles), in addition to the various smart manufacturing trends that could be implemented within the medical device manufacturing field by conducting a systematic literature review of 104 articles sourced from four digital libraries. Out of the 7 main themes and 270 unique applied technologies, 317 features and 117 unique obstacles were identified. Furthermore, the main findings include an overview of ways in which manufacturing could be improved and optimized within a regulated setting, such as medical device manufacturing.

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