Challenges of modeling and analysis in cybermanufacturing: a review from a machine learning and computation perspective

Kang, SungKu; Jin, Ran; Deng, Xinwei; Kenett, Ron S.

doi:10.1007/s10845-021-01817-9

Cited by 16 publications

(11 citation statements)

References 112 publications

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“…In this context, attempts are often made to represent complex relationships with complex models and small datasets. Also in other domains, such as process engineering (Napoli & Xibilia, 2011) or medical applications (Shaikhina & Khovanova, 2017), small amounts of data play an important role in the use of ML methods-and will continue to do so (Kang et al, 2021). Accordingly, there already exist quite many methods to train complex models with small datasets in the literature.…”

Section: Related Workmentioning

confidence: 99%

Capturing and incorporating expert knowledge into machine learning models for quality prediction in manufacturing

et al. 2022

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Increasing digitalization enables the use of machine learning (ML) methods for analyzing and optimizing manufacturing processes. A main application of ML is the construction of quality prediction models, which can be used, among other things, for documentation purposes, as assistance systems for process operators, or for adaptive process control. The quality of such ML models typically strongly depends on the amount and the quality of data used for training. In manufacturing, the size of available datasets before start of production (SOP) is often limited. In contrast to data, expert knowledge commonly is available in manufacturing. Therefore, this study introduces a general methodology for building quality prediction models with ML methods on small datasets by integrating shape expert knowledge, that is, prior knowledge about the shape of the input–output relationship to be learned. The proposed methodology is applied to a brushing process with 125 data points for predicting the surface roughness as a function of five process variables. As opposed to conventional ML methods for small datasets, the proposed methodology produces prediction models that strictly comply with all the expert knowledge specified by the involved process specialists. In particular, the direct involvement of process experts in the training of the models leads to a very clear interpretation and, by extension, to a high acceptance of the models. While working out the shape knowledge requires some iterations in general, another clear merit of the proposed methodology is that, in contrast to most conventional ML, it involves no time-consuming and often heuristic hyperparameter tuning or model selection step.

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Section: Related Workmentioning

confidence: 99%

Capturing and incorporating expert knowledge into machine learning models for quality prediction in manufacturing

et al. 2022

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“…In order to optimize the Master Production Program, we introduce some inputs such as orders' beginning of manufactured accessories Body A1 and Cuff A2 (lines 2-3). Indeed, we calculate the generated loads by the produced accessories in (lines [9][10][11]. Then, we calculate the sum of the generated loads (line 12) and our function objective H already de ned in the above (line 13).…”

Section: Master Production Program: Adjustment By Stockmentioning

confidence: 99%

“…J et al, 2013). For that, Kang and his colleagues set up challenges of modeling and analysis in cyber-physical production; which is a review from a machine learning and computation perspective (S. Kang et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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A proposed integrated manufacturing system of a workshop producing brass accessories in the context of industry 4.0

Chakroun

Hani

Elmhamedi

et al. 2022

Int J Adv Manuf Technol

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Industry 4.0 is the basis for the transformation of manufacturing companies into digital enterprises. It promises more exibility in manufacturing, as well as mass customization, better quality and enhanced productivity. As a result, it empowers companies to meet the challenges of smart manufacturing of increasingly individualized products with shorter time-to-market and improved quality. Smart manufacturing has an important part in Industry 4.0. In ful llment of a need for empirical exploitation, this contribution aims to characterize and analyze a smart manufacturing process of a company specialized in the production of brass accessories, namely the spherical bushels. Basically, we set up a simulation tool to develop a numerical production platform for Industry 4.0 which e ciently operates and manages the production, procurement: through the Material Requirement Planning (MRP, Master Production Program) method, the logistics warehouse and the Cyber-Physical Production System (CPPS). The ndings have been optimized by a new redesigned approach of MRP 2: it is the load-capacity adjustment for the manufacturing planning of a smart workshop and Industry 4.0. Indeed, it is a process of setting up an integrated manufacturing system, which has allowed us to reduce the assembly time of the spherical bushels and to control the production and the assembling process. It allows us to increase the equipment utilization rate by comparing it with the company's equipment before the switch to smart manufacturing. In addition, the optimized results show that the proposed model can signi cantly increase the production e ciency and practical application in Industry 4.0. To the best of our knowledge, this is a rst work addressing the implementation of a simulation platform controlled by a dedicated Cyber-Physical Production System (CPPS) and a Master Production Program. A case study for a company manufacturing brass accessories is presented in this paper. The developed simulation platform present a basis for a future digital twin of the company.

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“…Therefore, the current tendency is to use adaptive modeling processes capable of addressing all of the dynamic variations of the operating conditions [6]. Consequently, the available literature shows that modeling based on a set of well-demonstrated laws of physics will produce a sufficiently fast and stable response, leading to increased compatibility between simulation and actual functioning behaviors [7]. Conversely, this process will only be available when the following conditions are met (see Tu et al [8], § 5.5.1.4):…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects

et al. 2021

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To ensure the continuity of electric power generation for photovoltaic systems, condition monitoring frameworks are subject to major enhancements. The continuous uniform delivery of electric power depends entirely on a well-designed condition maintenance program. A just-in-time task to deal with several naturally occurring faults can be correctly undertaken via the cooperation of effective detection, diagnosis, and prognostic analyses. Therefore, the present review first outlines different failure modes to which all photovoltaic systems are subjected, in addition to the essential integrated detection methods and technologies. Then, data-driven paradigms, and their contribution to solving this prediction problem, are also explored. Accordingly, this review primarily investigates the different learning architectures used (i.e., ordinary, hybrid, and ensemble) in relation to their learning frameworks (i.e., traditional and deep learning). It also discusses the extension of machine learning to knowledge-driven approaches, including generative models such as adversarial networks and transfer learning. Finally, this review provides insights into different works to highlight various operating conditions and different numbers and types of failures, and provides links to some publicly available datasets in the field. The clear organization of the abundant information on this subject may result in rigorous guidelines for the trends adopted in the future.

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Challenges of modeling and analysis in cybermanufacturing: a review from a machine learning and computation perspective

Cited by 16 publications

References 112 publications

Capturing and incorporating expert knowledge into machine learning models for quality prediction in manufacturing

Capturing and incorporating expert knowledge into machine learning models for quality prediction in manufacturing

A proposed integrated manufacturing system of a workshop producing brass accessories in the context of industry 4.0

Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects

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