Context-aware manufacturing system design using machine learning

Ye, Yingxin; Hu, Tianliang; Nassehi, Aydin; Ji, Shuai; Ni, Hepeng

doi:10.1016/j.jmsy.2022.08.012

Cited by 10 publications

(2 citation statements)

References 31 publications

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“…Another control mechanism that was implemented involves an Analytic Hierarchy Process with expert rules applied to a dispatching problem in an assembly process to adapt to disturbances in production (Attajer et al, 2022). Specific external disruptions could include shifts in the market, prompting the development of a machine learning context-aware manufacturing system to effectively respond to varying demands (Ye et al, 2022).…”

Section: Previous Workmentioning

confidence: 99%

Cognitive manufacturing: definition and current trends

El Kalach,

Yousif,

Wuest

et al. 2024

J Intell Manuf

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Manufacturing systems have recently witnessed a shift from the widely adopted automated systems seen throughout industry. The evolution of Industry 4.0 or Smart Manufacturing has led to the introduction of more autonomous systems focused on fault tolerant and customized production. These systems are required to utilize multimodal data such as machine status, sensory data, and domain knowledge for complex decision making processes. This level of intelligence can allow manufacturing systems to keep up with the ever-changing markets and intricate supply chain. Current manufacturing lines lack these capabilities and fall short of utilizing all generated data. This paper delves into the literature aiming at achieving this level of complexity. Firstly, it introduces cognitive manufacturing as a distinct research domain and proposes a definition by drawing upon various preexisting themes. Secondly, it outlines the capabilities brought forth by cognitive manufacturing, accompanied by an exploration of the associated trends and technologies. This contributes to establishing the foundation for future research in this promising field.

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Section: Previous Workmentioning

confidence: 99%

Cognitive manufacturing: definition and current trends

El Kalach,

Yousif,

Wuest

et al. 2024

J Intell Manuf

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“…With the advent of the Industry 4.0 era, data-driven methods provide a new direction for quality prediction of complex processes [9,10]. Machine learning (ML), as a computational engine for hidden pattern recognition and data mining, uses mathematical algorithms to objectively analyze the links between heterologous, high-dimensional, nonlinear data [11][12][13]. With sufficient data, ML algorithms can account for uncertainty in production processes and assess the nonlinear relationships of part state evolutions, which can respond and help producers make intelligent quality-related decisions rapidly [14,15].…”

Section: Fig 1 Row Materials Cost Analysis Of Investment Casting Processmentioning

confidence: 99%

Machine learning-enabled early prediction of dimensional accuracy for complex products of investment casting

Dong

Wang²,

Wang³

et al. 2023

Preprint

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For the foundry industry, predicting the dimensional accuracy of investment precision castings is vital yet challenging. In order to reduce cost loss caused by out-of-tolerance phenomena, this work develops a data-driven framework for estimating and screening early products based on machine learning techniques. The hollow turbine blade is analyzed as a typical case for the proposed framework. Initially, a database was compiled from the same production line of wax patterns and corresponding castings. Feature engineering techniques were employed to choose the most important characteristics and simplify inputs, employing reject rate analysis and decision tree analysis. Random forest regressors (RFRs) were chosen as the fundamental models after a sensible selection of machine learning algorithms. To enhance the performance of RFRs, the results suggested that the thickness distribution of the source material is another crucial element in determining the dimensional correctness of castings. Ultimately, a hybrid metaheuristic strategy incorporating RFR and dung beetle optimization was developed. The results showed that the proposed prediction model could minimize the error by 22.4% compared to conventional models, making it an valuable tool for early dimensional quality evaluation and guiding wall thickness control for hollow turbine blades.

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