Neural networks applications in manufacturing processes

Udo, Godwin J.

doi:10.1016/0360-8352(92)90072-r

Cited by 55 publications

(17 citation statements)

References 10 publications

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“…Other reviews on applications of ANNs in manufacturing and industrial processes include Warwick, Irwin, and Hunt (1992), Irwin, O'Reilly, Lightbody, Brown, and Swidenbank (1995a), Irwin, Warwick, and Hunt (1995b), Hunt, Irwin, and Warwick (1995) who report on applications of neural networks in control and system optimisation, Hussain (1999) who surveys the chemical process industries and Udo (1992) and Zhang and Huang (1995) who review applications such as resource allocation, scheduling, injection moulding, milling, metal cutting, arc welding and spray painting.…”

Section: Introductionmentioning

confidence: 99%

Neural modelling, control and optimisation of an industrial grinding process

Govindhasamy

McLoone

Irwin

et al. 2005

Control Engineering Practice

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This paper describes the development of neural model-based control strategies for the optimisation of an industrial aluminium substrate disk grinding process. The grindstone removal rate varies considerably over a stone life and is a highly nonlinear function of process variables. Using historical grindstone performance data, a NARX-based neural network model is developed. This model is then used to implement a direct inverse controller and an internal model controller based on the process settings and previous removal rates. Preliminary plant investigations show that thickness defects can be reduced by 50% or more, compared to other schemes employed. r

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Section: Introductionmentioning

confidence: 99%

Neural modelling, control and optimisation of an industrial grinding process

Govindhasamy

McLoone

Irwin

et al. 2005

Control Engineering Practice

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“…The same can be said about issues in resource allocation (Udo 1992), as well in flow-line and job-shop protocols (Lewis et al 1996). Crama and Klundert provides additional evidence for the NP-completeness of scheduling issues, in terms of lower and upper bounds of processing windows (Crama and Klundert 1997).…”

Section: Np Complete Nature Of Product State Conceptmentioning

confidence: 92%

Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning

Wuest¹

2015

Springer Theses

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“…ANNs are becoming better well known and have been successfully implemented in manufacturing [12]. For instance, Philipoom et al [10] used neural network models to forecast the order due-date in a simple flow shop manufacturing system.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Due-date assignment in wafer fabrication using artificial neural networks

Sha

Hsu

2004

The International Journal of Advanced Manufacturing Technology

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Due-date assignment (DDA) is the first important task of shop floor control in wafer fabrication. Due-date related performance is impacted by the quality of the DDA rules. Assigning order due dates and timely delivering the goods to the customer will enhance customer service and competitive advantage. A new methodology for lead-time prediction, artificial neural network (ANN) prediction is considered in this work. An ANN-based DDA rule combined with simulation technology and statistical analysis is developed. Besides, regression-based DDA rules for wafer fabrication are modelled as benchmarking. Whether neural networks can outperform conventional and regression-based DDA rules taken from the literature is examined.From the simulation and statistical results, ANNbased DDA rules perform a better job in due-date prediction. ANN-based DDA rules have a lower tardiness rate than the other rules. ANN-based DDA rules have better sensitivity and variance than the other rules. Therefore, if the wafer fab information is not difficult to obtain, the ANN-based DDA rule can perform better due-date prediction. The SFM_sep and JIQ in regression-based and conventional rules are better than the others. KeywordsDue-date assignment AE Artificial neural network AE Wafer fabrication AE Simulation AE Shop floor control Abbreviations DDA due-date assignment ANN artificial neural network BPN back-propagation network SFC shop floor control AI artificial intelligence TWK due-date prediction rule based on total amount of works SLK due-date prediction rule based on slack time NOP due-date prediction rule based on number of operations JIQ due-date prediction rule based on current queue length in system JIBQ due-date prediction rule based on queue length in bottleneck station WIP work in process PSPpre-shop-pool KFM regression-based due-date prediction rule considering key factor SFM regression-based due-date prediction rule considering significant factors

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Neural networks applications in manufacturing processes

Cited by 55 publications

References 10 publications

Neural modelling, control and optimisation of an industrial grinding process

Neural modelling, control and optimisation of an industrial grinding process

Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning

Due-date assignment in wafer fabrication using artificial neural networks

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