How deals with discrete data for the reduction of simulation models using neural network

Thomas, Philippe; Thomas, André

doi:10.3182/20090603-3-ru-2001.0490

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…According to the experimental design approach, we can classify these variables into 2 types of factors: internal ones (load factor, number of passes, time per table, liter per table, basis weight, number of layers, number of products and drying time) and external ones (temperature, humidity, pressure). 2 factors (passes number and number of layers) are discrete ones that can each take 3 states and that are binarized (Thomas and Thomas 2009).…”

Section: Resultsmentioning

confidence: 99%

Improving production process performance thanks to neuronal analysis

Noyel¹,

Thomas²,

Charpentier³

et al. 2013

IFAC Proceedings Volumes

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Product quality level is become a key factor for companies' competitiveness. A lot of time and money are required to ensure and guaranty it. Besides, motivated by the need of traceability, collecting production data is now commonplace in most companies. Our paper aims to show that we can ensure the required quality thanks to an "on-line quality approch" and proposes a neural network based process to determine the optimal setting for production machines. We will illustrate this with the Acta-Mobilier case, which is a high quality lacquerer company.

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Section: Resultsmentioning

confidence: 99%

Improving production process performance thanks to neuronal analysis

Noyel¹,

Thomas²,

Charpentier³

et al. 2013

IFAC Proceedings Volumes

Self Cite

View full text Add to dashboard Cite

show abstract

“…The main advantage of this approach is that only one neural network models the entire system. However, this model includes more inputs and hidden neurons and so the computational times increase during the learning and exploitation steps [23]. These two approaches are not paradoxical.…”

Section: Illustrationmentioning

confidence: 99%

An Approach to Data Mining for Product-driven Systems

Thomas¹,

Thomas²

2013

Service Orientation in Holonic and Multi Agent Manufacturing and Robotics

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The first objective of this chapter is to highlight some new Product Driven Systems (PDS) issues. Effectively, several possibilities have been proposed to give to products or objects capacities to react to environment modifications (especially in manufacturing and logistics context here). In particular, bioinspired approaches are now promising. All these new perspectives lead putting products in action according to collected information. That's why all technics leading to exploit and organize data are necessary. The main objective of the chapter is addressed in a second part, where we highlight why learning machines could be seen as a new way to transform data in useful knowledge.

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“…According to the experimental design approach, we can classify these variables into two types of factors: internal ones (load factor, number of passes, time per table, liter per table, basis weight, number of layers, number of products and drying time) and external ones (temperature, humidity, pressure). Two factors (passes number and number of layers) are discrete ones that can each take three states and that are binarized (Thomas and Thomas 2009).…”

Section: Presentation Of the Processmentioning

confidence: 99%

Neural Networks Ensemble for Quality Monitoring

2013

Proceedings of the 5th International Joint Conference on Computational Intelligence

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Product quality level is a key concept for companies' competitiveness. Different tools may be used to improve quality such as the seven basic quality tools or experimental design. In addition, the need of traceability leads companies to collect and store production data. Our paper aims to show that we can ensure the required quality thanks to an "on line quality approach" based on exploitation of collected data by using neural networks tools. A neural networks ensemble is proposed to classify quality results which can be used in order to prevent defects occurrence. This approach is illustrated on an industrial lacquering process. Results of the neural networks ensemble are compared with the ones obtained with the best neural network classifier.

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How deals with discrete data for the reduction of simulation models using neural network

Cited by 5 publications

References 12 publications

Improving production process performance thanks to neuronal analysis

Improving production process performance thanks to neuronal analysis

An Approach to Data Mining for Product-driven Systems

Neural Networks Ensemble for Quality Monitoring

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