Optimizing a dental milling process by means of soft computing techniques

Vera, Vicente; García, Alberto Luis García; Suárez-García, María Jesús; Hernando, Beatriz; Redondo, Raquel; Corchado, Emilio; Sanchez, Maria Araceli; Gil, Ana Belén; Sedano, Javier

doi:10.1109/isda.2010.5687111

Cited by 6 publications

(9 citation statements)

References 25 publications

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“…adjusted between 15-25 micrometers which would lead to success in dental restorative treatment. Because of these and other reasons it is currently of great interest to optimise processes [8,9] related to the preparation of dental prostheses (dental structures of materials such as cobalt chromium, titanium, ceramics and/or resin) characterised by a high precision of adjustment in micrometers. Artificial Intelligence [10], in conjunction with optimisation and identification algorithms [11,12], is a very appropriate technology for addressing the development of such intelligent tools.…”

Section: Introductionmentioning

confidence: 99%

Applying soft computing techniques to optimise a dental milling process

Vera¹,

Corchado

Redondo

et al. 2013

Neurocomputing

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This study presents a novel soft computing procedure based on the application of artificial neural networks, genetic algorithms and identification systems, which makes it possible to optimise the implementation conditions in the manufacturing process of high precision parts, including finishing precision, while saving both time and financial costs and/or energy. This novel intelligent procedure is based on the following phases. Firstly, a neural model extracts the internal structure and the relevant features of the data set representing the system. Secondly, the dynamic system performance of different variables is specifically modelled using a supervised neural model and identification techniques. This constitutes the model for the fitness function of the production process, using relevant features of the data set. Finally, a genetic algorithm is used to optimise the machine parameters from a non parametric fitness function. The novel proposed approach was tested under real dental milling processes using a highprecision machining centre with five axes, requiring high finishing precision of measures in micrometers with a large number of process factors to analyse. The results of the experiment, which validate the performance of the proposed approach, are presented in this study.

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Section: Introductionmentioning

confidence: 99%

Applying soft computing techniques to optimise a dental milling process

Vera¹,

Corchado

Redondo

et al. 2013

Neurocomputing

Self Cite

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“…The final model is obtained using the full data set. Next, several different indexes were used to validate the models [18,17] such as the percentage representation of the estimated model, the loss (error) function (V) and the generalization error value.…”

Section: Dental Miling Time-error Prediction In Industrymentioning

confidence: 99%

Evaluation of Novel Soft Computing Methods for the Prediction of the Dental Milling Time-Error Parameter

Krömer

Novosád

Snåšel

et al. 2013

Advances in Intelligent Systems and Computing

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This multidisciplinary study presents the application of two well known soft computing methods-flexible neural trees, and evolutionary fuzzy rules-for the prediction of the error parameter between real dental milling time and forecast given by the dental milling machine. In this study a real data set obtained by a dynamic machining center with five axes simultaneously is analyzed to empirically test the novel system in order to optimize the time error.

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“…If after applying these models, a clear internal structure can be identified, this means that the data recorded is informative enough. Otherwise, data must be properly collected again [17], [18].…”

Section: Soft Computing For Data Structure Analysismentioning

confidence: 99%

“…Validation ensures that the selected model meets the necessary conditions for estimation and prediction. Typically, validation is carried out using three different methods: the residual analysis -by means of a correlation test between inputs, their residuals and their combinations-; the mean squared error (MSE) and the generalisation error value -normalised sum of squared errors (NSSE) -and finally a graphical comparison between the desired outputs and the model outcomes through simulation [20], [17], [18].…”

Section: System Modelling Using Identification Algorithmsmentioning

confidence: 99%

“…Moreover, several different indexes were used to validate the models [17], [18] such as the percentage representation of the estimated model; the graphical representation for the prediction - The percentage representation of the estimated model is calculated as the normalised mean error for the prediction (FIT1). The loss function or error function (V) is the numeric value of the mean square error (MSE) that is computed with the estimation data set.…”

Section: Modelling a Normal Dental Milling Operationmentioning

confidence: 99%

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A Hybrid System for Dental Milling Parameters Optimisation

Vera¹,

Sedano

Corchado

et al. 2011

Lecture Notes in Computer Science

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This study presents a novel hybrid intelligent system which focuses on the optimisation of machine parameters for dental milling purposes Based on the following phases. Firstly, an unsupervised neural model extracts the internal structure of a data set describing the model and also the relevant features of the data set which represents the system. Secondly, the dynamic system performance of different variables is specifically modelled using a supervised neural model and identification techniques from relevant features of the data set. This model constitutes the goal function of the production process. Finally, a genetic algorithm is used to optimise the machine parameters from a non parametric fitness function. The reliability of the proposed novel hybrid system is validated with a real industrial use case, based on the optimisation of a highprecision machining centre with five axes for dental milling purposes.

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Optimizing a dental milling process by means of soft computing techniques

Cited by 6 publications

References 25 publications

Applying soft computing techniques to optimise a dental milling process

Applying soft computing techniques to optimise a dental milling process

Evaluation of Novel Soft Computing Methods for the Prediction of the Dental Milling Time-Error Parameter

A Hybrid System for Dental Milling Parameters Optimisation

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