Application of Artificial Intelligent to Predict Surface Roughness

Asiltürk, İlhan

doi:10.1111/j.1747-1567.2012.00827.x

Cited by 16 publications

(11 citation statements)

References 24 publications

(34 reference statements)

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“…The detected surface roughness values were smaller than the plaque accumulation level (0.2 µm) 31) and the high microhardness values indicated high wear resistance 32) . The obtained accuracy of surface roughness and microhardness prediction was also acceptable like the other studies [13][14][15]18) . The experimental and model prediction results demonstrated that the microhardness of denture teeth decreased during the immersion period while the surface roughness increased.…”

Section: Discussionsupporting

confidence: 81%

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The accuracy of the prediction models for surface roughness and micro hardness of denture teeth

Deniz

Özkan

2019

Dent. Mater. J.

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The paper aimed to compare the performance of artificial neural network (ANN) model with the results of in vitro experiments. For these experiments, maxillary molars of four different denture teeth were subjected to tea, coffee, cola, cherry juice, distilled water. Vickers microhardness and surface roughness values were measured. Subsequently, ANN model for the prediction of microhardness and surface roughness of different denture teeth were examined. A back-propagation ANN has been used to develop a model relating to the amount of microhardness and surface roughness. The independent variables of the model are distilled water, tea, filtered coffee, cola, cherry juice, time and denture teeth. Microhardness and surface roughness were chosen as the dependent variables. According to the results, a neural network architecture having one input layer with ten neurons, two hidden layers with six neurons, one output layer with two neurons and an epoch size of 48 gives better prediction. Prediction models for dental materials could also be supportive for in vitro studies.

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Section: Discussionsupporting

confidence: 81%

“…Neural networks do not require any prior knowledge about the relationships that exist between the states of the parameters. There is now extensive literature showing how neural networks may be used for classification, estimation and prediction [13][14][15][16][17][18] . However, their implementations are new for denture materials.…”

Section: Introductionmentioning

confidence: 99%

The accuracy of the prediction models for surface roughness and micro hardness of denture teeth

Deniz

Özkan

2019

Dent. Mater. J.

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“…20 Roughness can be evaluated through different parameters, such as mean roughness (Ra), root mean square (rms), roughness (Rq), and maximum peak-to-valley roughness (Ry or Rmax). 25 Using a Surftest SJ-201P roughness tester (portable surface roughness tester), the machining quality was checked by measuring the arithmetic mean of the roughness profile, denoted as Ra. A profile measuring device is generally based on a tactile measurement principle.…”

Section: Evaluation Of the Hole Qualitymentioning

confidence: 99%

Experimental study of drilling white Calacatta–Carrara marble using artificial neural approach

Abbassi

Akrichi

Yahia

2019

Advances in Mechanical Engineering

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Highly accurate marble processing is increasingly needed to comply with tight parametric/geometric tolerances and surface integrity specifications encountered while structuring, sculpture, and decorating. In this study, a new approach based on the artificial neural network technique is evaluated for the prediction of process parameters in the machining of white Calacatta-Carrara marble. The rotation speed, feed speed, drill bit diameter, drill bit height, number of pecking cycles, and drilling depth were considered as input factors. Corresponding surface roughness, hole circularity, hole cylindricity, and hole-location error were sought in output. A series of experiments was carried out using a 5-axes computer numerical control vertical machining center (OMAG) to obtain the data used for the training and testing of the artificial neural network with reasonable accuracy, under varying machining conditions. A MATLAB TM interface was developed to predict surface roughness and geometric defects (circularity, cylindricity, and localization). A 6 3 4 size multilayered neural network was developed. The number of iterations was 1000 and no smoothing factor was used. The drill quality (holelocation error, hole circularity, and hole cylindricity) and the surface roughness were modeled and evaluated individually. One hidden layer used for all models, with the number of neurons for all the responses being executed separately, was 12 while the number of neurons in the hidden layer, with all the responses executed together, was 14. In conclusion, from the obtained verified experimentally optimization results, the errors are all within acceptable ranges, which, again, confirm that the artificial neural network technique is an efficient and accurate method in predicting responses in drilling.

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“…Among them, first of all, the product designation indicators that characterize the main functions of the product and the scope of its application, and which include classification, operational and structural indicators, are highlighted. The latter, in particular, include the dimensions (subject to their tolerances) of the product and its parts [3,4].…”

Section: Introductionmentioning

confidence: 99%

Method of Calculating Intermediate Diametral Sizes and Allowances for Designing Technology of Manufacture of Details

Khalimonenko

Pompeev

Timofeev³

2017

Proceedings of the International Conference "Actual Issues of Mechanical Engineering" 2017 (AIME 2017)

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-The method of calculation of intermediate diametrical sizes and allowances is considered in the article when designing the technology of manufacturing machine parts. This method makes it possible to ensure the quality of manufacturing machine parts by preliminarily determining the adjustment dimensions before machining. The proposed method allows determining allowances for machining, taking into account the error of the previous processing.Keywords -calculation of allowances, machining, processing technology of machine parts, quality of machine parts, calculation of beats, eccentricity.

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Application of Artificial Intelligent to Predict Surface Roughness

Cited by 16 publications

References 24 publications

The accuracy of the prediction models for surface roughness and micro hardness of denture teeth

The accuracy of the prediction models for surface roughness and micro hardness of denture teeth

Experimental study of drilling white Calacatta–Carrara marble using artificial neural approach

Method of Calculating Intermediate Diametral Sizes and Allowances for Designing Technology of Manufacture of Details

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