Use of Neural Networks in Prediction and Simulation of Steel Surface Roughness

Šarić, Tomislav; Šimunović, Goran; Šimunović, Katica

doi:10.2507/ijsimm12(4)2.241

Cited by 36 publications

(29 citation statements)

References 21 publications

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“…The surface roughness was affected by many controlled and uncontrolled process parameters that were difficult to achieve and continuously monitor [20].…”

Section: Experiments 31 Manufacture Using Cad/cam Toolsmentioning

confidence: 99%

Initial study of immersion thermal manikin development and its manufacture from solid blocks

Pahole

Valentan

Pavlinić³

et al. 2015

Teh. vjesn.

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Preliminary notesArticle presents the development and manufacture of a new generation of thermal manikins that can be used for testing different personal protective equipment as used under extreme environmental conditions. The demonstrated steps from the initial idea towards the final product were taken with the help of modern CAD/CAM tools and CNC manufacturing procedures. This article also presents some of the problems that occurred and had to be solved during the designing, manufacturing and assembly of the thermal manikin. Keywords: extreme environment; personal protective equipment; safety; thermal manikin Početna analiza razvoja termalne lutke za uporabu u vodi i njezina proizvodnja iz punih blokovaPrethodno priopćenje U radu se opisuje razvoj i proizvodnja nove generacije termalnih modela (lutaka) koji se mogu koristiti za ispitivanje različite osobne zaštitne opreme kada se rabi u ekstremnim uvjetima. Sve od početne ideje do konačnog proizvoda provedeno je uz pomoć suvremenih CAD/CAM alata i CNC proizvodnih postupaka. U radu se iznose i neki problemi koji su se pojavili tijekom konstruiranja, izrade i sastavljanja termalne lutke, a trebalo ih je riješiti.

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“…The surface roughness was affected by many controlled and uncontrolled process parameters that were difficult to achieve and continuously monitor [20].…”

Section: Experiments 31 Manufacture Using Cad/cam Toolsmentioning

confidence: 99%

Initial study of immersion thermal manikin development and its manufacture from solid blocks

Pahole

Valentan

Pavlinić³

et al. 2015

Teh. vjesn.

View full text Add to dashboard Cite

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“…Thus, this paper explores the possibility of using ANN for the prediction of the vibration received in the driver's seat. Few works have been found regarding the use of ANN for this application [17][18][19][20][21][22][23] so we believe that the methodology presented and the results obtained in this work can be useful for the assessment of the vibrational environment at driver's place.…”

Section: Introductionmentioning

confidence: 99%

Neural Network and Training Strategy Design for Train Drivers’ Vibration Dose Simulation

Muñoz-Guijosa¹,

Riesco²,

Olmedo³

2017

Int. j. simul. model.

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Vibration can cause professional illnesses in train drivers, giving also rise to lawsuits to the employer. A possible cause may be the lack of systematic vibration estimation processes, due to operational complexities, subjectivities involved and the cost of dedicated tests. Estimation quality may be improved by using a driver seat model along with cabin floor vibration data acquired during the train dynamic approval tests. However, due to the nonlinearities present, analytical models frequently show inaccurate results. This work deals with the design of an appropriate neural network for predicting the seat-driver interface vibration, based on selected and processed cabin floor acceleration data obtained during the dynamic approval tests. Network type, input signals set and signal conditioning have considerable impact on the simulation accuracy. Results show good correlation between simulated and experimental data, even better between simulated and measured standard vibration dose indicators, being RMS errors between 3.9 % and 9.4 % and peak factor errors between 0.8 % and 9.6 %.

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“…In that case, by means of camera, the tool image is captured from which advanced image processing algorithms extract the information representing actual tool condition, while on the basis of the information the tool condition can be classified [15][16][17]. Additionaly, many researchers deal with developing models for predicting output responses such as tool wear based on cutting parameters [18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Computer Vision-Based Approach to End Mill Tool Monitoring

Klancnik¹,

Ficko²,

Balic³

et al. 2015

Int. j. simul. model.

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The paper is concerned about the system of automatic detecting of wear and damages of the end mill tool by the use of computer vision. By using the algorithms developed for image segmentation and by an innovative approach to extraction of features describing individual end mill tool tooth the information representing significant features of the individual tool tooth is effectively gained from the captured image. The proposed approach to feature extraction is robust and independent of the scale and rotation of the end mill tool in the image. The features vectors have been classified by two approaches, i.e., k-nearest neighbour algorithm and artificial neural network. Both approaches have been tested by the test base of tool images and the results mutually compared. The classification has been validated by 10-fold cross-validation method. The best precision of classification (92.63 %) has been reached by the use of artificial neural network. Simulation results have confirmed that the proposed approach can improve monitoring of tool wear and damages and, consequently, the effectiveness and reliability of CNC milling machine tool.

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Use of Neural Networks in Prediction and Simulation of Steel Surface Roughness

Cited by 36 publications

References 21 publications

Initial study of immersion thermal manikin development and its manufacture from solid blocks

Initial study of immersion thermal manikin development and its manufacture from solid blocks

Neural Network and Training Strategy Design for Train Drivers’ Vibration Dose Simulation

Computer Vision-Based Approach to End Mill Tool Monitoring

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