An intelligent system approach for surface roughness and vibrations prediction in cylindrical grinding

Asiltürk, İlhan; Tınkır, Mustafa; Monuayri, Hazim El; Çelik, Levent

doi:10.1080/0951192x.2012.665185

Cited by 25 publications

(11 citation statements)

References 14 publications

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“…There are two investigation techniques of soft computing: Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). In the field of grinding, next to the soft computing techniques, the investigators also used different techniques like: Adaptive Neuro -Fuzzy Inference System [23] and Self -organizing map Neural Network with unsupervised learning, the feed forward Neural Network with supervised learning [24] and ANOVA analysis [25]. Therefore, in all described papers, the conclusions generally confirm the justification of application of soft computing techniques for solving the investigated technological problems.…”

Section: Introductionsupporting

confidence: 54%

Use of soft computing technique for modelling and prediction of CNC grinding process

et al. 2016

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Original scientific paper Due to the complexity of grinding process of multilayer ceramics, and the need for a specific product quality, the choice of optimal technological parameters is a challenging task for the manufacturers. The main aim of investigation is to secure the demanded final product quality (plane parallelism) in the function of input parameters (machine, machine operator, foil and production line). "Soft computing techniques" are becoming more interesting to the researchers for the modelling of processing parameters of complex technological processes. In this paper, a soft computing technique, known as the Artificial Neural Networks (ANN), is used for the modelling and prediction of parameters of technological process of CNC grinding of multilayer ceramics. The results show that the ANN with the back-propagation algorithm justifies the application also to this problem. By designing different architectures of ANN (learning rules, transfer functions, number and structure of hidden layers and other) on the set of data from the productiontechnological process, the best result of RMS error (10,76 %) in the process of learning and 12,07 % in the process of validation was achieved. The achieved results confirm the acceptability and the application of this investigation in the technological and operational preparation of production. Keywords: grinding, neural networks, prediction, soft computing Uporaba tehnike mekog računalstva za modeliranje i predviđanje postupka CNC brušenjaIzvorni znanstveni rad Zbog složenosti procesa brušenja višeslojne keramike te osiguranja zahtijevane kvalitete proizvoda, odabir optimalnih tehnoloških parametara je izazovan zadatak za proizvođače. Osigurati traženu izlaznu kvalitetu proizvoda (paralelnost površina) u funkciji ulaznih parametara (stroj, operater stroja, folija i proizvodna linija) predstavlja glavni cilj istraživanja. "Tehnike mekog računalstva" dobivaju pozornost istraživača za modeliranje procesnih parametara složenih tehnoloških procesa. U ovom radu koristi se tehnika mekog računalstva poznata kao umjetne neuronske mreže (ANN) za modeliranje i predviđanje parametara tehnološkog procesa CNC brušenja višeslojne keramike. Rezultati su pokazali da ANN s algoritmom širenja unazad potvrđuje primjenu i na ovaj problem. Oblikovanjem različitih arhitektura ANN (pravila učenja, prijenosne funkcije, broj i strukture skrivenih slojeva i drugi) na setu podataka iz proizvodno -tehnološkog procesa ostvaren je najbolji rezultat RMS greške od 10,76 % u procesu učenja i 12,07 % u procesu validacije. Ostvareni rezultati potvrđuju prihvatljivost i primjenu ovog istraživanja u tehnološkoj i operativnoj pripremi proizvodnje.

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Section: Introductionsupporting

confidence: 54%

Use of soft computing technique for modelling and prediction of CNC grinding process

et al. 2016

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“…Based on experimental studies on rough-grinding and finish-grinding processes, it was concluded that the proposed approach would provide better solutions as compared to the already adopted methods. Asiltürk et al [18] proposed the application of an adaptive network-based fuzzy inference system for effectively predicting SR and vibration in cylindrical grinding, while taking into account workpiece speed, feed rate and depth of cut as the input parameters. Khan et al [19] presented the application of GRA technique for optimizing an in-feed centerless cylindrical grinding process.…”

Section: Literature Reviewmentioning

confidence: 99%

Parametric Analysis of a Grinding Process Using the Rough Sets Theory

Agarwal

Dandge²,

Chakraborty

2020

FU Mech Eng

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With continuous automation of the manufacturing industries and the development of advanced data acquisition systems, a huge volume of manufacturing-related data is now available which can be effectively mined to extract valuable knowledge and unfold the hidden patterns. In this paper, a data mining tool, in the form of the rough sets theory, is applied to a grinding process to investigate the effects of its various input parameters on the responses. Rotational speed of the grinding wheel, depth of cut and type of the cutting fluid are grinding parameters, and average surface roughness, amplitude of vibration and grinding ratio are the responses. The best parametric settings of the grinding parameters are also derived to control the quality characteristics of the ground components. The developed decision rules are quite easy to understand and can truly predict the response values at varying combinations of the considered grinding parameters.

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“…Once the workpiece has been designed, its working performance will be mainly decided by its surface topography after its manufacturing process. [16][17][18] Good topography can improve many applying features of workpiece, for example, workpiece with smooth surface topography can reduce its contacting area and it will improve the wear resistance of workpiece. Surface with small fluctuation can tolerate more inner stress, and fatigue crack is not generated easily.…”

Section: Introductionmentioning

confidence: 99%

Research on machining topography of point grinding based on correlation function method

Shi

Xiu

Zhu

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part B:

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Point grinding uses the point contact to shape surface topography of workpiece which has high machining precision. Its surface topography has a close relation with its service performance. In order to study the topography of point grinding, these authors carried out the experiment and established a new assessing model based on the correlation function method. The inner characteristics of topography are revealed by self-correlation and mutual correlation function method and its mechanism is analyzed. The study shows that the topography of point grinding has obvious periodical portions, which means it is consistent and uniform. The mutual correlations of every direction show the surface topography of point grinding is isotropic. Point grinding surface has better consistency under larger included angle and smaller grinding depth. And it has better isotropy under larger included angle. Surface roughness has the opposite rule from the consistency and isotropy. The influence of point grinding angle on surface topology is much larger than grinding depth.

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An intelligent system approach for surface roughness and vibrations prediction in cylindrical grinding

Cited by 25 publications

References 14 publications

Use of soft computing technique for modelling and prediction of CNC grinding process

Use of soft computing technique for modelling and prediction of CNC grinding process

Parametric Analysis of a Grinding Process Using the Rough Sets Theory

Research on machining topography of point grinding based on correlation function method

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