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Pregled bibliografske jedinice broj: 830266

USE OF SOFT COMPUTING TECHNIQUE FOR MODELLING AND PREDICTION OF CNC GRINDING PROCESS


Šarić, Tomislav; Šimunović, Goran; Lujić, Roberto; Šimunović, Katica; Antić, Aco
USE OF SOFT COMPUTING TECHNIQUE FOR MODELLING AND PREDICTION OF CNC GRINDING PROCESS // Tehnički vjesnik : znanstveno-stručni časopis tehničkih fakulteta Sveučilišta u Osijeku, 23 (2016), 4; 1123-1130 doi:10.17559/TV-20160405151333 (međunarodna recenzija, članak, znanstveni)


Naslov
USE OF SOFT COMPUTING TECHNIQUE FOR MODELLING AND PREDICTION OF CNC GRINDING PROCESS

Autori
Šarić, Tomislav ; Šimunović, Goran ; Lujić, Roberto ; Šimunović, Katica ; Antić, Aco

Izvornik
Tehnički vjesnik : znanstveno-stručni časopis tehničkih fakulteta Sveučilišta u Osijeku (1330-3651) 23 (2016), 4; 1123-1130

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Grinding; neural networks; prediction; soft computing

Sažetak
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 production - technological 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.

Izvorni jezik
Engleski

Znanstvena područja
Strojarstvo



POVEZANOST RADA


Ustanove
Strojarski fakultet, Slavonski Brod

Časopis indeksira:


  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


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