An ANN approach for predicting the cutting inserts performances of different geometries in hard turning

Senthilkumar, N.; Tamizharasan, T.; Anandakrishnan, V.

doi:10.14743/apem2013.4.170

Cited by 21 publications

(13 citation statements)

References 25 publications

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“…There are many sources comprising the researches related to the use of superhard materials (SHM) in the machining of high-hardness materials [1,2]. Thence, the authors [3÷5] in their researches analyse the impact of a composition, hardness of tool and workpiece, as well as the elements of machining mode on the tool consistency and quality of the processed surface.…”

Section: Introductionmentioning

confidence: 99%

Impact of cutting elements on forces and roughness of surface during turning hard steel X160 CrMo V12 with CBN tool

et al. 2017

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Original scientific paper The goal of these researches is related to the analyses of the impact of elements of cutting mode on the change of cutting forces, quality of a machined surface and change of physical and mechanical properties of a surface layer of a CBN tool for turning of X160 CrMo V12 steel having a hardness of 60±2 HRC. The results of the analyses of tool surface wear indicate the presence of oxidation and changes of physical and mechanical properties in a tool surface layer. The evaluation clearly shows that a radial force (Fr) is dominant during the machining of this steel. The mathematical models of dependence of cutting force and a quality of machined surface were developed. It was ascertained that an oxidation of the tool surface, next to a contact zone toolcutting chip, leads to the increase of their hardness and reduction of their strength, and it finally leads to the emergence of micro-wear of a cutting knife and rapid tool wear. Keywords: CBN (corundum and boron-nitride); force; roughness; turning; wear Utjecaj parametara rezanja na sile i hrapavost površine pri tokarenju kaljenog čelika X160 CrMo V12 alatom od CBNIzvorni znanstveni članak Cilj ovih istraživanja se odnosi na istraživanje uticaja elemenata režima rezanja na promenu sila rezanja, kvaliteta obrađene površine i promene fizičko-mehaničkih svojstava u površinskom sloju alata od CBN pri struganju čelika X160 CrMo V12, tvrdoće 60±2 HRC. Rezultati istraživanja habanja površine alata ukazuju na prisustvo procesa oksidacije i promene fizičko-mehaničkih svojstava u površinskom sloju alata. Evaluacija sile jasno pokazuje da je radijalna sila (Fr) dominantna pri obradi ovog čelika. Razvijeni su matematički modeli zavisnosti sila rezanja i kvaliteta obrađene površine. Utvrđeno je da oksidacija površine alata, neposredno uz kontaktnu zonu alat-strugotina, dovodi do povećanja njihove tvrdoće i smanjenja čvrstoće, što u krajnjoj meri dovodi do pojave mikrokrzanja reznog sečiva a time i ubrzanog habanja alata.

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Section: Introductionmentioning

confidence: 99%

Impact of cutting elements on forces and roughness of surface during turning hard steel X160 CrMo V12 with CBN tool

et al. 2017

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“…An applied model based on an artificial neural network [4] has been used to predict wear at the flank face of the cutting inserts and the roughness of the machined workpiece's surface during the hard turning process.…”

Section: Introductionmentioning

confidence: 99%

Optimising external turning tool choice using AHP and ELECTRE II methods

2017

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Original scientific paper It is preferable to remove as much raw material in as short time as possible when processing materials using rough turning methods. Selecting the correct cutting tool at the beginning of the process is very important as this reduces the amount of material requiring later processing, in turn reducing the number of turning tools required, the number of tool changes and the total time taken to make a piece. In this paper, we present a system for selecting the best tools for external longitudinal turning. We take into account the characteristics of the machine, the power needed for cutting, the geometry and technological characteristics of the tools, and the machining parameters. Selection of the best tool for rough machining is made based on the processing time, the percentage of material to be cut, the cost of the tools and the number of tool changes. We ranked eight tools and used the analytical hierarchy process (AHP) method for multi-criteria optimisation then evaluated the results using ELECTRE II methods. The weight criteria were estimated using Saaty's pairwise comparison scale. Keywords: analytic hierarchy process; ELECTRE II method; multi-criteria decision-making; tool selection Izbor optimalnog alata za vanjsko tokarenje pomoću AHP i ELECTRE II metodeIzvorni znanstveni članak Prilikom obrade vanjskim grubim tokarenjem poželjno je skinuti što više materijala sa sirovog komada u što kraćem vremenu. Vrlo je važno izabrati tokarski nož koji prvi započinje obradu, kako bi ostatak materijala za kasniju obradu bio što manji. Time se smanjuje ukupno potrebni broj alata za tokarenje, broj izmjena alata te ukupno vrijeme izrade komada. U radu je prikazan sistem za izbor najpovoljnijeg alata za vanjsko uzdužno tokarenje uz uvažavanje karakteristika stroja, snage potrebne za rezanje, geometriju i tehnološke karakteristike alata te parametara obrade. Izbor najoptimalnijeg alata za grubu obradu izvršen je prema kriterijima vrijeme obrade, postotak odrezanoga materijala, cijena alata po reznoj oštrici te broju prolaza alata. Kod rangiranja rezultata obrade za 8 alata korištena je metoda za višekriterijsku optimizaciju AHP, a rezultati su dodatno vrednovani po ELECTRE II metodi. Vrijednosti težina kriterija procijenjene su prema Saty-jevoj ljestvici usporedbe kriterija u parovima.

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“…and with the goal of cost-efficient processing and shortening of time. The set goals are achieved by improving the conditions of machining by applying neural networks (NN) and genetic programming [10,11], as well as the approaches that use other methods [12÷14]. In the optimization of machining parameters, NN are often combined with GA.…”

Section: Introductionmentioning

confidence: 99%

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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An ANN approach for predicting the cutting inserts performances of different geometries in hard turning

Cited by 21 publications

References 25 publications

Impact of cutting elements on forces and roughness of surface during turning hard steel X160 CrMo V12 with CBN tool

Impact of cutting elements on forces and roughness of surface during turning hard steel X160 CrMo V12 with CBN tool

Optimising external turning tool choice using AHP and ELECTRE II methods

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

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