FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2289772362> ?p ?o ?g. }

• W2289772362 endingPage "1431" @default.
• W2289772362 startingPage "1427" @default.
• W2289772362 abstract "This paper presents an approach for the determination of the optimal cutting parameters (spindle speed, feed rate, depth of cut and engagement) leading to minimum surface roughness in face milling of high silicon stainless steel by coupling neural network (NN) and Electromagnetism-like Algorithm (EM). In this regard, the advantages of statistical experimental design technique, experimental measurements, artificial neural network, and Electromagnetism-like optimization method are exploited in an integrated manner. To this end, numerous experiments on this stainless steel were conducted to obtain surface roughness values. A predictive model for surface roughness is created by using a back propogation neural network, then the optimization problem was solved by using EM optimization. Additional experiments were performed to validate optimum surface roughness value predicted by EM algorithm. It is clearly seen that a good agreement is observed between the predicted values by EM coupled with feed forward neural network and experimental measurements. The obtained results show that the EM algorithm coupled with back propogation neural network is an efficient and accurate method in approaching the global minimum of surface roughness in face milling. Keywords—cutting parameters, face milling, surface roughness, artificial neural network, Electromagnetism-like algorithm, Topal et al. (3) proposed an ANN model for predicting the surface roughness from machining parameters such as cutting speed, feed rate, and depth of cut in milling of AISI 1040 steel. Dhokia et al. (4) developed a model based on neural network for prediction surface roughness behavior of the surface roughness for machined polypropylene products. Onwubolu (5) presented a hybrid modeling approach, based on the group method of data handling and the differential evolution population-based algorithm, for modeling and predicting surface roughness in turning operations. Most of the time, it is very difficult to find the related analytical or empirical expressions and proper coefficients to calculate the optimal cutting conditions for the considered material and tool. Recently analytical and empirical models have been developed by using neural network and response surface methodology in order to calculate surface roughness for several materials (6-8). Also the neural network model coupled with the GA is proposed to determine the optimal machining for surface roughness (9-11). Electromagnetism-like algorithm (EM) is a population-based meta-heuristic method for solving optimization problems. Experimental results show that EM algorithm is capable of finding good solution. Experimental results show that EM algorithm is capable of finding good solution (12). A meta-heuristic algorithm, based on electromagnetism-like mechanism (EM), has been successfully implemented in a few combinatorial problems. Debels et al. used a meta-heuristic algorithm capable of providing near-optimal heuristic solutions to solve the resource-constrained project scheduling problem, for relatively large instances (13). In this study, an ANN model based on experimental data was developed to predict surface roughness in face milling. The factors considered in the experiment were cutting speed, feed per tooth, depth of cut, and engagement. The developed ANN model includes more cutting parameters, which are more effective on surface roughness, than those in the literature. EM algorithm was used to find optimum cutting parameters leading to minimum surface roughness." @default.
• W2289772362 created "2016-06-24" @default.
• W2289772362 creator A5010315032 @default.
• W2289772362 creator A5011150626 @default.
• W2289772362 creator A5073055736 @default.
• W2289772362 date "2012-08-26" @default.
• W2289772362 modified "2023-09-26" @default.
• W2289772362 title "Optimum Surface Roughness Prediction in Face Milling of High Silicon Stainless Steel" @default.
• W2289772362 cites W1596195064 @default.
• W2289772362 cites W1978251268 @default.
• W2289772362 cites W1983944951 @default.
• W2289772362 cites W2040115889 @default.
• W2289772362 cites W2041228906 @default.
• W2289772362 cites W2044506241 @default.
• W2289772362 cites W2052336655 @default.
• W2289772362 cites W2052509829 @default.
• W2289772362 cites W2069929444 @default.
• W2289772362 cites W2085718660 @default.
• W2289772362 cites W2136459762 @default.
• W2289772362 cites W2514560812 @default.
• W2289772362 cites W2997148123 @default.
• W2289772362 cites W2011227376 @default.
• W2289772362 hasPublicationYear "2012" @default.
• W2289772362 type Work @default.
• W2289772362 sameAs 2289772362 @default.
• W2289772362 citedByCount "1" @default.
• W2289772362 countsByYear W22897723622012 @default.
• W2289772362 crossrefType "journal-article" @default.
• W2289772362 hasAuthorship W2289772362A5010315032 @default.
• W2289772362 hasAuthorship W2289772362A5011150626 @default.
• W2289772362 hasAuthorship W2289772362A5073055736 @default.
• W2289772362 hasConcept C107365816 @default.
• W2289772362 hasConcept C121332964 @default.
• W2289772362 hasConcept C127413603 @default.
• W2289772362 hasConcept C144024400 @default.
• W2289772362 hasConcept C154945302 @default.
• W2289772362 hasConcept C159985019 @default.
• W2289772362 hasConcept C191897082 @default.
• W2289772362 hasConcept C192562407 @default.
• W2289772362 hasConcept C2779304628 @default.
• W2289772362 hasConcept C36289849 @default.
• W2289772362 hasConcept C41008148 @default.
• W2289772362 hasConcept C50644808 @default.
• W2289772362 hasConcept C523214423 @default.
• W2289772362 hasConcept C62520636 @default.
• W2289772362 hasConcept C71039073 @default.
• W2289772362 hasConcept C78519656 @default.
• W2289772362 hasConcept C91748784 @default.
• W2289772362 hasConceptScore W2289772362C107365816 @default.
• W2289772362 hasConceptScore W2289772362C121332964 @default.
• W2289772362 hasConceptScore W2289772362C127413603 @default.
• W2289772362 hasConceptScore W2289772362C144024400 @default.
• W2289772362 hasConceptScore W2289772362C154945302 @default.
• W2289772362 hasConceptScore W2289772362C159985019 @default.
• W2289772362 hasConceptScore W2289772362C191897082 @default.
• W2289772362 hasConceptScore W2289772362C192562407 @default.
• W2289772362 hasConceptScore W2289772362C2779304628 @default.
• W2289772362 hasConceptScore W2289772362C36289849 @default.
• W2289772362 hasConceptScore W2289772362C41008148 @default.
• W2289772362 hasConceptScore W2289772362C50644808 @default.
• W2289772362 hasConceptScore W2289772362C523214423 @default.
• W2289772362 hasConceptScore W2289772362C62520636 @default.
• W2289772362 hasConceptScore W2289772362C71039073 @default.
• W2289772362 hasConceptScore W2289772362C78519656 @default.
• W2289772362 hasConceptScore W2289772362C91748784 @default.
• W2289772362 hasIssue "8" @default.
• W2289772362 hasLocation W22897723621 @default.
• W2289772362 hasOpenAccess W2289772362 @default.
• W2289772362 hasPrimaryLocation W22897723621 @default.
• W2289772362 hasRelatedWork W153394789 @default.
• W2289772362 hasRelatedWork W1553274644 @default.
• W2289772362 hasRelatedWork W1567953936 @default.
• W2289772362 hasRelatedWork W194227629 @default.
• W2289772362 hasRelatedWork W1978618432 @default.
• W2289772362 hasRelatedWork W1983460576 @default.
• W2289772362 hasRelatedWork W2009080901 @default.
• W2289772362 hasRelatedWork W2009619063 @default.
• W2289772362 hasRelatedWork W2018816054 @default.
• W2289772362 hasRelatedWork W2022699427 @default.
• W2289772362 hasRelatedWork W2030640017 @default.
• W2289772362 hasRelatedWork W2044506241 @default.
• W2289772362 hasRelatedWork W2161617772 @default.
• W2289772362 hasRelatedWork W2162141840 @default.
• W2289772362 hasRelatedWork W2181191677 @default.
• W2289772362 hasRelatedWork W2184494293 @default.
• W2289772362 hasRelatedWork W2229319820 @default.
• W2289772362 hasRelatedWork W2947429827 @default.
• W2289772362 hasRelatedWork W565456890 @default.
• W2289772362 hasRelatedWork W5678890 @default.
• W2289772362 hasVolume "6" @default.
• W2289772362 isParatext "false" @default.
• W2289772362 isRetracted "false" @default.
• W2289772362 magId "2289772362" @default.
• W2289772362 workType "article" @default.