Name
Abstract | ||
|---|---|---|
Display Omitted A system that integrates prediction, optimization and control with an emphasis on product quality in view of the surface roughness.The choice of cutting parameters that ensure the desired product quality in a known unit time.The flexibility of the system according to the current demands on product quality. The paper presents a system that, according to the requirements referring to the product quality given in surface roughness, with minimum machining time and maximum metal removal rate, recommends optimal cutting parameters with the possibility of surface roughness control during the machining process. The suggested evolutionary neuro-fuzzy system for evaluation of surface roughness is composed of three units: surface roughness prediction by cutting parameters, multi-objective optimization of cutting parameters aimed at minimum machining time and maximum metal removal rate and control of obtained or required surface roughness by means of the features quantified from digital image of the observed machined surface. The paper outlines the idea and architecture of the system as well as the possibilities of implementation. The obtained results, illustrated by experimental research, justify the application and further development of the suggested evolutionary neuro-fuzzy system for evaluation of surface roughness within the given constraints. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.asoc.2016.10.010 | Appl. Soft Comput. |
Keywords | Field | DocType |
Surface roughness,Cutting parameters,Adaptive neuro-fuzzy inference system (ANFIS),Fuzzy inference system (FIS),Genetic algorithm (GA) | Mathematical optimization,Neuro-fuzzy,Mechanical engineering,Machining process,Digital image,Artificial intelligence,Mathematics,Surface roughness,Machining time | Journal |
Volume | Issue | ISSN |
52 | C | 1568-4946 |
Citations | PageRank | References |
4 | 0.44 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ilija Svalina | 1 | 4 | 0.44 |
| Goran Simunovic | 2 | 30 | 1.79 |
| Tomislav Saric | 3 | 4 | 0.44 |
| Roberto Lujic | 4 | 4 | 0.44 |