Verification of Fuzzy Inference System for Cutting Speed while WEDM for the Abrasion-Resistant Steel Creusabro by Conventional Statistical Methods

• Published in: Metals (Basel ) Vol. 10; no. 1; p. 92
• Main Authors: Mouralova, Katerina, Hrabec, Pavel, Benes, Libor, Otoupalik, Jan, Bednar, Josef, Prokes, Tomas, Matousek, Radomil
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2020
• Subjects: Abrasion resistant steels; Controllers; creusabro; Cutting parameters; Cutting speed; design of experiment; Design of experiments; Electric discharges; Electric potential; electrical discharge machining; Energy consumption; fis; fuzzy inference system; Fuzzy logic; Fuzzy sets; Inference; Machine tools; Machining; Mechanical properties; Optimization; Quarries; Statistical methods; Steel; Taguchi methods; Voltage; wedm (wire electrical discharge machining); Wire
• ISSN: 2075-4701; 2075-4701
• DOI: 10.3390/met10010092

Wire electrical discharge machining is an unconventional machining method for the production of complex-shaped and very precise parts. Because of the high energy consumption of this machining process, it is necessary to maximize the cutting speed for its appropriate implementation. The abrasion-resistant steel Creusabro 4800 was chosen as the test material for this experiment, which is widely used especially for machines working in mines and quarries. In order to maximize the cutting speed, a fuzzy inference system (FIS) has been built, which uses 18 expert propositions to “model” the cutting speed based on five selected input parameters: gap voltage, pulse on time, pulse off time, discharge current, and wire feed. The obtained results were further verified by a design of experiment consisting of 33 tests for five selected input factors. Using the fuzzy inference system, the optimum machine parameters setup was found to maximize the cutting speed, in which the gap voltage = 60 V, pulse on time = 10 µs, pulse off time = 30 µs, wire feed = 10 m∙min−1 and discharge current = 35 A. The predicted value of the cutting speed using the fuzzy inference system is 6.471 mm∙min−1.