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

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## Abstract

**:**

^{−1}and discharge current = 35 A. The predicted value of the cutting speed using the fuzzy inference system is 6.471 mm∙min

^{−1}.

## 1. Introduction

_{18}mixed-orthogonal array table was selected as a work material for the experiments. The WEDM process parameters like open voltage, arc on time, off time, and servo voltage were considered in the experiments. According to the obtained results, the machining performance characteristics of the WEDM process can be efficiently improved using the combination of Taguchi method and fuzzy logic. Soepangkat et al. [17] investigated the optimization of the thickness of recast layer and surface roughness of the WEDM process using AISI D2 steel as an experimental material. They employed Taguchi method, fuzzy logic, and grey relational analysis applying different flushing pressure, open voltage, on time, off time, and servo voltage to study the multiple performance characteristics. The combination of the methods proved to be very efficient for the WEDM process of AISI D2 steel as was shown by the results of the experiments.

## 2. Experimental Setup and Material

#### 2.1. Experimental Material

#### 2.2. WEDM Machine Setup

_{on}), pulse off time (T

_{off}), discharge current (I), wire feed (v), and their limiting values, which are given in Table 1. The limiting values of individual parameter setup were determined on the basis of very extensive previous tests [24]. The actual cutting speed on the WEDM machine was read during the machining process.

#### 2.3. Mamdani Fuzzy Inference System

_{c}was divided into five levels (very low, low, mid, high, very high) and fuzzified in a similar way as shown in Figure 3.

## 3. Results and Discussion

^{−1}, and discharge current = 35 A, with the predicted cutting speed along with this machine parameters setup is 6.471 mm∙min

^{−1}. This result is illustrated by the FIS response surface shown in Figure 4. Because of the number of parameters used, it was necessary to fix the three input variables, i.e., pulse on time to 10 µs, discharge current to 35 A, and wire feed to 10 m·min

^{−1}. This figure also shows the optimum cutting speed in the selected parameter area.

^{−1}and the optimum position (except for the insignificant wire feed) is consistent with the result obtained from the fuzzy inference system. The results of the regression analysis “confirm the correctness” of the selected explanatory variables for cutting speed and were the same as in the study by Singh [27], which dealt with aluminum alloy machining. In this study the authors did not perform a similar statistical analysis and the effect of these variables on the response was taken as given.

## 4. Conclusions

^{−1}, and discharge current = 35 A for a maximum cutting speed of 6.471 mm∙min

^{−1}.

^{−1}for Sample 13.

^{−1}.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**(

**a**) Scheme of WEDM (wire electrical discharge machining); (

**b**) produced samples-one sample labeled green; (

**c**) microstructure of Creusabro 4800.

**Figure 4.**The prediction of cutting speed by FIS depending on the gap voltage and pulse off time with the defined optimum control surface.

**Figure 5.**The response area of the cutting speed depending on the gap voltage and pulse off time with the defined optimum.

Parameter | Gap Voltage | Pulse on Time | Pulse off Time | Wire Feed | Discharge Current |
---|---|---|---|---|---|

(V) | (µs) | (µs) | (m·min^{−1}) | (A) | |

Minimum | 50 | 6 | 30 | 10 | 25 |

Maximum | 70 | 10 | 50 | 14 | 35 |

Number of Sample | Gap Voltage (V) | Pulse on Time (µs) | Pulse off Time (µs) | Wire Feed (m·min^{−1}) | Discharge Current (A) | Cutting Speed (mm∙min^{−1}) | Number of Sample | Gap Voltage (V) | Pulse on Time (µs) | Pulse off Time (µs) | Wire Feed (m·min^{−1}) | Discharge Current (A) | Cutting Speed (mm∙min^{−1}) |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

1 | 70 | 8 | 40 | 12 | 30 | 5.4 | 18 | 60 | 8 | 40 | 12 | 30 | 5.4 |

2 | 60 | 8 | 30 | 12 | 30 | 5.6 | 19 | 60 | 8 | 40 | 12 | 30 | 5.4 |

3 | 60 | 8 | 40 | 12 | 25 | 4.9 | 20 | 70 | 6 | 50 | 14 | 25 | 3.9 |

4 | 60 | 10 | 40 | 12 | 30 | 6.1 | 21 | 50 | 6 | 30 | 14 | 25 | 4.9 |

5 | 50 | 8 | 40 | 12 | 30 | 5.3 | 22 | 60 | 8 | 40 | 12 | 30 | 5.4 |

6 | 60 | 8 | 50 | 12 | 30 | 5.1 | 23 | 70 | 10 | 30 | 14 | 25 | 4.9 |

7 | 60 | 6 | 40 | 12 | 30 | 4.8 | 24 | 50 | 6 | 50 | 10 | 25 | 3.9 |

8 | 60 | 8 | 40 | 12 | 35 | 5.8 | 25 | 60 | 8 | 40 | 12 | 30 | 5.3 |

9 | 60 | 8 | 40 | 10 | 30 | 5.4 | 26 | 50 | 10 | 50 | 14 | 25 | 4.7 |

10 | 60 | 8 | 40 | 14 | 30 | 5.4 | 27 | 50 | 10 | 30 | 10 | 25 | 5.5 |

11 | 60 | 8 | 40 | 12 | 30 | 5.4 | 28 | 50 | 6 | 50 | 14 | 35 | 4.8 |

12 | 50 | 6 | 30 | 10 | 35 | 5.3 | 29 | 50 | 10 | 50 | 10 | 35 | 5.8 |

13 | 70 | 10 | 50 | 10 | 25 | 4.8 | 30 | 70 | 6 | 30 | 14 | 35 | 4.9 |

14 | 70 | 10 | 30 | 10 | 35 | 6.2 | 31 | 50 | 10 | 30 | 14 | 35 | 5.9 |

15 | 60 | 8 | 40 | 12 | 30 | 5.4 | 32 | 60 | 8 | 40 | 12 | 30 | 5.4 |

16 | 70 | 6 | 50 | 10 | 35 | 4.9 | 33 | 70 | 6 | 30 | 10 | 25 | 4.6 |

17 | 70 | 10 | 50 | 14 | 35 | 5.9 | - | - | - | - | - | - | - |

Parameter | P-Value |
---|---|

Linear | |

pulse on time | 0.000 |

pulse off time | 0.000 |

discharge current | 0.000 |

2–Way Interaction | - |

pulse off time and discharge current | 0.023 |

Quadratic | - |

gap voltage | 0.000 |

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**MDPI and ACS Style**

Mouralova, K.; Hrabec, P.; Benes, L.; Otoupalik, J.; Bednar, J.; Prokes, T.; Matousek, R. Verification of Fuzzy Inference System for Cutting Speed while WEDM for the Abrasion-Resistant Steel Creusabro by Conventional Statistical Methods. *Metals* **2020**, *10*, 92.
https://doi.org/10.3390/met10010092

**AMA Style**

Mouralova K, Hrabec P, Benes L, Otoupalik J, Bednar J, Prokes T, Matousek R. Verification of Fuzzy Inference System for Cutting Speed while WEDM for the Abrasion-Resistant Steel Creusabro by Conventional Statistical Methods. *Metals*. 2020; 10(1):92.
https://doi.org/10.3390/met10010092

**Chicago/Turabian Style**

Mouralova, Katerina, Pavel Hrabec, Libor Benes, Jan Otoupalik, Josef Bednar, Tomas Prokes, and Radomil Matousek. 2020. "Verification of Fuzzy Inference System for Cutting Speed while WEDM for the Abrasion-Resistant Steel Creusabro by Conventional Statistical Methods" *Metals* 10, no. 1: 92.
https://doi.org/10.3390/met10010092