Affiliations: [a] Electronics Engineering, Dr APJ Abdul Kalam Technical University, Lucknow | [b] Department of Electronics & Communication Engineering, Pranveer Singh Institute of Technology (PSIT), Kanpur, India
Correspondence:
[*]
Corresponding author. Arti Saxena, Department of Electronics & Communication Engineering, PSIT College of Engineering (PSITcoe), Kanpur, India. Tel.: +8953030612; E-mail: saxenaarti@yahoo.com.
Abstract: On the everlasting demand for better accuracy, high speed, and the inevitable approach for the high-quality surface finish as the basic requirements in the process industry, there felt the requirement to develop models which are reliable for predicting surface roughness (SR) as it is having a crucial role in the process industries. In this paper, SBCNC-60 of HMT make used to study the purpose of machining, while cutting speed (CS), feed rate (FR), and the depth of cut (DoC) were considered as parameters for machining of P8 material. Turning experiments data is studied by keeping two parameters constant at the mid-level out of three parameters. An artificial intelligence technique named fuzzy was engaged in working out for surface roughness and material removal rate (MRR) to design the models of reliable nature for the predictions. The accurate prediction performance of the fuzzy logic model was then better analyzed by calculating MAPE, RMSE, MAD, and correlation coefficient between experimental values and fuzzy logic predictions. MAPE, RMSE, MAD, and correlation coefficient calculated 2.66%, 8.20, 6.44, and 0.98 for MRR and 4.19%,1.16, 0.86 and 0.90 for SR, respectively. Hence, the proposed fuzzy logic rules efficiently predict the SR and MRR on P8 material with higher accuracy and computational cost.
Keywords: Correlation coefficient (R), Fuzzy
Logic (FL), Mean Absolute difference (MAD), Mean Absolute
Percentage Error (MAPE), MRR, Root mean square error
(RMSE), Surface roughness (SR)
