An interpretable predictive modelling framework for the turning process by the use of a compensated fuzzy logic system. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Alalawin, Abdallah; AlAlaween, Wafa' H.; Shbool, Mohammad A.; Abdallah, Omar; Al-Qatawneh, Lina
• Is Part Of: Production & manufacturing research. Volume 10:Issue 1(2022)
• Page Start: 89
• Page End: 107
• Publication Date: 2022-12-31
• Subjects: Compensated fuzzy logic system -- cutting force -- Gaussian mixture model -- interval type-2 fuzzy logic system -- surface quality -- turning process; Manufacturing processes -- Periodicals; Production engineering -- Research -- Periodicals; Manufacturing processes; Production engineering -- Research; Periodicals; 670
• DOI: 10.1080/21693277.2022.2064359
• Languages: English
• ISSNs: 2169-3277

Abstract

ABSTRACT: This research presents a compensated fuzzy logic system that integrates an interval type-2 fuzzy logic system (IT2FLS) with the Gaussian mixture model (GMM) to model the turning process. First, an IT2FLS is elicited to model the turning process by mapping its input variables to the cutting force and the surface quality. Second, the GMM is incorporated in the IT2FLS structure to compensate for the error residuals. The idea of such an incorporation stems from the fact that the majority of the models are constructed based on the normality assumption of the error. The GMM is developed in a way that refines the extracted rules and considers stochastic unmodelled behaviours. Validated on real experiments, it has been demonstrated that the compensated fuzzy logic system has the ability to accurately predict the cutting force and the surface quality; deal with uncertainties; and provide users with comprehensive understanding of the turning process.