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Faculty of Engineering and Built Environment

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Author: search.filters.author.Dewa, MendonSubject: search.filters.subject.Fuzzy logic

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    An adaptive neuro-fuzzy inference scheme for defect detection and classification of solar Pv cells
    (Academy Publishing Center, 2024-09-12) Moyo, Ranganai Tawanda; Dewa, Mendon; Romero, Héctor Felipe Mateo; Gómez, Victor Alonso; Aragonés, Jose Ignacio Morales; Hernández-Callejo, Luis
    This research paper presents an innovative approach for defect detection and classification of solar photovoltaic (PV) cells using the adaptive neuro-fuzzy inference system (ANFIS) technique. As solar energy continues to be a vital component of the global renewable energy mix, ensuring the reliability and efficiency of PV systems is paramount. Detecting and classifying defects in PV cells are crucial steps toward ensuring optimal performance and longevity of solar panels. Traditional defect detection and classification methods often face challenges in providing precise and adaptable solutions to this complex problem. In this study the researchers pose an ANFIS-based scheme that combines the strengths of neural networks and fuzzy logic to accurately identify and classify various types of defects in solar PV cells. The adaptive learning mechanism of ANFIS enables the model to continuously adapt to changes in operating conditions ensuring robust and reliable defect detection capabilities. The ANFIS model was developed and implemented using MATLAB and a high predicting accuracy was achieved.
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    Implementation of fuzzy logic in scheduling a flexible manufacturing system
    (Southern African Institute for Industrial Engineering, 2023-10-31) Dewa, Mendon
    A flexible manufacturing system (FMS) has the advantage that it can easily adapt to equipment malfunctions and changes in the quantity or type of the product being processed. Proper scheduling is critical for production planning in order to meet customer demand. The case study organisation has been facing challenges in meeting due dates while concurrently minimising lead time and maximising machine utilisation. This paper presents the deployment of fuzzy logic in scheduling a flexible manufacturing system. The proposed approach identifies scheduling parameters for routing parts through system and membership functions and fuzzy rules are constructed to develop the best schedule using a toolbox on a MATLAB fuzzy logic platform. After comparison of several priority rules for job sequencing, the results demonstrated the applicability of fuzzy logic as a decision tool in the scheduling of flexible manufacturing systems.