Enhancing product predictive quality control using Machine Learning and Explainable AI DOI

Ahmed En-nhaili,

Adil Hachmoud, Anwar Meddaoui

et al.

Data & Metadata, Journal Year: 2024, Volume and Issue: 4, P. 500 - 500

Published: Nov. 26, 2024

The integration of predictive quality and eXplainable Artificial Intelligence (XAI) in product classification marks a significant advancement control processes. This study examines the application Machine Learning (ML) models XAI techniques managing quality, using case agri-food industry as an example. Predictive leverage historical real-time data to anticipate potential issues, thereby improving detection accuracy efficiency. ensures transparency interpretability, facilitating trust model’s decisions. combination enhances management, supports informed decision-making, regulatory compliance. demonstrates how ML models, particularly Neural Network (ANN), can accurately predict with providing clarity on reasoning behind these predictions. suggests future research directions, such expanding datasets, exploring advanced techniques, implementing monitoring, integrating sensory analysis, further improve various industries.

Language: Английский

Sliding mode control based Dynamic Voltage Restorer for Voltage Sag Compensation DOI Creative Commons

Ashraf K. Abdelaal,

Abdullah M. Shaheen, Attia A. El‐Fergany

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 102936 - 102936

Published: Sept. 1, 2024

Language: Английский

Citations

9
Health Index Degradation Prediction of Induction Motor Using Deep Neural Network Algorithm DOI Creative Commons
Arslan Ahmed Amin, Turki Alsuwian,

Abdulla Shahid

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104357 - 104357

Published: Feb. 1, 2025

Language: Английский

Citations

0
Design of the Low-Voltage High-Current BLDC Control Circuit Used in Aviation Starting System DOI

Zhangjun Sun,

Wu Ren,

Yongqin Hao

et al.

Lecture notes in electrical engineering, Journal Year: 2025, Volume and Issue: unknown, P. 271 - 278

Published: Jan. 1, 2025

Language: Английский

Citations

0
Optimized Design of a Permanent Magnet Brushless DC Motor for Solar Water-Pumping Applications DOI Creative Commons
Aryadip Sen, Bhim Singh, Kumar Mahtani

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104633 - 104633

Published: March 1, 2025

Language: Английский

Citations

0
Performance Optimization of Symmetrical Multi-Level Boost Converter Using Hybrid MPPT-ANN for Solar Energy Applications DOI Creative Commons

Ikram El Haji,

Meriem Megrini,

Mustapha Kchikach

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104729 - 104729

Published: March 1, 2025

Language: Английский

Citations

0
Embedded Processor-in-the-Loop Implementation of ANFIS-Based Nonlinear MPPT Strategies for Photovoltaic Systems DOI Creative Commons
Khalil Chnini, Mahamadou Abdou Tankari,

Houda Jouini

et al.

Energies, Journal Year: 2025, Volume and Issue: 18(10), P. 2470 - 2470

Published: May 12, 2025

The integration of photovoltaic (PV) systems into global energy production is rapidly expanding. However, achieving maximum power extraction remains a significant challenge due to the nonlinear electrical characteristics PV modules, which are highly sensitive environmental variations such as temperature fluctuations and irradiance changes. This study presents structured design, testing, quasi-experimental validation methodology for robust Maximum Power Point Tracking (MPPT) control in systems. We propose two advanced AI-based strategies: an Adaptive Neuro-Fuzzy Inference System combined with Fast Terminal Synergetic Control (ANFIS-FTSC) boost converter ANFIS Backstepping (ANFIS-BS) Single-Ended Primary Inductor Converter (SEPIC), both have demonstrated tracking efficiencies exceeding 99.6%. To evaluate real-time performance, Processor-in-the-Loop (PIL) conducted using ARM-based STM32F407VG microcontroller. adheres Model-Based Design (MBD) framework, ensuring systematic development, implementation, verification MPPT algorithms embedded environment. Experimental results demonstrate that proposed controllers achieve high efficiency, rapid convergence, point under varying operating conditions. successful PIL-based confirms feasibility these intelligent techniques real-world deployment systems, paving way more efficient adaptive renewable solutions.

Language: Английский

Citations

0
Design and PIL test of extended Kalman filter for PMSM field oriented control DOI Creative Commons
Meriem Megrini, Ahmed Gaga,

Youness Mehdaoui

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: 24, P. 102843 - 102843

Published: Sept. 6, 2024

Language: Английский

Citations

2
Heat Transfer Investigations on a Thermally Superior Alternative for the Flux Switching Permanent Magnet Electric Motor DOI Creative Commons
Tohid Sharifi, Alireza Eikani

Results in Engineering, Journal Year: 2024, Volume and Issue: 24, P. 103415 - 103415

Published: Nov. 17, 2024

Language: Английский

Citations

2
Enhancing product predictive quality control using Machine Learning and Explainable AI DOI

Ahmed En-nhaili,

Adil Hachmoud, Anwar Meddaoui

et al.

Data & Metadata, Journal Year: 2024, Volume and Issue: 4, P. 500 - 500

Published: Nov. 26, 2024

The integration of predictive quality and eXplainable Artificial Intelligence (XAI) in product classification marks a significant advancement control processes. This study examines the application Machine Learning (ML) models XAI techniques managing quality, using case agri-food industry as an example. Predictive leverage historical real-time data to anticipate potential issues, thereby improving detection accuracy efficiency. ensures transparency interpretability, facilitating trust model’s decisions. combination enhances management, supports informed decision-making, regulatory compliance. demonstrates how ML models, particularly Neural Network (ANN), can accurately predict with providing clarity on reasoning behind these predictions. suggests future research directions, such expanding datasets, exploring advanced techniques, implementing monitoring, integrating sensory analysis, further improve various industries.

Language: Английский

Citations

0