An Evaluation of Lean Six Sigma Readiness in IT Department of Higher Education Institution DOI
Devi Pratami, Nor Hasrul Akhmal Ngadiman, Syed Ahmad Helmi Syed Hassan

и другие.

Опубликована: Дек. 12, 2024

Язык: Английский

Intelligent Manufacturing in Aerospace: Integrating Industry 4.0 Technologies for Operational Excellence and Digital Transformation DOI
Vineet Bhatia, Sumati Sidharth, Sanjeev Kumar Khare

и другие.

Springer series in advanced manufacturing, Год журнала: 2024, Номер unknown, С. 389 - 434

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

2
Exploring the Challenges of Integrating Lean Green Practices in Industry 4.0 Manufacturing Frameworks: An Empirical Study DOI
Rajesh Kumar, Rajender Kumar,

Ashwini Kumar

и другие.

Springer series in advanced manufacturing, Год журнала: 2024, Номер unknown, С. 277 - 292

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

2
Industry 4.0 in Aircraft Manufacturing: Innovative Use Cases and Patent Landscape DOI
Vineet Bhatia, Ajay Kumar, Sumati Sidharth

и другие.

Springer series in advanced manufacturing, Год журнала: 2024, Номер unknown, С. 103 - 137

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

1
Comparative analysis of response surface methodology and adaptive neuro-fuzzy inference system for predictive fault detection and optimization in beverage industry DOI Creative Commons
Anthony O. Onokwai,

Olamide O. Olusanya,

Morakinyo K. Onifade

и другие.

Frontiers in Mechanical Engineering, Год журнала: 2024, Номер 10

Опубликована: Окт. 7, 2024

Maintenance is crucial for ensuring equipment reliability and minimizing downtime while managing associated costs. This study investigates a data-driven approach to predicting machine faults using Response Surface Methodology (RSM) Adaptive Neuro-Fuzzy Inference System (ANFIS). RSM was employed develop mathematical model analyze how operational parameters such as pressure, voltage, current, vibration, temperature affect fault occurrence. Data were collected at three levels each parameter central composite design. The identified that peaked pressure of 28.38 N/m 2 , an operating voltage 431.77 V, current consumption 12.54 A, vibration 47.17 Hz, 25°C, with maximum 25 observed. Conversely, the lowest detection occurred 29.42 441.04 12.04 49.46 46.5°C. A strong correlation found between these faults, achieving high accuracy (R = 98.22%) statistical significance ( p -value <0.05), demonstrating its in faults. also compared ANFIS process optimization beverage industry. While effectively optimized relationships, ANFIS, adaptive learning capabilities, provided superior prediction accuracy. comparative analysis highlighted strengths both methods suggested integrating them could enhance predictive maintenance strategies. findings offer valuable insights industry practitioners, recommending combined improve detection, optimize production processes, efficiency.

Язык: Английский

Процитировано

1
An Evaluation of Lean Six Sigma Readiness in IT Department of Higher Education Institution DOI
Devi Pratami, Nor Hasrul Akhmal Ngadiman, Syed Ahmad Helmi Syed Hassan

и другие.

Опубликована: Дек. 12, 2024

Язык: Английский

Процитировано

0