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Menampilkan 1–4 dari 4 artikel
Multi Objective Evolutionary Optimization of Additive Manufacturing Process Parameters for Enhanced Mechanical Performance and Surface Integrity
Yulaikha Maratullatifah
; Dwi Utari Iswavigra
; Very Dwi Setiawan
; Mursalim Mursalim
; Budi Wibowo
International Journal of Mechanical, Industrial and Control Systems Engineering
Vol 1
, No 1
(2026)
Introduction: Additive Manufacturing (AM) has revolutionized the production of complex geometries, offering flexibility, customization, and precision across various industries. However, optimizing multiple process parameters simultaneously to enhance AM performance remains a significant challenge. This study focuses on improving both mechanical properties and surface quality by utilizing multi-objective optimization techniques. Literature Review: The research reviews existing approaches in AM op...
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Experimental Investigation of Green Hydrogen Integration into Industrial Thermal Systems for Sustainable and Low Carbon Manufacturing Applications
Dwi Feriyanto
; Agus Wantoro
; Deny Prasetyo
; Very Dwi Setiawan
; Faizal Riza
International Journal of Industrial Innovation and Mechanical Engineering
Vol 1
, No 2
(2025)
Background: The global energy transition requires low-carbon solutions that can be integrated into existing thermal systems without drastic infrastructure changes. Hydrogen blending in conventional combustion systems has emerged as a promising pathway to reduce carbon emissions while maintaining operational flexibility. Objective: This study aims to experimentally evaluate the effect of hydrogen blending ratios (0–100% by volume) on thermal efficiency, CO₂ emissions, and NOx emissions, and to de...
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Carbon Neutral Industrial Process Optimization through Hybrid Machine Learning and Real Time Energy Efficiency Monitoring Framework
Suyahman Suyahman
; Ardy Wicaksono
; Dwi Utari Iswavigra
; Yogiek Indra Kurniawan
; Very Dwi Setiawan
; Dedi Setiadi
International Journal of Engineering and Applied Science
Vol 2
, No 2
(2025)
Introduction: Achieving carbon neutrality in industrial systems is essential for mitigating climate change and promoting sustainability. The increasing demand for energy optimization and carbon emission reduction has driven the development of advanced technologies, particularly hybrid machine learning (ML) models. These models, combining ensemble learning and reinforcement learning (RL), offer significant promise in optimizing industrial processes, reducing energy consumption, and improving envi...
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Hybrid Reinforcement Learning and Robust Adaptive Control Strategy for Autonomous Manufacturing Systems under Uncertain and Dynamic Production Environments
Irlon Irlon
; Teguh Muryanto
; Sayyid Jamal Al Din
; Dwi Utari Iswavigra
; Yulaikha Maratullatifah
; Very Dwi Setiawan
International Journal of Mechanical, Industrial and Control Systems Engineering
Vol 1
, No 1
(2024)
This study explores the integration of hybrid AI control models, combining reinforcement learning (RL) and robust adaptive control, to improve the adaptability, performance, and stability of autonomous manufacturing systems. Traditional control systems, while effective under stable conditions, often struggle to cope with disturbances and varying production demands. Hybrid AI models, which integrate classical control methods such as Proportional Integral Derivative (PID) with machine learning tec...
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