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All Journal Jurnal Rekayasa Mesin Jurnal Polimesin
Ananda Nur Daffa Zain
Universitas Muhammadiyah Yogyakarta
Automotive Engineering Control & Systems Engineering Engineering Materials Science & Nanotechnology Mechanical Engineering

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Predictive modeling of surface roughness and resultant force in CNC turning of AISI H13 using optimized artificial neural networks Sunardi Sunardi; Ananda Nur Daffa Zain
Jurnal Polimesin Vol 24, No 1 (2026): February
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jpl.v24i1.7558

Abstract

Artificial Neural Networks (ANNs) have gained increasing attention as effective tools for modeling nonlinear and multivariate relationships in complex manufacturing processes, where conventional predictive approaches often exhibit limited accuracy. In this study, an ANN-based predictive framework was developed to estimate surface roughness (Ra) and resultant force (F) in CNC turning of hardened AISI H13 steel. The framework was constructed using an experimental dataset comprising 324 machining records, with cutting speed (vc), feed rate (f), and depth of cut (ap) as input parameters, all normalized using the Min-Max scaling method to ensure stable and efficient model training. To identify the optimal training configuration, eight optimization algorithms: Adam, RMSprop, Nadam, Adagrad, Adadelta, Adamax, FTRL, and Stochastic Gradient Descent (SGD) are systematically evaluated, and Nadam was selected as the most effective optimizer with a learning rate of 0.0001 and a batch size of 16. Two dedicated feed forward ANN models are designed separately for Ra and F prediction and validated using the Leave-One-Out Cross-Validation (LOOCV) technique to enhance generalization and minimize overfitting. The resulting models achieved excellent predictive accuracy for resultant force (R² = 0.9939, MAE = 4.3313 N, RMSE = 7.5955 N) and moderate accuracy for surface roughness (R² = 0.6454, MAE = 0.1440 µm, RMSE = 0.1960 µm). These results demonstrate that the proposed ANN-based framework provides a reliable decision-support tool for process optimization, monitoring, and surface quality control in high-performance machining environments.
Clustering-Based Analysis of Fuel Efficiency and Emissions in Automotive Data Using PCA and K-Means Sunardi; Ananda Nur Daffa Zain
Jurnal Rekayasa Mesin Vol. 20 No. 2 (2025): Volume 20, Nomor 2, August 2025
Publisher : Mechanical Engineering Department - Semarang State Polytechnic

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32497/jrm.v20i2.6709

Abstract

Growing concerns regarding greenhouse gas emissions and fuel consumption have placed considerable demands on the automotive sector. To address these issues, this research applies unsupervised learning approaches namely Principal Component Analysis (PCA) and K-Means Clustering to categorize vehicles based on attributes associated with energy efficiency and environmental impact. Using a publicly available vehicle dataset, PCA was used to simplify the data by reducing dimensionality while preserving significant patterns. Subsequently, K-Means was employed to segment the data into three distinct clusters according to shared features like engine size, fuel usage, and CO₂ output. The resulting groupings effectively identified categories such as fuel-efficient, moderately consuming, and high-consumption vehicles. Visual representation in two-dimensional space further confirmed meaningful distinctions among the clusters, offering practical insights for both manufacturers and consumers.
Co-Authors Sunardi Sunardi, Sunardi