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Crankshaft’s machining process accuracy improvement by integrating DMAIC framework, Shainin Red X, and Taguchi Method Agus Setyo Anto Wibowo; Yunita Umniyati; Aditya Tirta Pratama
JTTM : Jurnal Terapan Teknik Mesin Vol 7 No 1 (2026): JTTM: Jurnal Terapan Teknik Mesin
Publisher : Teknik Mesin - Universitas Muhammadiyah Cileungsi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37373/jttm.v7i1.2103

Abstract

This study investigates the problem of oversized hole diameter defects in crankshaft machining. The problem occurred in fine boring process which accounted for 42.3% of total rejections in the machining process. To systematically overcome this issue, a hybrid methodology was adopted by integrating 3 methods: Six Sigma’s DMAIC framework, Shainin Red X root cause analysis, and Taguchi Design of Experiments (DOE). By using multi-vari analysis and Red X techniques, the dominant cause of variation was identified as inconsistent tool offset input by operators, with secondary factors including clamper piston wear and part positioning misalignment. Taguchi DOE confirmed that depth of cut significantly influenced hole diameter accuracy, while spindle speed had minimal effect. The optimized machining parameters improved process capability, reducing rejection rates and cutting failure costs. The results show the synergy between diagnostic and optimization methods, providing a structured, data-driven framework for sustainable machining improvements. The novelty of this study lies in the integrated application of DMAIC, Shainin Red X, and Taguchi DOE to focus on oversized hole defects in crankshaft fine boring, an approach that has rarely been applied to turning-based off-center boring processes, with implications for both academic research and industrial precision machining practice.
Optimized PID Tuning in Pyrolysis Temperature Control Using Genetic Algorithm and Particle Swarm Optimization Hartono; Yunita Umniyati; Eka Budiarto; Henry Nasution; Mulyadi
Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Vol 13 No 1 (2026): Jurnal Ecotipe, April 2026
Publisher : Jurusan Teknik Elektro, Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33019/k64tmh54

Abstract

Temperature control is crucial for maintaining stable and effective thermal treatment in pyrolysis system. For this application, Proportional-Integral-Derivative (PID) controller is frequently utilized due to its ease of use and efficiency. This study aims to evaluate and compare the performance of classical and metaheuristic tuning methods for PID controllers in pyrolysis temperature control. This work compares conventional Ziegler-Nichols (ZN) and Cohen-Coon (CC) methods with metaheuristic optimization techniques, specifically Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), for tuning PID controller parameters. The main contribution of this research is the demonstration of improved control performance and computational efficiency using PSO-based PID tuning for pyrolysis applications. Simulation results show that PID controllers that the parameters tuned by GA and PSO achieve faster and smoother responses, with small overshoot, compared to classical methods. From both methods, PSO provides balanced performance with the shortest rise time (30.66 s), fastest settling time (50.80 s), and lowest overshoot (1.15%). Although both GA and PSO can maintain the set point of 500 °C with satisfactory transient response, PSO also shows better convergence efficiency, with smaller iteration numbers and lower computational effort. The results indicate that PSO-tuned PID is suitable for pyrolysis temperature control applications.