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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika)
Rosyadi, Niam
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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MULTI-RESPONSE OPTIMIZATION OF DIELECTRIC FLUID MIXTURE IN EDM USING GREY RELATIONAL ANALYSIS (GRA) IN TAGUCHI METHOD Forestryani, Veniola; Rosyadi, Niam; Ahsan, Muhammad
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 3 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (681.708 KB) | DOI: 10.30598/barekengvol16iss3pp949-960

Abstract

In the current study, combining the powder with dielectric fluid in electrical discharge machining (PMEDM) is a very fascinating technological approach. This approach is the most effective at increasing both productivity and the quality of a machined surface at the same time. The Taguchi–GRA approach was used to optimize the surface roughness (SR), material removal rate (MRR), and micro-hardness of a machined surface (HV) in electrical discharge machining of die steels in dielectric fluid with mixed powder. Workpiece materials (with 3 levels such as SKD61, SKD11, and SKT4), electrode materials (with 2 levels such as copper, and graphite), pulse-on time, electrode polarity, current, pulse-off time, and titanium powder concentration were all used in the study. The effect on the ideal results was also evaluated using some interaction pairings among the process parameters. Powder concentration, electrode material, electrode polarity, current, pulse-on time, pulse-off time, and Interaction between workpiece material and powder concentration were obtained to be significant in the ideal condition, where larger MRR and HV are wanted (as per the HB criterion), but lower values are desired for the remaining responses, such as surface roughness (SR). Powder concentration was also discovered to be a major component, however, it only accounts for 8.35 percent of the ideal condition. MRR = 54.36 mm3/min, SR = 5.65 m, and HV =832.66 HV were the best quality attributes based on the grey grade.
Monitoring PH of Shrimp Water using Progressive Max Chart Rosyadi, Niam; Syahzaqi, Idrus; Ibrahim, Auron Saka; Sihotang, Raja Van Den Bosch; Ahsan, Muhammad; Mashuri, Muhammad
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 4 (2025): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i4.30255

Abstract

Control charts aim to reduce variability in the process and monitor for out-of- control processes. So far, the process of monitoring quality is usually carried out partially, namely monitoring the mean process and process variability. This approach is less effective and time-consuming because two separate charts must be created simultaneously. One alternative is to analyze both parameters simultaneously, such as through the Progressive Max Chart method (Mixed-Methods Research: Quantitative and Applied). The Progressive Max Chart is a control chart designed for monitoring both the mean and variability by considering the case of subgroup observations. This study uses a quantitative approach, combining primary data collection and simulations to generate findings through statistical analysis and quantifiable measurements. The purpose of this research is to compare methods such as the Progressive Max Chart, EWMA-Max, and Max Chart. The analysis results show that the Progressive Max Chart method performs better than the Max Chart and EWMA- Max Chart, both in terms of mean, variance, and mean-variance detection, for small shifts and large shifts. The control chart performance results provide optimal outcomes for monitoring out-of-control signals at subgroup sizes of n = 2, 3, 5. This is characterized by ARL₁ values that approach 1 more quickly. This method is applied to pH data from vannamei shrimp pond water located in Madura. The Progressive Max Chart method provides optimal results by maximizing the detection of in-control signals. Additionally, it is tested on synthesized data and demonstrates optimal performance in detecting both small and large shifts in mean, variance, and mean-variance.
Co-Authors Ahsan, Muhammad Forestryani, Veniola Ibrahim, Auron Saka Idrus Syahzaqi muhammad mashuri Sihotang, Raja Van Den Bosch