A Confidence Interval-Based Process Optimization Method Using Second-Order Polynomial Regression Analysis

Yu, Jungwon; Kim, Jin Hong; Lee, Young Jae; Lim, Kil-Taek; Kim, Seiki; Ryu, Sung‐Soo; Jeong, Hyeondeok

doi:10.3390/pr8101206

Cited by 10 publications

(4 citation statements)

References 33 publications

(43 reference statements)

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“…Here, it is assumed that as the length of PIs for a solution becomes shorter, its uncertainty is reduced from a statistical viewpoint. The proposed method is an advanced version of the method proposed in [50] so that it can be also applicable for MRO problems, and the significance of the different solutions is evaluated in terms of PIs instead of confidence intervals.…”

Section: Pi-based Methods For Optimizing Parameter Pointsmentioning

confidence: 99%

A machine learning approach for ball milling of alumina ceramics

Raju

Jin

et al. 2022

Int J Adv Manuf Technol

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In this work, machine learning (ML) approach based on polynomial regression was explored to analyze the optimal processing parameters and predict the target particle sizes for ball milling of alumina ceramics. Data points were experimentally obtained by measuring the particle sizes. Prediction interval (PI)-based optimization methods using polynomial regression analysis are proposed. As a first step, functional relations between the inputs and the responses are derived by applying the regression analysis. Later, based on these relations, objective functions to be maximized are defined by desirability approach. Finally, the proposed PI-based methods optimize both parameter points and intervals of the target mill for accomplishing userspecified target responses. The optimization results show that the PI-based point optimization methods can select and recommend statistically reliable optimized parameter points even though unique solutions for the objective functions do not exist. From the results of confirmation experiments, it is established that the optimized parameter points can produce desired particle sizes with quality responses quite similar to the target responses.

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Section: Pi-based Methods For Optimizing Parameter Pointsmentioning

confidence: 99%

A machine learning approach for ball milling of alumina ceramics

Raju

Jin

et al. 2022

Int J Adv Manuf Technol

Self Cite

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“…Regarding polynomials, varied types and degrees exist. The work in [31] focused on optimization when a confidence interval-based process optimization technique was offered. For this aim, second-order polynomial regression analysis was applied.…”

Section: Methodsmentioning

confidence: 99%

Mathematical Modeling of the Manufacturing Sector’s Dominant Part as a Base for Automation

Grešová

Svetlík

2021

Applied Sciences

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The current great expansion of automation and robotics affects a multiplicity of various fields. A prominent example is industry, where the different manufacturing processes and technologies embrace a certain level of automation and robotics. Thus, the use of robotics and automation implementation is part of a rapidly rising trend in industry. The presented paper deals with the manufacturing segment in the context of automation. The main subject is data analysis, with our own subsequent model building and final realization of the prediction corresponding to the machinery and electrical machinery sector as a highly relevant automation driver through the use of mathematical modeling. The design of the model is accompanied by optimization of the particular weights. Determination of the most suitable model is preceded by creating and testing a number of models to decide upon the final one. The construction of the mathematical model pursues the aim of making predictions relating to the machinery and electrical machinery sector for the specific national economy as the concluding investigation step. We apply a polynomial approximation as the research method. The software selected for our purposes is Matlab.

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“…[17]- [19], [27]. The effect of temperature on the frequency change of the tuning fork can be determined by looking at the value of the adjusted coefficient of determination (Adj.…”

Section: Derivation Of Thetuning Fork Frequency Equationmentioning

confidence: 99%

Experiment to Determine the Relationship Between Temperature and Tuning Fork Frequency

Hasan,

Natalisanto,

Zarkasi

2024

IPR

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A tuning fork is a special tool made of metal and shaped like the letter U with one handle. Tuning forks can produce certain frequencies; usually, the value is written on the handle. This study aims to investigate the relationship between temperature and changes in tuning fork frequency and model it using polynomial regression. This research uses laboratory experiments with tuning forks that have frequencies of 341.3 Hz, 426.5 Hz, and 512 Hz, then the temperature on the tuning fork is varied from 30C to 220C with a difference of 10C. The study's results adjusted R-Square values sequentially 0.94745, 0.99565, and 0.97721, which stated the relationship between temperature and frequency changes. The Adjusted R-Square value close to 1 means that changes in temperature on the tuning fork greatly influence changes in the frequency produced by the tuning fork. The polynomial regression model used is very suitable

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A Confidence Interval-Based Process Optimization Method Using Second-Order Polynomial Regression Analysis

Cited by 10 publications

References 33 publications

A machine learning approach for ball milling of alumina ceramics

A machine learning approach for ball milling of alumina ceramics

Mathematical Modeling of the Manufacturing Sector’s Dominant Part as a Base for Automation

Experiment to Determine the Relationship Between Temperature and Tuning Fork Frequency

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