Six Sigma Quality Approach to Robust Optimization

Xiao, Song; Li, Yinjiang; Rotaru, Mihai; Sykulski, J.K.

doi:10.1109/tmag.2014.2360435

Cited by 20 publications

(3 citation statements)

References 16 publications

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“…The digital networking of workplaces and automated data acquisition and evaluation also create new opportunities for optimization projects [64]. A proven optimization approach is Six Sigma, which consists of a total of five phases and is used as a globally recognized method for process improvement and error reduction [65]. From the implementation of five-phase digitization, Six Sigma can improve the process and reduce errors to the maximum.…”

Section: Current State-of-the-art Of Six Sigma Research On the Automo...mentioning

confidence: 99%

A Systematic Literature Review of Six Sigma Implementation on the Automotive Component Industry

Parid,

Hardi Purba,

Santoso

2023

j.sist.teknik.industri.

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The main purpose of this white paper is to analyze current research on Six Sigma in the auto parts industry and identify opportunities for further research. This study used a systematic literature search of the most recent scientific articles (2013-2022) searched using specific inclusion and exclusion criteria. His two main categories of research goals emerged. The development of Six Sigma and the impact of Six Sigma. In recent years, research on the impact of Six Sigma is the most popular and has always been there from year to year, while research on the development of Six Sigma is the least popular. The study can provide insights to researchers, consultants and quality managers in developing and implementing Six Sigma in the automotive components industry sector. This research found that there is a lot of research on the complete Six Sigma methodology in the automotive component industry, especially research on the impact of implementing Six Sigma. From these findings, the main avenue for future research can be identified, namely Six Sigma Digitalization.

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Section: Current State-of-the-art Of Six Sigma Research On the Automo...mentioning

confidence: 99%

A Systematic Literature Review of Six Sigma Implementation on the Automotive Component Industry

Parid,

Hardi Purba,

Santoso

2023

j.sist.teknik.industri.

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“…The Six Sigma model is a method for working more accurately and quickly. Six Sigma applies to various sectors such as a quality approach to robust optimization [4], for HPLC-UV method optimization [5], Performance optimization of the retail Industry [6], for production process optimization in a paper production company [7], Improving online quality control [8], for robust design optimization [9], Multi-Point Dieless Forming Process [10], Sorbitol production optimization in B2B industry [11], in the mould industry [12], for Inherent Issues in Production Shop Floor Management [13], and others. Therefore, by removing and locating the root causes of faults and reducing manufacturing variance, Six Sigma procedures aim to increase manufacturing quality.…”

Section: Introductionmentioning

confidence: 99%

Performance optimization of radiator engine parameters during hard conditions by control charts monitoring and evaluating

Abduljabbar,

Oleiwi,

Sabry

2023

EEJET

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Recently, engine design and control systems have been developed using data-driven modeling techniques to specify the in-cylinder complicated combustion process. The cooling fan performance is highly influenced by several factors that are determined based on what is called (DOE) «design of experiments». These factors include blade tip clearance, pitch angle, distance from radiator. This work presents a method to improve a cooling fan performance of an engine by designing a Six Sigma technique using Control, Improve, Analyze, Measure, and Define (CIAMD). First, let’s assess the existing cooling fan performance and define its problem. Then, let’s specify the parameters that affect on fan performance to be optimized. Next, let’s conduct sensitivity analysis and evaluate manufacturing control of the developed cool Fan. The primary fan does not distribute air enough by the radiator to maintain the machine cool throughout hard circumstances. First, the work demonstrates how to develop an experiment to examine the influence of three performance elements: blade pitch angle, blade-tip clearance, and fan distance from the radiator. In order to improve the performance of the cooling fan, Box-Behnken design is adopted for testing quadratic (nonlinear) effects. It then indicates how to predict optimal quantities for every element, to produce a technique that makes airflows above the objective of 1486.6 m3/h when utilizing experimental measurements. Finally, it reveals how to operate simulations to confirm that this method creates airflow based on the specifications with more additional fans manufactured performance of 99.999 %. The results of S and X-bar control charts indicate that the manufacturing process is statistically under control

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“…From the perspective of industrial production, the performance of a good design of an electromagnetic device, like a transformer, should not be sensitive to those uncertainties. To achieve this goal, reliabilitybased and robust optimizations have attracted significant research attention recently, especially when the industrial big data about the material and manufacturing process are considered [6,[10][11][12][13][14][15]. These topics are of ever-growing significance for smart manufacturing in the context of industry 4.0.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions

Lei

Bramerdorfer

et al. 2021

Applied Sciences

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This paper reviews the recent developments of design optimization methods for electromagnetic devices, with a focus on machine learning methods. First, the recent advances in multi-objective, multidisciplinary, multilevel, topology, fuzzy, and robust design optimization of electromagnetic devices are overviewed. Second, a review is presented to the performance prediction and design optimization of electromagnetic devices based on the machine learning algorithms, including artificial neural network, support vector machine, extreme learning machine, random forest, and deep learning. Last, to meet modern requirements of high manufacturing/production quality and lifetime reliability, several promising topics, including the application of cloud services and digital twin, are discussed as future directions for design optimization of electromagnetic devices.

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Six Sigma Quality Approach to Robust Optimization

Cited by 20 publications

References 16 publications

A Systematic Literature Review of Six Sigma Implementation on the Automotive Component Industry

A Systematic Literature Review of Six Sigma Implementation on the Automotive Component Industry

Performance optimization of radiator engine parameters during hard conditions by control charts monitoring and evaluating

Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions

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