IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v7y2015i11p15464-15486d59178.html
   My bibliography  Save this article

Analysis on Accuracy of Bias, Linearity and Stability of Measurement System in Ball screw Processes by Simulation

Author

Listed:
  • Fan-Yun Pai

    () (Department of Business Administration, National Changhua University of Education, No. 2, Shi-Da Road, Changhua 500, Taiwan
    Department of Industrial Engineering and Management, Da-Yeh University, No. 168, University Rd., Dacun, Changhua 51591, Taiwan)

  • Tsu-Ming Yeh

    () (Department of Industrial Engineering and Management, Da-Yeh University, No. 168, University Rd., Dacun, Changhua 51591, Taiwan)

  • Yung-Hsien Hung

    () (Department of Industrial Engineering and Management, Da-Yeh University, No. 168, University Rd., Dacun, Changhua 51591, Taiwan)

Abstract

To consistently produce high quality products, a quality management system, such as the ISO9001, 2000 or TS 16949 must be practically implemented. One core instrument of the TS16949 MSA (Measurement System Analysis) is to rank the capability of a measurement system and ensure the quality characteristics of the product would likely be transformed through the whole manufacturing process. It is important to reduce the risk of Type I errors (acceptable goods are misjudged as defective parts) and Type II errors (defective parts are misjudged as good parts). An ideal measuring system would have the statistical characteristic of zero error, but such a system could hardly exist. Hence, to maintain better control of the variance that might occur in the manufacturing process, MSA is necessary for better quality control. Ball screws, which are a key component in precision machines, have significant attributes with respect to positioning and transmitting. Failures of lead accuracy and axial-gap of a ball screw can cause negative and expensive effects in machine positioning accuracy. Consequently, a functional measurement system can incur great savings by detecting Type I and Type II errors. If the measurement system fails with respect to specification of the product, it will likely misjudge Type I and Type II errors. Inspectors normally follow the MSA regulations for accuracy measurement, but the choice of measuring system does not merely depend on some simple indices. In this paper, we examine the stability of a measuring system by using a Monte Carlo simulation to establish bias, linearity variance of the normal distribution, and the probability density function. Further, we forecast the possible area distribution in the real case. After the simulation, the measurement capability will be improved, which helps the user classify the measurement system and establish measurement regulations for better performance and monitoring of the precision of the ball screw.

Suggested Citation

  • Fan-Yun Pai & Tsu-Ming Yeh & Yung-Hsien Hung, 2015. "Analysis on Accuracy of Bias, Linearity and Stability of Measurement System in Ball screw Processes by Simulation," Sustainability, MDPI, Open Access Journal, vol. 7(11), pages 1-23, November.
  • Handle: RePEc:gam:jsusta:v:7:y:2015:i:11:p:15464-15486:d:59178
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/7/11/15464/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/7/11/15464/
    Download Restriction: no

    References listed on IDEAS

    as
    1. repec:eee:reensy:v:144:y:2015:i:c:p:296-310 is not listed on IDEAS
    2. Amigun, Bamikole & Petrie, Daniel & Görgens, Johann, 2011. "Economic risk assessment of advanced process technologies for bioethanol production in South Africa: Monte Carlo analysis," Renewable Energy, Elsevier, vol. 36(11), pages 3178-3186.
    3. Arnold, Uwe & Yildiz, Özgür, 2015. "Economic risk analysis of decentralized renewable energy infrastructures – A Monte Carlo Simulation approach," Renewable Energy, Elsevier, vol. 77(C), pages 227-239.
    4. Gurgur, Cigdem Z. & Jones, Michael, 2010. "Capacity factor prediction and planning in the wind power generation industry," Renewable Energy, Elsevier, vol. 35(12), pages 2761-2766.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    measurement system analysis; Monte Carlo simulation; bias; linearity; stability;

    JEL classification:

    • Q - Agricultural and Natural Resource Economics; Environmental and Ecological Economics
    • Q0 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General
    • Q2 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation
    • Q3 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation
    • Q5 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics
    • Q56 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:7:y:2015:i:11:p:15464-15486:d:59178. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (XML Conversion Team). General contact details of provider: https://www.mdpi.com/ .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.