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Understanding Quality in Manufacturing Processes and Measurement Techniques

This document delves into the concepts of quality in manufacturing, discussing variations, statistical representations, and the importance of robustness in design and production. It outlines the importance of accurate measurement methods through examples, including unit measurements, engineering specifications, and statistical data analysis. Key aspects include the differences between precision and accuracy, quality loss implications, and a comparative analysis of manufacturing processes. This comprehensive overview is vital for professionals looking to enhance their understanding of quality management in manufacturing.

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Understanding Quality in Manufacturing Processes and Measurement Techniques

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  1. 2.008QUALITY

  2. Market Research Conceptual Design Design for Manufacture Unit Manufacturing Processes Assembly and Joining Factory, Systems & Enterprise Manufacture

  3. Outline • What is quality? • Variations • Statistical representation • Robustness Read Chapter 35 & 36

  4. What is quality?

  5. Variations • Part and assembly variations • Variations in conditions of use • Deterioration

  6. Variable OutcomeResults from measuring intermediate or final process outcome Men Machines Outcome is measured • Unit of measure (mm, kg, etc.) •The measurement method must produce accurate and precise results over time Materials Methods Outcome examples • Shaft O.D. (inches) • Hole distance from reference surface (mm) • Circuit resistance (ohms) • Heat treat temperature (degrees) • Railcar transit time (hours) • Engineering change processing time (hours)

  7. Technological Development • Physical masters • Engineering drawings • Go / No-Go gage • Statistical measurement • Continuous on-line measurement

  8. +/-0.1” +/-0.1” 4.5” 1.5” +/-0.004” 1.00” Engineered Part • Design specification +/-0.004” • Process specification

  9. Engineered Part (cont’d) • Raw data, n = 20 1.0013 0.9986 1.0015 0.9996 1.0060 0.9997 1.0029 0.9977 1.0042 0.9955 1.0019 0.9970 0.9992 1.0034 0.9995 1.0022 1.0020 0.9960 1.0013 1.0020 • 6 Buckets .994 -.996 2 .996 -.998 2 .998 -1.000 5 1.000 -1.002 6 1.002 -1.004 3 1.004 -1.006 2

  10. +/-0.1” +/-0.1” 4.5” 1.5” +/-0.004” 1.00” 7 6 5 4 Frequency 3 2 1 0 .994 - .996 - .998 - 1.000 - 1.002 -1.004 – .996 .998 1.000 1.002 1.004 1.006 Diameter, in. Engineered Part • Design specification +/-0.004” • Process specification

  11. Failing balls hit these pins and go either left or right Ball part way through row of pins Manufacturing Outcome: Central Tendency

  12. 45 40 35 30 Frequency 25 20 15 10 5 0 0.71670.75058 0.852220.8861 0.8861-0.91998 0.750580.78446 0.784460.81834 0.953860.98774 0.81834-0.85222 0.91998-0.95386 Mass, g Central TendencyHalloween M&M mass histograms: n = 100

  13. Mean 0.89876 Median 0.90183 Std. Dev 0.04255 Minimum 0.71670 Maximum 0.98774 0.7000 0.7580 0.8160 0.8740 0.9320 0.9900 Dispersion Mass of Halloween M&M/ g, n=100

  14. Statistical Distribution • Central tendency • Sample mean (arithmetic): • Sample median • Measures of dispersion • Standard deviation • Variance • Range

  15. f( x) x a b P z Normal Probability Density Function Probability Normalized

  16. P 0 Z1 Areas under the Normal Distribution Curve

  17. P 0 Z1 Areas under the Normal Distribution Curve

  18. Normal Distribution Example Take a M&M with mass = 0.9g, based on our calculated normal curve, how many M&M’s have a mass greater than 0.9g? Z = (0.9 -0.89876) / 0.04255 = 0.29 Area to the right of Z=0.29, from table on previous page: P = (1 -0.6141) = 0.3859 So, the number of M&M’s with a mass greater than 0.9g # = P*n = 0.3859 * 100 = 39

  19. Robustness Not precise Precise Not accurate Accurate

  20. Tokyo Color Density San Diego T-5 T T+5 D C B A B C D Quality Loss $100 T-5 T T+5 A Tale of Two Factories

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