CPL (Lower Capability Index)

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2. ์†๋ ฅ์ด ์ผ์ •ํ•˜๊ฒŒ ์ฆ๊ฐ€ํ•˜๋Š” ์šด๋™ โ…ข.ํž˜๊ณผ ์šด๋™ 2.์—ฌ๋Ÿฌ ๊ฐ€์ง€ ์šด๋™. ๋„์ž… โ…ข.ํž˜๊ณผ ์šด๋™ 2. ์—ฌ๋Ÿฌ ๊ฐ€์ง€ ์šด๋™ 2. ์†๋ ฅ์ด ์ผ์ •ํ•˜๊ฒŒ ์ฆ๊ฐ€ํ•˜๋Š” ์šด๋™.
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6 ์žฅ. printf ์™€ scanf ํ•จ์ˆ˜์— ๋Œ€ํ•œ ๊ณ ์ฐฐ printf ํ•จ์ˆ˜ ์ด์•ผ๊ธฐ printf ๋Š” ๋ฌธ์ž์—ด์„ ์ถœ๋ ฅํ•˜๋Š” ํ•จ์ˆ˜์ด๋‹ค. โ€“ ์˜ˆ์ œ printf1.c ์ฐธ์กฐ printf ๋Š” ํŠน์ˆ˜ ๋ฌธ์ž ์ถœ๋ ฅ์ด ๊ฐ€๋Šฅํ•˜๋‹ค. ํŠน์ˆ˜ ๋ฌธ์ž์˜ ๋ฏธ \a ๊ฒฝ๊ณ ์Œ ์†Œ๋ฆฌ ๋ฐœ์ƒ \b ๋ฐฑ์ŠคํŽ˜์ด์Šค (backspace)
์ธก์ • ์‹œ์Šคํ…œ ๋ถ„์„. ์ธก์ • ์‹œ์Šคํ…œ ๋ถ„์„ (Data Collection) PURPOSE: ์ธก์ • ์‹œ์Šคํ…œ์˜ ์‹ ๋ขฐ ์ •๋„๋ฅผ ๊ฐ๊ด€์ ์ธ ๋ฐฉ๋ฒ•์œผ๋กœ ์ œ์‹œ OBJECTIVE: 1. ์ธก์ •์‹œ ๋ฐœ์ƒํ•˜๋Š” ๋ณ€๋™์˜ ์š”์ธ์„ ์ดํ•ดํ•˜๊ณ  ๋ช…ํ™•ํžˆ ํ•œ๋‹ค. 2. Minitab ์œผ๋กœ Gage R&R Study ๋ฅผ.
์ถœ์„์ˆ˜์—… ๊ณผ์ œ โ€“ ์ด 5๋ฌธ์ œ, 10์›” 25์ผ ์ œ์ถœ ์ •๋ณดํ†ต๊ณ„ํ•™๊ณผ ์žฅ์˜์žฌ ๊ต์ˆ˜.
์žฌ๋ฃŒ์ˆ˜์น˜ํ•ด์„ HW # ๋ฐ•์žฌํ˜.
ํ˜„์žฅ์˜ ์ž‘์—…๊ฐœ์„  ๋ฐ ๊ด€๋ฆฌ๊ธฐ๋ฒ• ๊ณผ์ •.
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์ œ12์ฃผ ํšŒ๊ท€๋ถ„์„ Regression Analysis
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๊ณต์ •๋Šฅ๋ ฅ๊ณผ ๊ณต์ •์‹ค์ .
๋””์ง€ํ„ธ์˜์ƒ์ฒ˜๋ฆฌ ๋ฐ ์‹ค์Šต ๋Œ€๊ตฌ๋ณด๊ฑด๋Œ€ํ•™ ๋ฐฉ์‚ฌ์„ ๊ณผ.
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๋ฉ€ํ‹ฐ๋ฏธ๋””์–ด ์‹œ์Šคํ…œ (์•„๋‚ ๋กœ๊ทธ ์ด๋ฏธ์ง€,์‹ ํ˜ธ๋ฅผ ๋””์ง€ํ„ธ๋กœ ๋ณ€ํ™˜ ๋ฐฉ๋ฒ•) ์ด๋ฆ„ : ๊น€๋Œ€์ง„ ํ•™๋ฒˆ :
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6์žฅ. printf์™€ scanf ํ•จ์ˆ˜์— ๋Œ€ํ•œ ๊ณ ์ฐฐ
์ƒ๊ด€ํ•จ์ˆ˜ correlation function
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์—”์ง€๋‹ˆ์–ด ์ž…์žฅ์—์„œ ๋ฐ”๋ผ๋ณธ ํ’ˆ์งˆ Quality in Engineering
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์ œ4์žฅ ์ œ์–ด ์‹œ์Šคํ…œ์˜ ์„ฑ๋Šฅ.
์…์‚ฐ๊ด€๋ฆฌ์‹œ์Šคํ…œ ์ž‘์—…์ผ๋ณด ๋“ฑ๋ก โ˜ž โ˜ž ์ž‘์—…์ผ๋ณด๋“ฑ๋ก - ์‹คํ–‰ํ™”๋ฉด C B A ์‚ฌ์šฉ์„ค๋ช…
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์ œ 10 ์žฅ ์˜์‚ฌ๊ฒฐ์ •์ด๋ž€ ์˜์‚ฌ๊ฒฐ์ •์€ ์„ ํƒ์ด๋‹ค.
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CEO๊ฐ€ ๊ฐ€์ ธ์•ผ ํ•  ํ’ˆ์งˆ ํ˜์‹  ๋งˆ์ธ๋“œ.
์‹ค๋ฌผ๊ด€๋ฆฌ์˜ ํ•œ๊ณ„ ์‹ค๋ฌผ ๊ด€๋ฆฌ์˜ ํ•œ๊ณ„์ ์— ๋Œ€ํ•œ ์‹ค๋ก€(๋ณด์ด์ง€ ์•Š๋Š” ๊ฒƒ,์ •๋Ÿ‰ํ™” ๋  ์ˆ˜ ์—†๋Š” ๊ฒƒ)
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Chapter 1 ๋‹จ์œ„, ๋ฌผ๋ฆฌ๋Ÿ‰, ๋ฒกํ„ฐ.
์ œ 5์žฅ ์ œ์–ด ์‹œ์Šคํ…œ์˜ ์„ฑ๋Šฅ ํ”ผ๋“œ๋ฐฑ ์ œ์–ด ์‹œ์Šคํ…œ ๊ณผ๋„ ์„ฑ๋Šฅ (Transient Performance)
1. MTBF์˜ ์ •์˜ ํ‰๊ท ๊ณ ์žฅ๊ฐ„๊ฒฉ์‹œ๊ฐ„ (Mean Time Between Failures)์˜ ์˜๋ฏธ๋กœ ์‹ ๋ขฐ์„ฑ์„ ๋‚˜ํƒ€๋‚ด๋Š” ์ง€ํ‘œ์ด๋‹ค.
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A SMALL TRUTH TO MAKE LIFE 100%
๋ฌธ์ œ์˜ ๋‹ต์•ˆ ์ž˜ ์ƒ๊ฐํ•ด ๋ณด์‹œ๊ธฐ ๋ฐ”๋ž๋‹ˆ๋‹ค..
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Presentation transcript:

CPL (Lower Capability Index) ๊ทœ๊ฒฉํ•˜ํ•œ (LSL) ๊ทœ๊ฒฉ์ƒํ•œ (USL) ๋ชฉํ‘œ ๊ณต์ • ํ‰๊ท  ๊ณต์ •์‚ฐํฌ = 6 s

๊ณต์ • ์ค‘์‹ฌ์œ„์น˜์˜ ์˜ํ–ฅ ํ‰๊ฐ€์— ์ ํ•ฉํ•œ ์ง€์ˆ˜ ๋ชฉํ‘œ ๊ณต์ •์‚ฐํฌ = 6 s ๊ทœ๊ฒฉํ•˜ํ•œ (LSL) ๊ทœ๊ฒฉ์ƒํ•œ (USL) ๋ชฉํ‘œ ๊ณต์ • ํ‰๊ท  ๊ณต์ •์‚ฐํฌ = 6 s โ€ป ์ผ๋ฐ˜์ ์œผ๋กœ ๊ณต์ •ํ‰๊ท ( )์ด ๊ทœ๊ฒฉ์ค‘์‹ฌ ๋ณด๋‹ค ํฌ๋‹ค๋ฉด CPU๋ฅผ ์‚ฌ์šฉํ•˜๊ณ , ์ž‘๋‹ค๋ฉด CPL์„ ์‚ฌ์šฉํ•œ๋‹ค.

Cpk, Ppk ๋‹ค๋ฅธ ์‚ฐ์ถœ ๋ฐฉ๋ฒ• ์‚ฐ์ถœ ๋ฐฉ๋ฒ• LSL USL 32ppm 22,750ppm ๋‹จ, 0โ‰คK โ‰ค 1 99.72% ฮผ

Cpk ์—ฐ์Šต๋ฌธ์ œ LSL = 3 mm USL = 5 mm = 4.4 mm s = .2 mm 5 3 4 ๋ชฉํ‘œ 3 4 5 = x Why could you choose at this point (or even earlier) to continue on with only the CPU formula? ์ด๊ณต์ •์˜ ๊ณต์ •๋Šฅ๋ ฅ๋น„(CR)์™€ Cpk ๊ฐ’์€?

Cpk ์˜ˆ์ œ Cpk = 1 Cpk > 1 Cpk = 1.33 Cpk = 1.33 Cpk ๊ฐ€ 1์ธ ๊ฒฝ์šฐ๋Š” ๊ณต์ •์‚ฐํฌ์˜ 6s USL LSL Cpk = 1 Cpk > 1 ๊ณต์ •์‚ฐํฌ์˜ 6s ๋ฒ”์œ„ Cpk ๊ฐ€ 1์ธ ๊ฒฝ์šฐ๋Š” ๊ณต์ •์‚ฐํฌ์˜ 6s ๋ฒ”์œ„๊ฐ€ ๊ณต์ •์˜ ๊ทœ๊ฒฉ๋ฒ”์œ„์™€ ์ •ํ™•ํžˆ ์ผ์น˜ํ•จ์„ ์˜๋ฏธํ•œ๋‹ค. Cpk ๊ฐ€ 1๋ณด๋‹ค ํฐ ๊ฒฝ์šฐ๋Š” ๊ณต์ •์‚ฐํฌ์˜ 6s ๋ฒ”์œ„๊ฐ€ ๊ทœ๊ฒฉ๋ฒ”์œ„ ๋‚ด์— ์žˆ์Œ์„ ์˜๋ฏธํ•œ๋‹ค. USL LSL Cpk = 1.33 USL LSL Cpk = 1.33

์ถ”๊ฐ€์ ์ธ Cpk ์˜ˆ์ œ Cpk ๊ฐ€ ์Œ์ˆ˜ (< 0) Cpk = 0 0 < Cpk < 1 Cpk = 0 LSL USL Cpk ๊ฐ’์ด ์Œ์ˆ˜์ธ ๊ฒฝ์šฐ๋Š” ๊ณต์ • ์ค‘์‹ฌ๊ฐ’์ด ๊ทœ๊ฒฉ์„  ๋ฐ–์— ์žˆ์Œ์„ ์˜๋ฏธํ•œ๋‹ค. Cpk = 0 0 < Cpk < 1 LSL USL LSL USL Cpk ๊ฐ’์ด 0๊ณผ 1์‚ฌ์ด์— ์žˆ๋Š” ๊ฒฝ์šฐ๋Š” ๊ณต์ •์‚ฐํฌ์˜ 6s ๋ฒ”์œ„์˜ ์ผ๋ถ€๊ฐ€ ๊ทœ๊ฒฉ ๋ฐ–์— ์žˆ์Œ์„ ์˜๋ฏธํ•œ๋‹ค. Cpk ๊ฐ’์ด 0์ธ ๊ฒฝ์šฐ๋Š” ๊ณต์ • ์ค‘์‹ฌ๊ฐ’์ด ๊ทœ๊ฒฉ์„ ์— ์œ„์น˜ํ•ด ์žˆ์Œ์„ ์˜๋ฏธํ•œ๋‹ค. LSL USL Cpk = 0

Cpk, Ppk โ–ถ ๊ณต์ • ์น˜์šฐ์นจ์„ ๋ฐ˜์˜ํ•œ ์ง€์ˆ˜ A, B, C์˜ ์ถ”์ •๋ถˆ๋Ÿ‰๋ฅ ๊ณผ ๊ณต์ •๋Šฅ๋ ฅ์€? A B C ๊ทœ๊ฒฉํ•˜ํ•œ (LSL) ๊ทœ๊ฒฉ์ค‘์‹ฌ (Nominal) ๊ทœ๊ฒฉ์ƒํ•œ (USL) A, B, C์˜ ์ถ”์ •๋ถˆ๋Ÿ‰๋ฅ ๊ณผ ๊ณต์ •๋Šฅ๋ ฅ์€? A B C 6 ฯƒ 6 ฯƒ 6 ฯƒ

๊ณต์ •๋Šฅ๋ ฅ๊ณผ ์ถ”์ •๋ถˆ๋Ÿ‰๋ฅ ์˜ ๊ด€๊ณ„ CpK PPH IPTV PPM .33 31.7 317.5 317,500 ์–‘์ธก๊ทœ๊ฒฉ์„ ๊ฐ€์ง€๋Š” ๊ณต์ • ์ •๊ทœ๋ถ„ํฌ ๊ณต์ • ๋ชฉํ‘œ๊ฐ’์ด ๊ทœ๊ฒฉ์ค‘์‹ฌ๊ณผ ์ผ์น˜ CpK PPH IPTV PPM .33 31.7 317.5 317,500 1.00 .27 2.7 2,700 1.3 .001 .096 96 1.5 .00068 .0068 6.8 2.0 .0000002 .000002 .002

๊ณต์ • ํŒ์ • ๊ธฐ์ค€ ๋“ฑ๊ธ‰ Cp or Cpk CR(%) ๊ณต์ •๋Šฅ๋ ฅ์˜ ํ‰๊ฐ€ / ์กฐ์ฒ˜ ๊ฐœ์„  ํŠน๊ธ‰ Cpโ‰ฅ1.67 59.9% 1๋“ฑ๊ธ‰ * ๊ณต์ •๋Šฅ๋ ฅ์€ ์ถฉ๋ถ„ํ•˜๋‹ค. ์œ ์ง€๊ด€๋ฆฌ์— ๋…ธ๋ ฅ์„ ์ง‘์ค‘ํ•˜๊ณ , ๊ด€๋ฆฌ๋ฐฉ์•ˆ์„ ๊ณต์œ ํ•œ๋‹ค. 1๋“ฑ๊ธ‰ 1.67 >Cp โ‰ฅ1.33 75.2% ์ด์ƒ์ ์ธ ์ƒํƒœ์ด๋ฏ€๋กœ ์œ ์ง€ํ•˜๊ณ , ์ง€์†์  ๊ฐœ์„ ์„ ์ถ”์ง„ํ•œ๋‹ค. 2๋“ฑ๊ธ‰ 1.33 >Cp โ‰ฅ1.00 100% *๊ณต์ •๋Šฅ๋ ฅ์€ ๊ทธ๋Ÿฐ๋Œ€๋กœ ๊ดœ์ฐฎ๋‹ค ๊ณต์ •๊ด€๋ฆฌ๋ฅผ ์ฒ ์ €ํžˆ ํ•˜์—ฌ ๊ด€๋ฆฌ์ƒํƒœ๋ฅผ ์œ ์ง€ํ•œ๋‹ค. Cp๊ฐ€ 1์— ๊ฐ€๊นŒ์›Œ์ง€๋ฉด, ๋ถˆ๋Ÿ‰ํ’ˆ ๋ฐœ์ƒ์˜ ์šฐ๋ ค๊ฐ€ ์žˆ์œผ๋ฏ€๋กœ ํ•„์š”์— ๋”ฐ๋ผ ์กฐ์ฒ˜๋ฅผ ์ทจํ•œ๋‹ค. 3๋“ฑ๊ธ‰ 1.00 >Cp โ‰ฅ0.67 149.3% * ๊ณต์ •๋Šฅ๋ ฅ์ด ๋ถ€์กฑํ•˜๋‹ค ๋ถˆ๋Ÿ‰ํ’ˆ์ด ๋ฐœ์ƒ๋˜๊ณ  ์žˆ๋‹ค. ์ „์ˆ˜์„ ๋ณ„, ๊ณต์ •์˜ ์ฒ ์ €ํ•œ ๊ด€๋ฆฌ,๊ฐœ์„ ์ด ํ•„์š” 4๋“ฑ๊ธ‰ 0.67 >Cp ์ดˆ๊ณผ * ๊ณต์ •๋Šฅ๋ ฅ์ด ๋Œ€๋‹จํžˆ ๋ถ€์กฑํ•˜๋‹ค. ๋„์ €ํžˆ ํ’ˆ์งˆ์„ ๋งŒ์กฑ์‹œํ‚ฌ ์ƒํƒœ๊ฐ€ ์•„๋‹ˆ๋‹ค. ์ƒ์‚ฐ์„ ์ค‘์ง€ํ•˜๋“ ๊ฐ€, ํ’ˆ์งˆ์˜ ๊ฐœ์„ ์›์ธ์„ ์ถ”๊ตฌ ํ•˜๋Š” ํ•œํŽธ, ๊ธด๊ธ‰ํ•œ ๋Œ€์ฑ…์„ ํ•„์š”๋กœ ํ•œ๋‹ค. ๋˜๋Š”, ๊ทœ๊ฒฉ์„ ์žฌ๊ฒ€ํ†  ํ•œ๋‹ค.

๊ณต์ • ํŒ์ • ๊ธฐ์ค€ โ–ถ ์ผ๋ฐ˜์ ์ธ ์ž๋™์ฐจ ์—…๊ณ„ ์š”๊ตฌ์‚ฌํ•ญ ๊ณ ๊ฐ ์š”๊ตฌ์‚ฌํ•ญ ์šฐ์„ ! ์ดˆ๊ธฐ ๊ณต์ •๋Šฅ๋ ฅ ์–‘์‚ฐ ๊ณต์ •๋Šฅ๋ ฅ Ppk โ‰ฅ 1.67 Cpk โ‰ฅ 1.67 Ppk โ‰ฅ 1.67 Cpk โ‰ฅ 1.67 ๊ณ ๊ฐ ์š”๊ตฌ์‚ฌํ•ญ ์šฐ์„ !

Cpk and Ppk - ์—ฐ์Šต๋ฌธ์ œ ๊ด€๋ฆฌ๋„๋ฅผ ํ†ตํ•ด ํŠน์ •๊ณต์ •์ด ์•ˆ์ •์ƒํƒœ์ด๋ฉฐ ํ‰๊ท ( )์ด 4.4mm์ด๊ณ , ๊ฐ€ 0.4652mm์ธ ๊ฒƒ์„ ์•Œ์•˜๋‹ค. ๊ณต์ •๊ทœ๊ฒฉ์€ 4 mm ยฑ 1 mm ์ด๋‹ค. ๋‹จ. d2๋Š” 2.326 (๊ตฐ ํฌ๊ธฐ 5) 1. ๊ณต์ •์˜ ํ‘œ์ค€ํŽธ์ฐจ๋Š” ์–ผ๋งˆ์ธ๊ฐ€ (s)? 2. Cpk๋Š” ์–ผ๋งˆ์ธ๊ฐ€? 3. Cpk์™€ ๊ณต์ •์„ ๊ฐœ์„ ์„ ํ•˜๊ธฐ ์œ„ํ•ด ๋ฌด์—‡์„ ํ•ด์•ผ ํ•˜๋Š”๊ฐ€? ์ƒ๊ธฐ ์˜ˆ์—์„œ 30๊ฐœ์˜ ๋ฌด์ž‘์œ„ ์ƒ˜ํ”Œ์„ ์ทจํ•˜์—ฌ ์ธก์ •ํ•˜์˜€์„ ๋•Œ ํ‰๊ท ( )์ด 4.4 mm์ด๊ณ  ํ‘œ์ค€ํŽธ์ฐจ(s)๊ฐ€ 0.3mm ์ด์˜€๋‹ค. 4. Ppk๋Š” ์–ผ๋งˆ์ธ๊ฐ€? 5. ๊ตฌํ•ด์ง„ Ppk๋ฅผ Cpk์™€ ๋น„๊ตํ•˜๊ณ , ์ฐจ์ด๋ฅผ ์„ค๋ช…ํ•˜์‹œ์˜ค Facilitator Notes: We will work through the first three questions together, and then you will do the last two. 1. The process standard deviation will be: (the d2 was taken from the table on p.69) 2. Cpk for the process: From the data and/or the chart below, it is apparent that CPU will be the formula for this problem (why?). 3 mm 4 mm 5 mm X = 4.4 mm =

๊ณต์ •๋Šฅ๋ ฅ์ง€์ˆ˜ - ์—ฐ์Šต๋ฌธ์ œ -R๊ด€๋ฆฌ๋„๋ฅผ ํ†ตํ•ด ๊ณต์ •๋Šฅ๋ ฅ์„ ๋ถ„์„ํ•ด ๋ณด์ž. ๊ณต์ •์€ ์•ˆ์ •์ƒํƒœ์ด๋ฉฐ, ๊ณต์ • ํ‰๊ท ( )์€ 10, ๋Š” 2.32์ด๋‹ค. ๋‹จ, ๊ทœ๊ฒฉ์€ 12 ยฑ 4์ด๋ฉฐ, d2๋Š” 2.32 ์ด๋‹ค. ์•„๋ž˜ ๋ฌธ์ œ๋ฅผ ํ‘ธ์‹œ์˜ค 1. ๊ณต์ • ๋ถ„ํฌ ๊ทธ๋ฆผ์„ ๊ทœ๊ฒฉ๊ณผ ํ•จ๊ป˜ ๊ทธ๋ฆฌ์‹œ์˜ค 2. CR ๊ฐ’์„ ๊ณ„์‚ฐํ•˜๊ณ  ๊ทธ ์˜๋ฏธ๋ฅผ ํ•ด์„ํ•˜์‹œ์˜ค. 3. Cp ๊ฐ’์„ ๊ณ„์‚ฐํ•˜๊ณ , CR๊ฐ’๊ณผ ๋น„๊ตํ•˜๊ณ  ๊ทธ ์˜๋ฏธ๋ฅผ ํ•ด์„ํ•˜์‹œ์˜ค. 4. Cpk ๊ฐ’์„ ๊ตฌํ•˜๊ณ  ๊ณต์ •๋Šฅ๋ ฅ์„ ํŒ๋‹จํ•˜์‹œ์˜ค 5. ๋‹น์‹ ์ด ์ž‘์„ฑํ•œ ๊ณต์ •๋ถ„ํฌ๊ทธ๋ฆผ๊ณผ ๊ณต์ •๋Šฅ๋ ฅ ๋ถ„์„๊ฒฐ๊ณผ๋ฅผ ํ† ๋Œ€๋กœ ๋‹ค์Œ ์ค‘ ์–ด๋А ๊ฒƒ์„ ์ˆ˜ํ–‰ํ•ด์•ผ ํ•˜๋Š”๊ฐ€? a. ์กฐ์น˜ํ•  ํ•„์š”๊ฐ€ ์—†๋‹ค. b. ๊ณต์ •์˜ ์ค‘์‹ฌ์„ ๊ทœ๊ฒฉ์ค‘์‹ฌ๊ณผ ์ผ์น˜์‹œ์ผœ์•ผ ํ•œ๋‹ค. c. ์‚ฐํฌ๋ฅผ ๊ฐ์†Œ์‹œ์ผœ์•ผ ํ•œ๋‹ค. d. ์„ค๊ณ„๊ทœ๊ฒฉ์„ ์™„ํ™”ํ•ด์•ผ ํ•œ๋‹ค. Facilitator Notes: You have 20 minutes to answer these questions. Cover the above information and directions with the participants. Give them the 20 minutes prescribed for the exercise; you may consider combining the activity with a ten-minute break, to allow participants flexibility in controlling their schedule. If you have the participants work individually, combining the activity with a break allows people who work faster to do other things and even leave the room, without forcing them to wait for the more methodical individuals. This can lower the possible stress on both sorts of participant from this exercise. When the exercise has been finished and the group is back together, you may either review the answers with the participants, or tell them that you will review the answers a bit later, as they get into the area of โ€œApplicationsโ€ of process measurement. The answers to this exercise are on page 75.

ํ’ˆ์งˆ์— ๋Œ€ํ•œ โ€œ๊ณจ๋Œ€โ€ ์‚ฌ๊ณ ๋ฐฉ์‹ (โ€œ๊ทœ๊ฒฉ์— ๋Œ€ํ•œ ์ ํ•ฉ์„ฑโ€) LSL USL x x x Facilitator Notes: In the next few pages we will consider the need sometimes to โ€œimproveโ€ process evaluation measures like Cpk and CR. The traditional โ€œgoal postโ€ view of quality is that a product characteristic is โ€œgoodโ€ if its value is within specifications, and โ€œbadโ€ if its value is outside of specs. Under this view, there is no distinction between a result just inside of a spec limit, and one near the center of specification. Even as late as the 1980s, โ€œConformance to Specโ€ was considered all that was needed for most product characteristics. BAD GOOD BAD

๊ณต์ •์ค‘์‹ฌ์œ„์น˜์™€ ๋ชฉํ‘œ๊ฐ’ ์ผ์น˜์˜ ์ค‘์š”์„ฑ - Target โ€œ์†์‹คํ•จ์ˆ˜โ€ LSL USL X B๊ณต์ •๋ณด๋‹ค A๊ณต์ •์˜ ์†์‹ค์ด ํ›จ์”ฌ ์ ๋‹ค. PROCESS โ€œAโ€ โ€œBโ€ - Target โ€œ์†์‹คํ•จ์ˆ˜โ€ Facilitator Notes: However, Dr. Genichi Taguchi helped promote the idea that a process with less variation around the target value will produce more product closer to that target value -- which often results in greater customer satisfaction. Facilitator explains what the โ€œloss curveโ€ is and how it shows increasingly poor results the farther a process deviates from the nominal. For many characteristics, a process centered more on the target (here, mid-specification) will produce better products that result in greater customer satisfaction. B๊ณต์ •๋ณด๋‹ค A๊ณต์ •์˜ ์†์‹ค์ด ํ›จ์”ฌ ์ ๋‹ค. ๋ชฉํ‘œ๊ฐ’์— ๋งž์ถ”๋„๋ก ๋…ธ๋ ฅํ•˜๋ผ!!!

์‚ฐํฌ ๊ฐ์†Œ์˜ ์ค‘์š”์„ฑ Target โ€œ์†์‹คํ•จ์ˆ˜โ€ LSL USL (Both B๊ณต์ •๋ณด๋‹ค A๊ณต์ •์˜ ์†์‹ค์ด ํ›จ์”ฌ ์ ๋‹ค. Processes) โ€œ์†์‹คํ•จ์ˆ˜โ€ Facilitator Notes: Even when two distributions are well-targeted, their results will differ depending upon their dispersion. More of the tightly-dispersed Process A is closer to the nominal than is true for Process B, and you can see that more of B will exist at higher loss levels. Quality is best achieved by minimizing dispersion around the best, nominal, or target value. PROCESS โ€œAโ€ PROCESS โ€œBโ€ B๊ณต์ •๋ณด๋‹ค A๊ณต์ •์˜ ์†์‹ค์ด ํ›จ์”ฌ ์ ๋‹ค. ์‚ฐํฌ๋ฅผ ์ค„์ด๋„๋ก ๋…ธ๋ ฅํ•˜๋ผ!!!

Quality Loss Function โ–ถ Loss Function ์œ ํ˜• ์ผ๋ฐ˜ํŠน์„ฑ (Standards Care) : ์•„๋ž˜ ๊ทธ๋ฆผ์ฒ˜๋Ÿผ ๊ทœ๊ฒฉ ๋‚ด์—๋งŒ ์žˆ๋‹ค๋ฉด ๊ณต์ •์‚ฐํฌ์— ์˜ํ•œ ์†์‹ค์ด ๋งค์šฐ ์ž‘๊ฒŒ ์ผ์œผํ‚ค๋Š” ์ œํ’ˆ/๊ณต์ • ํŠน์„ฑ์œผ๋กœ Standards Care๊ฐ€ ํ•„์š” 100% ๋ถˆ๋Ÿ‰๋ฅ  50% Loss Function 0% 50 60 70 80 90 100 ์ผ๋ฐ˜ torque (N-m) โ†’ ๊ณต์ •๋ถ„ํฌ์˜ ยฑ3 ๏ณ๊ฐ€ ๊ทœ๊ฒฉ ๋‚ด์— ์žˆ๋„๋ก ๊ณต์ •์„ ๊ด€๋ฆฌํ•œ๋‹ค. (์„ ํƒ์ ์œผ๋กœ SPC์˜ ์ ์šฉ์ด ํ•„์š”ํ•˜๋‹ค)

Quality Loss Function โ–ถ Loss Function ์œ ํ˜• ์ œํ’ˆ ํ’ˆ์งˆ ๊ด€๋ฆฌ ํŠน์„ฑ(Product Quality Characteristics : PQC) : ์•„๋ž˜ ๊ทธ๋ฆผ์ฒ˜๋Ÿผ ๊ทœ๊ฒฉ์„ ๋ฒ—์–ด๋‚˜๋ฉด ๊ธ‰๊ฒฉํ•˜๊ฒŒ ์†์‹ค์ด ์ฆ๊ฐ€ํ•˜๋Š” ํŠน์„ฑ๋“ค๋กœ Extra Care๊ฐ€ ํ•„์š”ํ•˜๋‹ค 100% ๋ถˆ๋Ÿ‰๋ฅ  50% Under Torque๋กœ ์ธํ•œ Wheel Rattle ๋ณผํŠธ ํŒŒ์†์— ์˜ํ•œ ์†์‹ค ์ฆ๊ฐ€ 0% 50 60 70 80 90 100 Wheel nut torque (N-m) โ†’ ๊ณต์ •๋ถ„ํฌ์˜ ยฑ6 ๏ณ๊ฐ€ ๊ทœ๊ฒฉ ๋‚ด์— ์žˆ๋„๋ก ๊ณต์ •์„ ๊ด€๋ฆฌํ•œ๋‹ค. (SPC์˜ ์ ์šฉ์ด ํ•„์š”ํ•˜๋‹ค)

Quality Loss Function โ–ถ Loss Function ์œ ํ˜• ํ•ต์‹ฌ ์ œํ’ˆ ํŠน์„ฑ(Key Product Characteristics : KPC) : ์•„๋ž˜ ๊ทธ๋ฆผ์ฒ˜๋Ÿผ ๊ทœ๊ฒฉ๊ณผ ์ƒ๊ด€ ์—†์ด Target์„ ๋ฒ—์–ด๋‚˜๋ฉด ์†์‹ค์„ ๋ฐœ์ƒ์‹œํ‚ค๋Š” ํŠน์„ฑ์œผ๋กœ Extra Care๊ฐ€ ํ•„์š”ํ•˜๋‹ค 100% ๋ถˆ๋Ÿ‰๋ฅ  50% ๊ณ ๊ฐ์ด ๋„ˆ๋ฌด ๋ฅ๋‹ค๊ณ  ๋А๋‚€๋‹ค ๊ณ ๊ฐ์ด ๋„ˆ๋ฌด ์ฐจ๊ฐ‘๋‹ค๊ณ  ๋А๋‚€๋‹ค 0% -5 -6 2 8 9 10 ๋ƒ‰์žฅ ์˜จ๋„ (C) โ†’ Target์„ ๋งž์ถ”๊ธฐ ์œ„ํ•ด ์ง€์†์ ์ธ ๊ณต์ •์‚ฐํฌ ๊ฐœ์„ ํ™œ๋™์ด ํ•„์š”ํ•˜๋‹ค. (์ ๊ทน์ ์ธ SPC์˜ ์ ์šฉ์ด ํ•„์š”ํ•˜๋‹ค)

Quality Loss Function Characteristics ์š”๊ตฌ ๊ณต์ •๋Šฅ๋ ฅ Cp ยณ 2.00 KPC Cpk ยณ 1.5 โ–ถ ํŠน์„ฑ์— ๋”ฐ๋ฅธ ๊ณต์ •๋Šฅ๋ ฅ ๊ธฐ์ค€(GMPT ์‚ฌ๋ก€) Characteristics ์š”๊ตฌ ๊ณต์ •๋Šฅ๋ ฅ Cp ยณ 2.00 Cpk ยณ 1.5 KPC Cp ยณ 2.00 Cpk ยณ 1.5 PQC ์ผ๋ฐ˜ ํŠน์„ฑ๋„ PQC ์ˆ˜์ค€์˜ ๊ด€๋ฆฌ ๊ถŒ๊ณ  Standard Product Characteristic Cp ยณ 1.00 Cpk ยณ 1.00

MODULE III: APPLICATIONS SPC Statistical Process Control MODULE III: APPLICATIONS 3. ๊ณต์ •ํ‰๊ฐ€ ์ง€์ˆ˜์˜ ์ ์šฉ Facilitator Notes: We are now going to consider how process evaluation measurement information can be used to understand and improve processes (why we are here!).

๏ƒผ ๊ณต์ •๋Šฅ๋ ฅ์กฐ์‚ฌ์˜ ์ˆ˜ํ–‰ ์„ค๋น„/๊ณต์ •์„ ํŒŒ์•…ํ•œ๋‹ค. ํŠน์„ฑ์„ ์„ ํƒํ•œ๋‹ค. ๊ฒŒ์ด์ง€(์ธก์ •๊ธฐ)๋ฅผ ๊ฒ€์ฆํ•œ๋‹ค. 15-20 ๊ตฐ์— ๋Œ€ํ•œ ๋‹จ๊ธฐ ๊ณต์ •๋Šฅ๋ ฅ์„(Cp and Cpk) ํ‰๊ฐ€ํ•œ๋‹ค ๊ด€๋ฆฌ๋„๋กœ ์žฅ๊ธฐ ๊ณต์ •๋Šฅ๋ ฅ์„ ํ‰๊ฐ€ํ•œ๋‹ค. ์ด์ƒ์›์ธ์„ ๋ฐœ๊ฒฌํ•˜์—ฌ ์ œ๊ฑฐํ•œ๋‹ค. Facilitator Notes: A capability study is made for different reasons, for example to respond to a customer request or to improve the product quality. The following support the effective conduct and use of process evaluation studies: understand the effects of the process upon customer satisfaction develop a process description including inputs, process steps and quality characteristics define the process conditions for each process variable (feed, speed, temperature, etc.) evaluate the measurement error decide upon the size of the sample. Short-term capability uses consecutive production over a one time period. The sample size should be a minimum of 15-20 subgroups of parts. plan to use control charts to evaluate the stability of the process for long-term capability studies be prepared to spend time investigating for assignable causes and eliminating them from the process

๊ณต์ •ํ‰๊ฐ€์ง€์ˆ˜์˜ ์ ์šฉ ์‹ ๊ทœ์žฅ๋น„์˜ ํ‰๊ฐ€ ๊ณต์ •์˜ ๊ณ ์œ  ๋˜๋Š” ์ด ๋ณ€๋™์— ๊ทผ๊ฑฐํ•œ ๊ณต์ฐจ๊ฒ€ํ†  ์ •๊ธฐ์ ์ธ ๊ณต์ •์‹ค์  ์กฐ์‚ฌ ๊ณต์ •๊ฐ€๋™ ์ค‘ ์กฐ์ •์˜ ์˜ํ–ฅ์„ ์ฐพ๊ธฐ ์œ„ํ•จ ์‹ค์งˆ์ ์ธ ๊ทœ๊ฒฉ ์„ค์ • Facilitator Notes: Cpk and Ppk can be used to: measure continual improvement using trends over time prioritize the order in which processes will be improved The numerous situations and problems we face are unequal in importance. Our problem-solving and process improvement resources are not unlimited, so we have to focus on the most significant needs. A definite basis is required for any such selection. Understanding the meaning of capability information can help us better select which processes can and should be improved first. If the actual process evaluation measurements do not compare well with the stated specifications, but customer satisfaction is not impaired by the process output, it may be worth revisiting the specifications themselves.

๊ณต์ •ํ‰๊ฐ€์ง€์ˆ˜ ์ ์šฉ ๊ณ ๋ ค์‚ฌํ•ญ ๊ณต์ •์„ ์„ค๋ช…ํ•˜๊ธฐ ์œ„ํ•ด ๊ณต์ •๋Šฅ๋ ฅ์ง€์ˆ˜ ๋˜๋Š” ๊ณต์ •๋Šฅ๋ ฅ๋น„ ํ•˜๋‚˜๋ฅผ ๋‹จ๋…์œผ๋กœ ์‚ฌ์šฉํ•˜์ง€ ์•Š๋Š”๋‹ค. ๊ณต์ •์„ ์™„์ „ํžˆ ์ดํ•ดํ•˜๊ธฐ ์œ„ํ•ด ๋‘ ๊ฐœ ์ด์ƒ์˜ ๊ณต์ •๋Šฅ๋ ฅ ํ‰๊ฐ€์ง€์ˆ˜๋ฅผ ์‚ฌ์šฉํ•œ๋‹ค. ๊ด€๋ฆฌ๋„, ๋ถ„ํฌ๋„, ๋“ฑ์˜ ๋„ํ‘œ์™€ ํ•จ๊ป˜ ๊ณต์ •๋Šฅ๋ ฅ์„ ๋ถ„์„ํ•œ๋‹ค ๊ณต์ •์˜ ์†Œ๋ฆฌ(VOP)๋ฅผ ๊ณ ๊ฐ์˜ ์†Œ๋ฆฌ(VOC)์— ์ผ์น˜์‹œํ‚ค๋ ค ๋…ธ๋ ฅํ•œ๋‹ค. Facilitator Notes: Note how the average and range chart is used: first, to judge statistical stability and therefore what measures can be calculated, and what judgements made from the results second, if the process is shown to be statistically stable, as the source for -- the estimate of s for CR, Cp, and Cpk - R/d2

๊ณต์ •๋Šฅ๋ ฅ์ง€์ˆ˜์˜ ์—ฐ๊ด€๋œ ์‚ฌ์šฉ 8 12 16 14 10 11 13 9 7 15 ์™œ ์šฐ๋ฆฌ๋Š” Cpk์™€ ํ•จ๊ป˜ Cp๋ฅผ ๊ณ„์† ์‚ฌ์šฉํ•˜๋Š”๊ฐ€? USL LSL Target 8 12 16 14 10 11 13 9 7 15 ๊ตฐ์˜ ํฌ๊ธฐ : 5 Facilitator Notes: Cp = the potential of a process and Cpk = where the process actually is. The difference between Cpk and Cp represents the potential gain to be had from centering the process. Cp can be 1 or more and the process may be producing 100% unacceptable product. On the other hand, Cpk by itself may not show the potential results of process centering. In this example, (which gives some of the measurement answers to Exercise Two on page 49) the capability ratio (CR) of .75 is equivalent to the capability index of 1.33. These values indicate that the width of the 6s process dispersion is only 3/4ths that of the specification width; or alternatively that the specification spread is a third again as wide as the process spread. Against a maximum acceptable value of 1 for CR and a minimum acceptable value of 1 for Cp, from this standpoint, this process has very good potential. On the other hand, the Cpk value shows that the combination of process dispersion and centering is such that some of the process lies beyond a tolerance limit. Cpk like Cp requires a minimum value of 1 to be considered โ€œcapableโ€ of providing mostly acceptable product. Here we see that a โ€œtailโ€ of the process carries beyond the lower spec limit; the smaller CPL value also indicates this fact. Since the Cp and CR values are very good, the best course of action would be to attempt to center this process. ์™œ ์šฐ๋ฆฌ๋Š” Cpk์™€ ํ•จ๊ป˜ Cp๋ฅผ ๊ณ„์† ์‚ฌ์šฉํ•˜๋Š”๊ฐ€?

๊ณต์ •๋Šฅ๋ ฅ์ง€์ˆ˜์™€ ๊ณต์ •์‹ค์ ์ง€์ˆ˜์˜ ๋น„๊ต โ€œ๊ณต์ •๋Šฅ๋ ฅ์ง€์ˆ˜๋Š” ์˜ˆ์ธก์„ ์œ„ํ•œ ์ง€์ˆ˜๋กœ ์‚ฌ์šฉํ•˜๊ณ , ๊ณต์ •์‹ค์ ์ง€์ˆ˜๋Š” ๊ณผ๊ฑฐ์˜ ์‹ค์ (์„ฑ๊ณผ)์„ ์ธก์ •ํ•˜๋Š” ์ง€์ˆ˜๋กœ ์‚ฌ์šฉ๋œ๋‹ค.โ€ ์ƒ๊ธฐ ๋ฌธ๊ตฌ๋Š” ๋ฌด์—‡์„ ์˜๋ฏธํ•˜๋Š”๊ฐ€? ๋™์ผ๊ณต์ •์— ๋Œ€ํ•œ Cpk ์™€ Ppk ์ง€์ˆ˜์— ํฐ ์ฐจ์ด๊ฐ€ ์žˆ๋‹ค๋ฉด ์ค‘์š” ์ด์œ ๋Š” ๋ฌด์—‡์ธ๊ฐ€? ์–ด๋–ค ์กฐ์น˜๊ฐ€ ํ•„์š”ํ•œ๊ฐ€? 1) ๊ณต์ •๋Šฅ๋ ฅ์ง€์ˆ˜๋Š” ๊ณต์ •์˜ ์šฐ์—ฐ์›์ธ์„ ๋ฐ˜์˜ํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ์ด์ƒ์›์ธ์— ์˜ํ•ด ๋ณ€ํ™”๊ฐ€ ์ผ์–ด๋‚˜์ง€ ์•Š๋Š” ํ•œ ๊ณต์ •์˜ ์˜ˆ์ƒ๋˜๋Š” ์ˆ˜ํ–‰๋Šฅ๋ ฅ์„ ๋‚˜ํƒ€๋‚ผ ์ˆ˜ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ๊ณต์ •๋Šฅ๋ ฅ์ง€์ˆ˜๋Š” ์˜ˆ์ธก์„ ์œ„ํ•ด ์‚ฌ์šฉ๋œ๋‹ค.๋ฐ˜๋ฉด์— ๊ณต์ •์‹ค์ ์ง€์ˆ˜๋Š” ๋•Œ๋ฌธ์— ์ฃผ์–ด์ง„ ์‹œ๊ฐ„ ๋™์•ˆ ์šฐ์—ฐ์›์ธ๊ณผ ์ด์ƒ์›์ธ์— ์˜ํ•œ ๊ณต์ •์˜ ์ด๋ณ€๋™์„ ๋‚˜ํƒ€๋‚ธ๋‹ค. ๊ณต์ •์‹ค์ ์ง€์ˆ˜๋Š” ์ด์ƒ์›์ธ์— ์˜ํ•ด ๊ณต์ •์ด ๋ถˆ์•ˆ์ •ํ•˜๋ฏ€๋กœ ๋ฏธ๋ž˜์˜ ์ˆ˜ํ–‰๋Šฅ๋ ฅ์„ ์˜ˆ์ธกํ•  ์ˆ˜ ์—†๋‹ค, ๊ณต์ •์‹ค์ ์ง€์ˆ˜๋Š” ๊ณต์ •์˜ ์‹œ๊ฐ„์— ํ๋ฆ„์— ๋”ฐ๋ฅธ ์‹ค์ ์„ ์ถฉ์‹คํžˆ ๋ฐ˜์˜ํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ๊ณผ๊ฑฐ์˜ ๊ณต์ •์ด๋ ฅ(๊ณต์ •์‹ค์ )์„ ํ†ตํ•ด ๊ณผ๊ฑฐ ๊ณต์ •์ด ์ž˜ ๊ด€๋ฆฌ๋˜์—ˆ๋Š”์ง€, ๋˜๋Š” ์•ˆ์ •์ ์ด์—ˆ๋Š”์ง€ ํŒ๋‹จํ•˜๋Š”๋ฐ ์œ ์šฉํ•˜๋‹ค. 2) Cpk ์™€ Ppk ์ง€์ˆ˜์˜ ๊ฐ€์žฅ ์ค‘์š”ํ•œ ์ฐจ์ด๋Š” ์ด์ƒ์›์ธ์— ์˜ํ•œ ๋ณ€๋™์ด ์œ ์˜์„ฑ์ด๋‹ค. 3) ์ด์ƒ์›์ธ์„ ์ œ๊ฑฐํ•˜๋Š” ๊ฒƒ์€ Ppk ์ง€์ˆ˜๋ฅผ ํ–ฅ์ƒ ์‹œํ‚ค๊ณ  Ppk ์™€ Cpk ๊ฐ„์˜ ์ฐจ์ด๋ฅผ ์ค„๊ฒŒ ํ•œ๋‹ค. ์ด๊ฒƒ์€ ๊ณต์ •์˜ ์‚ฐํฌ๋ฅผ ๊ฐœ์„ ํ•œ๋‹ค๋Š” ์˜๋ฏธ์ด๋ฉฐ, ๊ณต์ • ์ค‘์‹ฌ์œ„์น˜์˜ ๊ฐœ์„ ์„ ์˜๋ฏธํ•œ๋‹ค. Cpk ์ง€์ˆ˜๊ฐ€ ํ•ฉ๊ฒฉ์ด๋‚˜ Ppk ์ง€์ˆ˜๊ฐ€ ๋ถˆํ•ฉ๊ฒฉ์ธ ๊ฒฝ์šฐ, ์ด๊ฒƒ์€ ๋งค์šฐ ์ค‘์š”ํ•˜๋‹ค.

๋‹จ๊ธฐ ๊ณต์ •๋ณ€๋™ ๋Œ€ ์žฅ๊ธฐ ๊ณต์ •๋ณ€๋™ รฅ ( ) x s - Cpk = ์—ฐ์† ๋ฐ์ดํ„ฐ Ppk = ๋žœ๋คํ•˜๊ฒŒ ์ˆ˜์ง‘๋œ ๋ฐ์ดํ„ฐ x x = n i x s 1 2 ์ œ์กฐ๊ณต์ • ๋‚ด์— ๋‹จ๊ธฐ ๋ณ€๋™์€ ์ž์žฌ์˜ ๋กœํŠธ๋ณ„ ๋ณ€๋™ ๋ฐ ์ž‘์—…์ž๊ฐ„ ๋ณ€๋™ ๋“ฑ์„ ํฌํ•จํ•˜์ง€ ์•Š๋Š”๋‹ค. ์žฅ๊ธฐ๋ณ€๋™์š”์ธ์€ ์ƒํ™ฉ์— ๋”ฐ๋ฅธ ์ด์ƒ์›์ธ๋“ค์„ ๊ณ ๋ คํ•  ์ˆ˜ ์žˆ๋‹ค ๊ณต์ •๋Šฅ๋ ฅ์ง€์ˆ˜ Cp ๋ฐ Cpk ๋Š” ๋‹จ๊ธฐ ๊ณต์ •๋Šฅ๋ ฅ ์ง€์ˆ˜๋กœ ์‚ฌ์šฉ๋˜๊ณ , ๊ณต์ •์‹ค์ ์ง€์ˆ˜๋Š” Pp and Ppk ๋Š” ์žฅ๊ธฐ๊ณต์ •๋А๋ ฅ์ง€์ˆ˜๋กœ ์‚ฌ์šฉ๋œ๋‹ค. Cpk = ์—ฐ์† ๋ฐ์ดํ„ฐ Ppk = ๋žœ๋คํ•˜๊ฒŒ ์ˆ˜์ง‘๋œ ๋ฐ์ดํ„ฐ

๊ทœ๊ฒฉ๋ชฉํ‘œ์— ๊ณต์ • ์ค‘์‹ฌ ์ผ์น˜ํ™” ๊ณ ๊ฐ ์†์‹ค์„ ์ตœ์†Œํ™”ํ•˜๊ธฐ ์œ„ํ•ด, ๊ณต์ •์ค‘์‹ฌ์„ ๊ทœ๊ฒฉ์˜ ๋ชฉํ‘œ๊ฐ’์— ๋งž์ถ”์–ด์•ผ ํ•œ๋‹ค. ํ•ต์‹ฌ์ œํ’ˆํŠน์„ฑ(KPCs) ๋ฐ KPCs์™€ ๊ด€๋ จ๋œ ํ•ต์‹ฌ๊ณต์ •ํŠน์„ฑ(KCCs)์„ ๊ด€๋ฆฌํ•˜๊ธฐ ์œ„ํ•œ ํ‘œ์ค€ํ™”๋œ ์ ˆ์ฐจ(KCDS) ๊ฐ€ ์žˆ๋‹ค. Facilitator Notes: We have seen that process evaluation measures compare the actual performance of a manufacturing process to the desired performance, in terms of the distribution of a โ€œQuality Characteristicโ€, which is a critical dimension or other feature of the product or an important process variable which affects the quality of the product. The Key Characteristics Designation System (KCDS) helps identify, communicate, control, and monitor Key Product Characteristics (specially selected product specifications) and their associated Key Control Characteristics (the KPCsโ€™ related process variables). One way of judging whether a characteristic is a potential KPC is to ask whether reducing its variation around a target value would result in marked improvement in customer satisfaction. Capability measures such as CR and Cpk help achieve control over these โ€œQuality Characteristicsโ€. Generally, capability measures are required only for KPCs and may be useful for some of their KCCs.

๊ธฐ๋Šฅ ์ ๊ฒ€, ๊ฒ€์ฆ/์ถ”์ ์„ฑ, ์•ˆ์ „/๋ฒ•๊ทœ ๋ฐ ๋ถ€ํ’ˆ ์ทจ๊ธ‰๊ด€๋ฆฌ KCDS โ€œํ”ผ๋ผ๋ฏธ๋“œโ€ ํŠน๋ณ„ ๋ถ€๊ฐ€ ์ผ๋ฐ˜ ์ œํ’ˆํ’ˆ์งˆํŠน์„ฑ (PQC) ๊ธฐ๋Šฅ ์ ๊ฒ€, ๊ฒ€์ฆ/์ถ”์ ์„ฑ, ์•ˆ์ „/๋ฒ•๊ทœ ๋ฐ ๋ถ€ํ’ˆ ์ทจ๊ธ‰๊ด€๋ฆฌ ๋ชจ๋“  ์ œํ’ˆ ๋ฐ ๊ณต์ • ๊ด€๋ฆฌ ์ˆ˜์ค€ ํ•ต์‹ฌ์ œํ’ˆ ํŠน์„ฑ(KPC) ํ•ต์‹ฌ ์ œํ’ˆ ํŠน์„ฑ์— ๋Œ€ํ•ด ํ•„์š”ํ•œ ํŠน๋ณ„ ๊ด€๋ฆฌ์˜ 2๊ฐ€์ง€ ์ˆ˜์ค€ ์ผ๋ถ€ ๋ถ€ํ’ˆ์— ๋Œ€ํ•ด ํ•„์š”ํ•œ ๋ถ€๊ฐ€์ ์ธ ๊ด€๋ฆฌ ์—…๋ฌด/ํ™œ๋™ ์ œํ’ˆ ๋ฐ ๊ณต์ •์— ๋Œ€ํ•œ ์ผ๋ฐ˜(์ผ์ƒ ๋ฐ ํ†ต์ƒ) ๊ด€๋ฆฌ

GM KCDS ํ”„๋กœ์„ธ์Šค๋Š” ๋ถ€ํ’ˆ ๋ฐ ๋ถ€ํ’ˆ ํŠน์„ฑ ์š”๊ตฌ์‚ฌํ•ญ์— ์ดˆ์ ์„ ๋งž์ถ”๊ณ  ์žˆ๋‹ค. ๋ถ€ํ’ˆ ์ทจ๊ธ‰๊ด€๋ฆฌ ๊ธฐ๋Šฅ ์ ๊ฒ€ ๋ถ€๊ฐ€์ ์ธ ๊ด€๋ฆฌ๊ฐ€ ์š”๊ตฌ๋˜๋Š” ๋ถ€ํ’ˆ ์ถ”์ ์„ฑ ๋ถ€ํ’ˆ ์ˆ˜์ค€ (๊ณ„์ˆ˜์น˜) ์•ˆ์ „/๋ฒ•๊ทœ ์ผ๋ฐ˜ ๊ด€๋ฆฌ๊ฐ€ ์š”๊ตฌ๋˜๋Š” ๋ถ€ํ’ˆ ๋ชจ๋“  ์ œํ’ˆ ๋ฐ ๊ณต์ • ํ•ต์‹ฌ์ œํ’ˆ ํŠน์„ฑ(KPC) ํŠน๋ณ„๊ด€๋ฆฌ ํŠน์„ฑ(KCC) ํŠน๋ณ„ ํŠน์„ฑ ํŠน๋ณ„ ๊ด€๋ฆฌ ํŠน์„ฑ ์ˆ˜์ค€ (๊ณ„๋Ÿ‰์น˜) ์ œํ’ˆํ’ˆ์งˆ ํŠน์„ฑ(PQC) ํŠน๋ณ„๊ด€๋ฆฌ ํŠน์„ฑ(KCC) ์ผ๋ฐ˜ ํŠน์„ฑ ์ผ๋ฐ˜ ๊ด€๋ฆฌ

KPC ๋Š” ํ†ต๊ณ„์  ๊ด€๋ฆฌ์ƒํƒœ์™€ ์•ˆ์ •์ƒํƒœ๋ฅผ ๋ณด์žฅํ•ด์•ผ ํ•œ๋‹ค

PQC โ€“๋ชฉํ‘œ์— ์ผ์น˜ํ•จ์„ ๋ณด์žฅํ•ด์•ผ ํ•œ๋‹ค.

Variation control (PQC) Variation reduction (KPC) KPC/PQC โ€“ SPC๋ฅผ ์ ์šฉํ•ด์•ผ ํ•œ๋‹ค. Variation control (PQC) Variation reduction (KPC) Standard Care PQC KPC

๊ณต์ •ํ‰๊ฐ€ ๊ฒฐ๊ณผ์— ๋Œ€ํ•œ ๋Œ€์‘์กฐ์น˜ ๊ณต์ •ํ‰๊ฐ€ ๊ฒฐ๊ณผ๊ฐ€ ๋ฐ”๋žŒ์ง์Šค๋Ÿฝ์ง€ ์•Š๋‹ค๋ฉด, ๋‹ค์Œ์„ ๊ณ ๋ คํ•œ๋‹ค: ํ•˜๋‚˜ ์ด์ƒ์˜ ์ด์ƒ์›์ธ์ด ์กด์žฌํ•˜๋Š” ๊ฒฝ์šฐ, ๋ชจ๋“  ์ด์ƒ์›์ธ์„ ์ œ๊ฑฐํ•˜๊ณ  ๋‹ค์‹œ ๊ณต์ •์„ ํ‰๊ฐ€ํ•œ๋‹ค. ๊ณต์ • ์ค‘์‹ฌ์œ„์น˜๋ฅผ ๊ทœ๊ฒฉ๋ชฉํ‘œ์— ๋งž์ถ˜๋‹ค ๊ทœ๊ฒฉ์ด ๋ณ€๊ฒฝ๋˜์–ด์•ผ ํ•˜๋Š”์ง€ ๊ฒ€ํ† ํ•œ๋‹ค. ๊ณต์ • ์‚ฐํฌ๋ฅผ ์ค„์ธ๋‹ค. ๊ณต์ •์„ ๋ณ€ํ™”(๊ฐœ์„ ) ์‹œํ‚จ๋‹ค. ๊ณต์ •์„ ๋” ์ž˜ ๊ด€๋ฆฌํ•˜๋„๋ก ํ•œ๋‹ค. Facilitator Notes: A combination of these tactics may be required when the results of a process evaluation are unacceptable. If the process is not stable to begin with, the first response should be to try to identify and remove the special causes which are the source of the instability. Centering an incapable process will help minimize the proportion that is out-of-specification. Sometimes it helps to know whether product out of specification in one direction is scrap, and out of spec in the other direction is only rework. If the process dispersion is less than the specification width, but the process is poorly centered, centering it may make the results acceptable, even if the process should โ€œstrayโ€ a bit later on. Occasionally the specifications may be changed without harm to customer satisfaction. Reducing process dispersion is generally more difficult than shifting its mean, and may amount essentially to the next option, which is changing the process by changing some major process element (buying a new machine, installing a new measurement system, procuring new raw materials). Deming suggested that if, in the end, you cannot do it right, find someone who can and buy from them. This would be the most extreme choice.

๊ณ„์ˆ˜์น˜ ๋ฐ์ดํ„ฐ์— ๋Œ€ํ•œ ๊ณต์ •๋Šฅ๋ ฅ p ์™€ np ๊ด€๋ฆฌ๋„์—์„œ ๊ณต์ •๋Šฅ๋ ฅ์€ ๊ณต์ •ํ‰๊ท  ๋ถˆ๋Ÿ‰๋ฅ ( )๋กœ ๊ณ„์‚ฐํ•œ๋‹ค. ๋‹จ ๋ชจ๋“  ํƒ€์ ์ด X c = 5 UCL = 11.7 LCL = 0 9 4 4 2 7 0 4 12 4 8 2 6 3 5 2 6 1 8 4 7 p ์™€ np ๊ด€๋ฆฌ๋„์—์„œ ๊ณต์ •๋Šฅ๋ ฅ์€ ๊ณต์ •ํ‰๊ท  ๋ถˆ๋Ÿ‰๋ฅ ( )๋กœ ๊ณ„์‚ฐํ•œ๋‹ค. ๋‹จ ๋ชจ๋“  ํƒ€์ ์ด ๊ด€๋ฆฌ์ƒํƒœ ํ•˜์— ์žˆ์–ด์•ผ ํ•œ๋‹ค. ๊ณต์ •๋Šฅ๋ ฅ์€ ์–‘ํ’ˆ๋ฅ ( )๋กœ๋„ ํ‘œํ˜„๋  ์ˆ˜ ์žˆ๋‹ค. c ๊ด€๋ฆฌ๋„์—์„œ ๊ณต์ •๋Šฅ๋ ฅ์€ ๊ณ ์ •๋œ ์ƒ˜ํ”Œํฌ๊ธฐ n์—์„œ์˜ ํ‰๊ท ๊ฒฐ์ ์ˆ˜( ) ์ด๋‹ค. u ๊ด€๋ฆฌ๋„์—์„œ ๊ณต์ •๋Šฅ๋ ฅ์€ ๋‹จ์œ„๋‹น ํ‰๊ท ๊ฒฐ์ ์ˆ˜ ( ) ์ด๋‹ค. Capability can be applied to attribute data situations. The Automotive Industry Action Group Statistical Process Control Manual provides brief points on this matter. Note that control charts are required to generate attribute capability measures.

์‹ฌํ™” ํ•™์Šต 1.๊ณต์ •๋Šฅ๋ ฅ์ด ๊ณต์ •์˜ ์†Œ๋ฆฌ๋ฅผ ๋Œ€ํ‘œํ•˜๋Š” ๊ฒƒ์€ ๋ฌด์—‡์„ ์˜๋ฏธํ•˜๋Š” ๊ฒƒ์ธ๊ฐ€? 2. ๋‹ค์Œ ์–ด๋А ๊ณต์ •๋Šฅ๋ ฅ ์ธก์ •์ˆ˜๋‹จ์ด ๊ณต์ •์˜ ์‹ค์ œ ์‚ฐํฌ ๋ฐ ์œ„์น˜๋ฅผ ๊ทœ๊ฒฉ๊ณผ ๋น„๊ตํ•˜๋Š”๊ฐ€? A. ๊ณต์ •๋Šฅ๋ ฅ๋น„ C. ๊ณต์ •์‹ค์ ์ง€์ˆ˜ E. ๊ณต์ •๋Šฅ๋ ฅ์ง€์ˆ˜ B. Cpk D. ๊ณต์ •์‹ค์ ๋น„ 3. ๋‹ค์Œ ์ค‘ ํ•œ ๊ฐœ๋ฅผ ์ œ์™ธํ•œ ๋ชจ๋‘๊ฐ€ Cpk ๋ฐ Ppk์˜ ์ฐจ์ด๋ฅผ ์„ค๋ช…ํ•˜๊ณ  ์žˆ๋‹ค. ์–ด๋А ๊ฒƒ์ด ํ‹€๋ฆฐ ๊ฒƒ์ธ๊ฐ€? A. Cpk ๋ฐ Ppk๋Š” ํ‘œ์ค€ํŽธ์ฐจ๋ฅผ ์ถ”์ •ํ•˜๋Š” ๊ณ„์‚ฐ์‹์ด ๋‹ค๋ฅด๋‹ค B. Ppk๋Š” ์ด์ƒ์›์ธ ๋ฐ ์šฐ์—ฐ์›์ธ ๋ชจ๋‘๋ฅผ ํฌํ•จํ•œ ๊ณต์ •์˜ ์ด๋ ฅ์„ ๋‚˜ํƒ€๋‚ด๋Š”๋ฐ ์‚ฌ์šฉํ•˜๋ฉฐ, ๋ฐ˜๋ฉด์— Cpk๋Š” ์šฐ์—ฐ์›์ธ์— ์˜ํ•œ ๊ณต์ •ํŠน์„ฑ์น˜์˜ ๊ฒฐ๊ณผ๋ฅผ ๋‚˜ํƒ€๋‚ธ๋‹ค C. Cpk๋Š” ์ผ๋ฐ˜์ ์œผ๋กœ Ppk ๋ณด๋‹ค ์งง์€ ๊ธฐ๊ฐ„ ์ˆ˜์ง‘๋œ ๋ฐ์ดํ„ฐ๋กœ๋ถ€ํ„ฐ ๊ณ„์‚ฐ๋œ๋‹ค D. Cpk๋Š” ๊ณต์ •์˜ ์œ„์น˜ ๋ฐ ์‚ฐํฌ ๋ชจ๋‘๋ฅผ ๊ทœ๊ฒฉ๊ณผ ๋น„๊ตํ•˜๋Š” ๋ฐ˜๋ฉด, Ppk๋Š” ์‹œ๊ฐ„์— ๋”ฐ๋ฅธ ๊ณต์ •์˜ ์‚ฐํฌ๋งŒ ๊ทœ๊ฒฉ๊ณผ ๋น„๊ตํ•œ๋‹ค. 4. ๊ณต์ •๋Šฅ๋ ฅ๋น„ (CR) ์™€ ๊ณต์ •๋Šฅ๋ ฅ์ง€์ˆ˜ (Cp)์˜ ๊ด€๊ณ„๋Š”?(๋‘๊ฐœ์˜ ๋‹ต์„ ๊ณ ๋ฅด์‹œ์˜ค) A. ์„œ๋กœ ์—ญ์ˆ˜ ๊ด€๊ณ„์ด๋‹ค B. ๊ณต์ •๋Šฅ๋ ฅ๋น„ X 2 = ๊ณต์ •๋Šฅ๋ ฅ์ง€์ˆ˜ C. ๊ณต์ •๋Šฅ๋ ฅ๋น„๋Š” ์šฐ์—ฐ์›์ธ์— ์˜ํ•œ ๋ณ€๋™์„ ๋‚˜ํƒ€๋‚ด๊ณ , ๊ณต์ •๋Šฅ๋ ฅ์ง€์ˆ˜๋Š” ์ด์ƒ์›์ธ์— ์˜ํ•œ ๋ณ€๋™์„ ๋‚˜ํƒ€๋‚ธ๋‹ค D. ์ด ๋‘๊ฐ€์ง€ ๋ชจ๋‘ ๊ณต์ •์ค‘์‹ฌ์˜ ์œ„์น˜์™€๋Š” ๊ด€๋ จ์ด ์—†๋‹ค. E. ์ด ๋‘๊ฐ€์ง€ ๋ชจ๋‘ ๊ทœ๊ฒฉ(๊ณต์ฐจ์˜ ํญ)๊ณผ๋Š” ๊ด€๋ จ์ด ์—†๋‹ค Answer ๊ณต์ •๋Šฅ๋ ฅ์€ ๊ทœ๊ฒฉ๊ณผ ๊ด€๋ จ๋œ ๊ฒƒ์ด์ง€๋งŒ, ๊ณต์ •์ด ์‹ค์ œ ์šด์˜์ƒํƒœ๋ฅผ ๋‚˜ํƒ€๋‚ด๋Š” ์ฒ™๋„์ด๋‹ค b. 3. d. 4. a. d.

์‹ฌํ™” ํ•™์Šต 5. ๊ฐ€์žฅ ์ข‹์€ Cpk ๊ฒฐ๊ณผ๋Š”? 6. ๋‹ค์Œ ์ค‘ ์–ด๋А ๊ฒฝ์šฐ์— ๊ณต์ •์˜ ๊ฐœ์„ ์„ ๊ณ ๋ คํ•ด์•ผ ํ•˜๋Š”๊ฐ€? A. ๊ฐ€๋Šฅํ•œ 1์— ๊ทผ์ ‘ํ•œ ๊ฐ’ D. ๊ฐ€๋Šฅํ•œ 1๋ณด๋‹ค ํฐ ๊ฐ’ B. 0 ๊ณผ 1 ์‚ฌ์ด ๊ฐ’ E. ๊ณต์ •์ด ์•ˆ์ •๋˜์–ด ์žˆ๋Š” ํ•œ ๊ฒฐ๊ณผ๊ฐ’์€ ์ƒ๊ด€ ์—†๋‹ค C. 1๋ณด๋‹ค ์ ์€ ๊ฐ’ (์Œ์ˆ˜) 6. ๋‹ค์Œ ์ค‘ ์–ด๋А ๊ฒฝ์šฐ์— ๊ณต์ •์˜ ๊ฐœ์„ ์„ ๊ณ ๋ คํ•ด์•ผ ํ•˜๋Š”๊ฐ€? A. ๊ณต์ •์ด SPC์„ ํ†ตํ•ด ๊ด€๋ฆฌ๋˜๊ณ  ์žˆ๋Š” KPC ํŠน์„ฑ์œผ๋กœ ๊ณต์ • ์‚ฐํฌ๋ฅผ ๋” ํšจ๊ณผ์ ์œผ๋กœ ๊ฐ์†Œ ์‹œํ‚ฌ ์ˆ˜ ์—†๋Š” ๊ฒฝ์šฐ B. ๊ณต์ •์˜ Cpk๋Š” ๋‚˜์˜์ง€๋งŒ ์•ˆ์ •์ƒํƒœ์ด๋‚˜, ์ข‹์€ Cpk์—์„œ๋Š” ๋ถˆ์•ˆ์ • ํ•œ ๊ฒฝ์šฐ C. ๊ณต์ •์€ ์ค‘์š”ํ•˜์ง€ ์•Š๊ณ  Cpk๋„ ๋‚˜์˜์ง€๋งŒ, ์ข‹์€ Cpk์˜ ๊ณต์ •์—์„œ๋„ ๊ทœ๊ฒฉ์„ ๋งŒ์กฑ์‹œํ‚ค์ง€ ๋ชปํ•˜๋Š” ๊ฒฝ์šฐ D. ๋‚˜์œ Cpk ์˜ ๊ณต์ •์ด ์ข€ ๋” ์ข‹์€ Cpk์˜ ๊ณต์ • ๋ณด๋‹ค ๊ณต์ •์ค‘์‹ฌ์ด ๊ทœ๊ฒฉ ์ค‘์‹ฌ์— ๋” ๊ทผ์ ‘ํ•  ์ˆ˜ ์žˆ๋Š” ๊ฒฝ์šฐ 7.์™œ ๊ณต์ •์˜ ์•ˆ์ •์„ฑ์ด ๊ณต์ •๋Šฅ๋ ฅ ์ธก์ •์— ์ค‘์š”ํ•œ๊ฐ€? A. ๊ณต์ •์ด ์•ˆ์ •์ƒํƒœ๊ฐ€ ์•„๋‹ˆ๋ฉด, ๊ณต์ •๋Šฅ๋ ฅ์€ ๊ฐœ๋ณ„ ์ธก์ •๊ฐ’์œผ๋กœ๋งŒ ์œ ํšจํ•˜๋‹ค. B. ์•ˆ์ •๋œ ๊ณต์ •์€ ๋˜ํ•œ ๊ณต์ •๋Šฅ๋ ฅ๋„ ์ข‹๋‹ค. C. ๊ณต์ •์ด ์•ˆ์ •์ƒํƒœ์ด์–ด์•ผ ๊ฐœ์„ ํ•  ์ˆ˜ ์žˆ๋‹ค D. ์•ˆ์ •๋œ ๊ณต์ •์€ ๋˜ํ•œ ๊ณต์ •์ค‘์‹ฌ์ด ๊ทœ๊ฒฉ์ค‘์‹ฌ์— ์ผ์น˜ํ•  ๊ฒƒ์ด๋‹ค E. ์‹ค์ œ๋กœ, ๋ถˆ์•ˆ์ • ๊ณต์ •์˜ ๊ณต์ •๋Šฅ๋ ฅ ์ธก์ •๊ฐ’์€ ์•ˆ์ •๋œ ๊ณต์ •์˜ ๊ณต์ •๋Šฅ๋ ฅ ์ธก์ •๊ฐ’๊ณผ ๋น„์Šทํ•˜๋‹ค. Answer Key 5. d. d. Loss function ๊ฐœ๋…์„ ๋„์ž…ํ•˜์—ฌ ๋‹ต์€ d 7. A.

(Measurement system Analysis] SPC Statistical Process Control MSA (Measurement system Analysis] ์ธก์ •์‹œ์Šคํ…œ ๊ฐœ์š” ์ธก์ •์‹œ์Šคํ…œ ๋ณ€๋™ ์ธก์ •์‹œ์Šคํ…œ ํ‰๊ฐ€์ ˆ์ฐจ ๊ณ„๋Ÿ‰ํ˜• ์ธก์ •์‹œ์Šคํ…œ ํ‰๊ฐ€ ๊ณ„์ˆ˜ํ˜• ์ธก์ •์‹œ์Šคํ…œ ํ‰๊ฐ€ 6. Best Practice 7. GRR ์‹ค์Šต

1. ์ธก์ •์‹œ์Šคํ…œ ๊ฐœ์š” ์ธก์ •์‹œ์Šคํ…œ ์šฉ์–ด ์ •์˜ ์ธก์ •์‹œ์Šคํ…œ ๊ธฐ๋ณธ ์š”๊ตฌํŠน์„ฑ ์ธก์ •์‹œ์Šคํ…œ ํ‰๊ฐ€๋ž€? ์ธก์ •์‹œ์Šคํ…œ ํ‰๊ฐ€๋ชฉ์  ์ธก์ • Process ์ธก์ •์‹œ์Šคํ…œ ๋ณ€๋™์ธ์ž ์ธก์ •์‹œ์Šคํ…œ ๋ณ€๋™์˜ํ–ฅ Facilitator says These seven topic areas will be the focus during this course: The course must begin with some basic definitions and history. There will be discussion on ways to measure, interpret, and control variation. Participants will construct and use the various tools in the Quality Control โ€œToolboxโ€. Since processes are subject to change, the need to track their behavior through time will be emphasized. Time will be spent making and interpreting control charts. Participants will practice evaluating processes. Participants will learn that knowing how โ€œgoodโ€ measurement systems are is vital before making judgements based upon the measurements taken.

์ธก์ •์‹œ์Šคํ…œ ์šฉ์–ด์ •์˜ ์ธก์ •(Measurement) : ํŠน์ • ์†์„ฑ๋“ค์— ๊ด€๋ จํ•˜์—ฌ ์‚ฌ๋ฌผ์˜ ๊ด€๊ณ„๋ฅผ ๋‚˜ํƒ€๋‚ด๊ธฐ ์œ„ํ•ด ์ˆซ์ž(๊ฐ’)์„ ๋ถ€์—ฌํ•˜๋Š” ๊ฒƒ. (์ˆซ์ž๋ฅผ ๋ถ€์—ฌํ•˜๋Š” ๊ณผ์ •์„ ์ธก์ •Process, ๋ถ€์—ฌ๋œ ๊ฐ’์€ ์ธก์ •๊ฐ’) ๊ฒŒ์ด์ง€(Gage) : ์ธก์ •์น˜๋ฅผ ์–ป๊ธฐ ์œ„ํ•ด ์‚ฌ์šฉ๋˜๋Š” ๊ธฐ๊ตฌ ex)๊ณ ๋…ธ ๊ฒŒ์ด์ง€ ๋“ฑ ์ธก์ •์‹œ์Šคํ…œ(Measurement System) : ์ธก์ •ํ•  ํŠน์„ฑ์— ์ˆ˜์น˜๋ฅผ ๋ถ€์—ฌํ•˜๋Š”๋ฐ ์ด์šฉ๋˜๋Š” ์ž‘์—…, ์ ˆ์ฐจ, ๊ฒŒ์ด์ง€, ์žฅ๋น„, ์†Œํ”„ํŠธ ์›จ์–ด ๋ฐ ํ‰๊ฐ€์ž์˜ ์ง‘ํ•ฉ, ์ธก์ •๊ฐ’์„ ์–ป๊ธฐ ์œ„ํ•ด ์‚ฌ์šฉ ๋˜๋Š” ์ „์ฒด ๊ณผ์ •. ํŒ๋ณ„๋ ฅ(Discrimination) : ์ธก์ •๋˜๋Š” ํŠน์„ฑ์น˜์˜ ๋ฏธ์„ธํ•œ ๋ณ€ํ™”๋„ ํƒ์ง€ํ•˜์—ฌ ์ด๋ฅผ ๋‚˜ํƒ€๋‚ผ ์ˆ˜ ์žˆ๋Š” ๊ณ„์ธก๊ธฐ์˜ ๋Šฅ๋ ฅ์œผ๋กœ ํ•ด์ƒ๋„ (Scale ๋˜๋Š” Resolution)๋ผ๊ณ ๋„ ํ•œ๋‹ค. ํŒ๋ณ„๋ ฅ ์˜ ์ฒ™๋„๋Š” ์ผ๋ฐ˜์ ์œผ๋กœ ์ธก์ •๊ธฐ ๋ˆˆ๊ธˆ์˜ ์ตœ์†Œ๋‹จ์œ„์˜ ๊ฐ’์œผ๋กœ ํ•œ๋‹ค. ์ธก์ •๊ธฐ์˜ ๋ˆˆ๊ธˆ์ด ์„ธ๋ฐ€(coarse)ํ•˜์ง€ ์•Š๋‹ค๋ฉด ๊ทธ๋•Œ๋Š” ๋ฐ˜๋ˆˆ๊ธˆ(half-graduation)์ด ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ๋‹ค ์ผ๋ฐ˜์ ์ธ ๊ฒฝํ—˜๋ฒ•์น™์€ ์ธก์ •๊ธฐ์˜ ํŒ๋ณ„๋ ฅ ์€ ์ ์–ด๋„ ์ธก์ •๋ฒ”์œ„์˜ ์‹ญ๋ถ„์˜ ์ผ๊นŒ์ง€ ์ธก์ •ํ•  ์ˆ˜ ์žˆ์–ด์•ผ ํ•œ๋‹ค. Full interval Half interval

์ธก์ •์‹œ์Šคํ…œ ์šฉ์–ด์ •์˜-๊ณ„์† ํ‘œ์ค€(Standards) - ๋น„๊ต๋ฅผ ์œ„ํ•ด ์ˆ˜์šฉ๋œ ๊ธฐ์ค€, ํ•ฉ๊ฒฉ ๊ธฐ์ค€, ๊ธฐ์ค€ ๊ฐ’ ์ฐธ๊ฐ’(True Value) : ํŠน์ • ๊ฐ€๊ณตํ’ˆ์˜ ์‹ค์ œ ๊ฐ’์œผ๋กœ ํ™•์‹คํ•˜๊ฒŒ ์•Œ ์ˆ˜ ์—†๋Š” ๊ฐ’ ๊ธฐ์ค€๊ฐ’(Reference Value) : ์ฐธ๊ฐ’ ๋Œ€์‹  ์‚ฌ์šฉํ•˜๋Š” ํŠน์ • ๊ฐ€๊ณตํ’ˆ์˜ ์ธ์ •๋œ ๊ฐ’ ๋ถˆํ™•๋„(Uncertainty) : ์ฐธ๊ฐ’์ด ํฌํ•จ๋œ๋‹ค๊ณ  ์ƒ๊ฐ๋˜๋Š” ์ธก์ •๊ฐ’์˜ ์ถ”์ • ๋ฒ”์œ„ ์ธก์ •์ˆ˜๋ช…์ฃผ๊ธฐ(Measurement Life Cycle) : ๊ณต์ •์— ๋Œ€ํ•œ ์ดํ•ด ์ฆ๊ฐ€ ๋ฐ ๊ฐœ์„ ์— ๋”ฐ๋ผ ์ธก์ • ๋ฐฉ๋ฒ•์ด ์‹œ๊ฐ„์— ๋”ฐ๋ผ ๋ณ€ํ™”๋˜๋Š” ๊ฒƒ์„ ์˜๋ฏธํ•œ๋‹ค. (ex : Torque ๋ชจ๋‹ˆํ„ฐ๋ง) ์˜ˆ : ์ƒ˜ํ”Œ๋ง ๊ณ„ํš๊ฐ์†Œ(์‹œ๊ฐ„๋‹น5๋ฒˆ ์—์„œ ๊ต๋Œ€๋‹น ํ•œ๋ฒˆ์œผ๋กœ) ์ธก์ •ํ”„๋กœ์„ธ์Šค์˜ ๋ชฉํ‘œ๋Š” ๋ถ€ํ’ˆ์˜ ์ฐธ๊ฐ’ ์ด๋‹ค. ์–ด๋–ค ์ธก์ •๊ฐ’์ด๋ผ๋„ ๊ฐ€๋Šฅํ•˜๋ฉด (๊ฒฝ์ œ์ ์œผ๋กœ) ์ฐธ๊ฐ’์— ๊ทผ์ ‘ํ•œ ๊ฒƒ์ด ๋ฐ”๋žŒ์งํ•˜๋‚˜ ์ฐธ๊ฐ’์€ ๊ฒฐ์ฝ” ํ™•์‹คํ•˜๊ฒŒ ์•Œ ์ˆ˜ ์—†์œผ๋‚˜ ๋ถˆํ™•๋„๋Š” ํŠน์„ฑ์˜ ์ž˜์ •์˜๋œ ์šด์šฉ์ •์˜ ์— ๋ฐ”ํƒ•์„ ๋‘” ๊ธฐ์ค€๊ฐ’์„ ์‚ฌ์šฉํ•˜๊ฑฐ๋‚˜ NIST(๋ฏธ๊ตญํ‘œ์ค€๊ธฐ์ˆ ์›)๋กœ์˜ ์†Œ๊ธ‰์„ฑ์ด ์žˆ๋Š” ๋†’์€ ์ฐจ์›์˜ ํŒ๋ณ„๋ ฅ์„ ๊ฐ–์ถ˜ ์ธก์ •์‹œ์Šคํ…œ์˜ ๊ฒฐ๊ณผ๋ฅผ ์‚ฌ์šฉํ•จ ์œผ๋กœ์„œ ์ตœ์†Œํ™” ํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ธฐ์ค€๊ฐ’ ์€ ์ฐธ๊ฐ’ ๋Œ€์‹  ์‚ฌ์šฉ๋˜๊ธฐ ๋•Œ๋ฌธ์— ์ด๋“ค ์šฉ์–ด๋Š” ๋ณดํ†ต ํ˜ผ์šฉ๋˜๋Š”๋ฐ ์ด๊ฒƒ์€ ๊ถŒ์žฅํ•˜์ง€ ์•Š๋Š”๋‹ค.

์ธก์ •์‹œ์Šคํ…œ ์šฉ์–ด์ •์˜-๊ณ„์† ์ธก์ •์˜ ์†Œ๊ธ‰์„ฑ (๊ตญ๊ฐ€ ํ‘œ์ค€๊ธฐ) (๋น„๊ต ํ‘œ์ค€๊ธฐ) (ํ˜„์žฅ ํ‘œ์ค€๊ธฐ) (์ƒ์‚ฐ ๊ฒŒ์ด์ง€) ๊ด‘ ํŒŒ ํ‘œ์ค€๊ธฐ ๊ฐ„์„ญ ๋น„๊ต๊ธฐ (๊ตญ๊ฐ€ ํ‘œ์ค€๊ธฐ) ๋ ˆ์ด์ € ์ธก์ •๊ธฐ (๋น„๊ต ํ‘œ์ค€๊ธฐ) ์ขŒํ‘œ ์ธก์ •๊ธฐ (ํ˜„์žฅ ํ‘œ์ค€๊ธฐ) ๊ณ ์ • ๊ฒŒ์ด์ง€ (์ƒ์‚ฐ ๊ฒŒ์ด์ง€)

์ธก์ •์‹œ์Šคํ…œ ์šฉ์–ด์ •์˜ - ๊ณ„์† ์ธก์ • ํ’ˆ์งˆ โ€œ์ข‹์€ ์ธก์ •ํ’ˆ์งˆโ€ ์ด๋ž€ ์ธก์ •์น˜๊ฐ€ โ€œ๊ธฐ์ค€๊ฐ’โ€ ๋˜๋Š” ์ฐธ๊ฐ’์— ๊ทผ์ ‘ํ•จ์„ ์˜๋ฏธํ•˜๊ณ , โ€œ๋‚˜์œ ์ธก์ •ํ’ˆ์งˆโ€ ์ด๋ž€ ์ธก์ •์น˜๊ฐ€ โ€œ๊ธฐ์ค€๊ฐ’โ€๊ณผ ์ฐจ์ด๊ฐ€ ํผ์„ ์˜๋ฏธํ•œ๋‹ค. ์™„๋ฒฝํ•œ ์ธก์ •๊ฐ’ (๋ฌด ๋ถ„์‚ฐ, ๋ฌด BIAS, ์ธก์ •๋ถ€ํ’ˆ์ด ์˜ค ๋ถ„๋ฅ˜๋  ํ™•๋ฅ ์ด ์—†๋Š” ์ด์ƒ์  ์ธก์ •์‹œ์Šคํ…œ) ์€ ๊ฒฐ์ฝ” ์กด์žฌํ•˜์ง€ ์•Š๋Š”๋‹ค ํ†ต๊ณ„์ ์œผ๋กœ ์˜ˆ์ธก ๊ฐ€๋Šฅ Vs ์˜ˆ์ธก ๋ถˆ๊ฐ€๋Šฅ

์ธก์ •์‹œ์Šคํ…œ์˜ ๊ธฐ๋ณธ ์š”๊ตฌ ํŠน์„ฑ ์ธก์ •์‹œ์Šคํ…œ์˜ ์š”๊ตฌ ํŠน์„ฑ ์ธก์ •์‹œ์Šคํ…œ์˜ ๋ณ€๋™ํญ์€ ์ œ์กฐ๊ณต์ •์˜ ๋ณ€๋™ํญ๋ณด๋‹ค ์ž‘์•„์•ผ ํ•œ๋‹ค. ์ธก์ •์‹œ์Šคํ…œ์˜ ๋ณ€๋™ํญ์€ SPEC' ๋ฒ”์œ„๋ณด๋‹ค ์ž‘์•„์•ผ ํ•œ๋‹ค. ์ธก์ •์‹œ์Šคํ…œ์˜ ๋ณ€๋™ํญ์€ ์ œ์กฐ๊ณต์ •์˜ ๋ณ€๋™ํญ๊ณผ SPEC' ํ•œ๊ณ„ ์ค‘ ๋” ์ž‘์€ ๊ฒƒ๊ณผ ๋น„๊ตํ•ด ์ž‘์•„์•ผ ํ•œ๋‹ค. (์ผ๋ฐ˜์ ์œผ๋กœ 10% ๋ณด๋‹ค ํฌ์ง€ ์•Š์Œ) ์ธก์ •์‹œ์Šคํ…œ์€ ํ†ต๊ณ„์  ๊ด€๋ฆฌ์ƒํƒœ(ํ†ต๊ณ„์  ์•ˆ์ •์ƒํƒœ)๋ฅผ ์œ ์ง€ : ์ธก์ •์‹œ์Šคํ…œ์˜ ๋ณ€๋™ ์š”์ธ์€ ์šฐ์—ฐ ์›์ธ์— ์˜ํ•œ ๋ณ€๋™๋งŒ ํฌํ•จ๋˜๋„๋ก ๊ด€๋ฆฌ MSA3ํŒ ๋‚ด์šฉ

์ธก์ •์‹œ์Šคํ…œ ํ‰๊ฐ€๋ž€? ์ธก์ •์—๋Ÿฌ๋ฅผ ์ •๋Ÿ‰์ ์œผ๋กœ ์ธก์ •ํ•˜๊ณ  ์—๋Ÿฌ์˜ ๊ทผ์›์„ ํŒŒ์•…ํ•˜๋Š” ๊ฒƒ ํ‰๊ฐ€๋œ ์ธก์ •์—๋Ÿฌ์˜ ๋Ÿ‰์— ๊ทผ๊ฑฐํ•˜์—ฌ ์ธก์ •์‹œ์Šคํ…œ์˜ ์ ํ•ฉ์—ฌ๋ถ€๋ฅผ ๊ฒฐ์ •ํ•˜๋Š” ๊ฒƒ ์ธก์ •์‹œ์Šคํ…œ์˜ ์ธก์ •์‚ฐํฌ(๋ณ€๋™)์„ ์ค„์ด๊ธฐ ์œ„ํ•œ ๊ฐœ์„ ๋ฐฉ์•ˆ์„ ์ œ์‹œํ•˜๋Š” ๊ฒƒ

์ธก์ •์‹œ์Šคํ…œ ํ‰๊ฐ€ ๋ชฉ์  ์ธก์ • BIAS์™€ ์‚ฐํฌ์— ๋”ฐ๋ฅธ ์ธก์ • ๋ฐ์ดํ„ฐ์˜ ์งˆ(Quality) ๊ฒฐ์ • ์ธก์ • ๋ฐ์ดํ„ฐ(๊ฒ€์‚ฌ)๋Š” ์ œ์กฐ๊ณต์ •์˜ ์กฐ์ •์—ฌ๋ถ€ ๊ฒฐ์ • ๊ทผ๊ฑฐ ์ธก์ • ๋ฐ์ดํ„ฐ(SPC)์—์„œ ์–ป์–ด์ง„ ํ†ต๊ณ„๋Ÿ‰์„ ์ด์šฉํ•˜์—ฌ ๊ณต์ • ๋ถ„์„ ๋ฐ ๊ด€๋ฆฌ

์ธก์ • Process ๊ด€๋ฆฌ๋Œ€์ƒ๊ณต์ • ์˜์‚ฌ ๊ฒฐ์ • ์ธก์ • ๋ถ„์„ ์ธก์ • ํ”„๋กœ์„ธ์Šค ์ธก์ •๊ฐ’ ์ธก์ •๊ณผ์ •์€ ํ•˜๋‚˜์˜ ํ”„๋กœ์„ธ์Šค์ด๋‹ค. ์ธก์ • ๋ฐ ๋ถ„์„ํ™œ๋™์„ ํ•˜๋‚˜์˜ ํ”„๋กœ์„ธ์Šค๋กœ ํŒŒ์•… - ์ธก์ • ์žฅ๋น„๋Š” ์ธก์ • ํ”„๋กœ์„ธ์Šค์˜ ์ผ๋ถ€์ด๋‹ค ์ธก์ • ํ”„๋กœ์„ธ์Šค ๊ด€๋ฆฌ๋Œ€์ƒ๊ณต์ • ์˜์‚ฌ ๊ฒฐ์ • ์ธก์ • ๋ถ„์„ ์ธก์ •๊ฐ’

(Measurement Process) ์ธก์ • ์‹œ์Šคํ…œ INPUTS ์ธก์ • (Measurement Process) ์ธก์ •์น˜ (OUTPUTS) ๊ฒŒ ์ด ์ง€ ์ธก ์ • ์ž ์ธก์ •ํ™˜๊ฒฝ ์ธก์ •์žฌ๋ฃŒ ์ธก์ •๋ฐฉ๋ฒ• ๊ฐœ ์„  ์กฐ์‚ฌ/์ง„๋‹จ ๊ณต์ •๊ฐœ์„ ์„ ์œ„ํ•œ ์ •๋ณด

2. ์ธก์ •์‹œ์Šคํ…œ ๋ณ€๋™

Measurement Systems Errors of Variation and Position ์ธก์ •์‹œ์Šคํ…œ ๋ณ€๋™ ์œ ํ˜• Measurement Systems Errors of Variation and Position ์ธก์ •์‹œ์Šคํ…œ ๋ณ€๋™ Measurement Error Resolution Position Variation Fineness of Incrementation ์œ„์น˜ (Location) ํŒ๋ณ„๋ ฅ (Discrimination) ์‚ฐํฌ(ํผ์ง) (Dispersion) Bias Repeatability Reproducibility Difference between average of measurements and true value Variation in measurements by one operator Variation between averages of measure-ments between operators ํŽธ์˜(์ •ํ™•๋„*) Bias (Accuracy) ์žฌํ˜„์„ฑ (Reproducibility) ๋ฐ˜๋ณต์„ฑ(์ •๋ฐ€๋„**) (Repeatability) Stability Linearity Changes in bias over time Changes in bias over the range of measurement ์•ˆ์ •์„ฑ (Stability) ์„ ํ˜•์„ฑ (Linearity) ์ •ํ™•๋„* : ํŽธ์˜ + ๋ฐ˜๋ณต์„ฑ ์ •๋ฐ€๋„**: ๋ฐ˜๋ณต์„ฑ + ์•ˆ์ •์„ฑ+ ์„ ํ˜•์„ฑ ISO์™€ ASTM(๋ฏธ๊ตญ์žฌ๋ฃŒ์‹œํ—˜ํ•™ํšŒ) ์—์„œ๋Š” ์ •ํ™•๋„๋ฅผ ์น˜์šฐ์นจ๊ณผ ๋ฐ˜๋ณต์„ฑ์ด ํฌํ•จ๋œ ์šฉ์–ด๋กœ ์‚ฌ์šฉํ•œ๋‹ค. ์ •ํ™•๋„๋ผ๋Š” ์šฉ์–ด๋ฅผ ์‚ฌ์šฉํ•˜๋Š”๋ฐ ํ˜ผ๋ž€์„ ํ”ผํ•˜๊ธฐ ์œ„ํ•ด์„œ ASTM ์—์„œ๋Š” ์œ„์น˜์˜ค์ฐจ๋ฅผ ๋ฌ˜์‚ฌํ•˜๋Š” ๊ฒƒ์œผ๋กœ ํŽธ์˜๋ผ๋Š” ์šฉ์–ด๋งŒ์„ ์‚ฌ์šฉํ•  ๊ฒƒ์„ ๊ถŒ์žฅํ•œ๋‹ค.

์ธก์ •์‹œ์Šคํ…œ ๋ณ€๋™์œ ํ˜•-๊ณ„์† ๋ฏผ๊ฐ๋„ (Sensitivity): ๊ฐ์ง€ํ•  ์ˆ˜ ์žˆ๋Š” ์ถœ๋ ฅ ์‹ ํ˜ธ๋ฅผ ์ฃผ๋Š” ๊ฐ€์žฅ ์ž‘์€ ์ž…๋ ฅ ์‹ ํ˜ธ๋กœ ์ธก์ •๋œ ํŠน์„ฑ๋ณ€ํ™”์— ๋Œ€ํ•œ ์ธก์ •์‹œ์Šคํ…œ์˜ ๋ฐ˜์‘์„ฑ์„ ์˜๋ฏธํ•œ๋‹ค. ์ด๋Š” ๊ฒŒ์ด์ง€์˜ ํŒ๋ณ„๋ ฅ, ํ’ˆ์งˆ, ๋ณด์ „, ํ‘œ์ค€ ์šด์šฉ์กฐ๊ฑด์— ์˜ํ•ด ๊ฒฐ์ •๋œ๋‹ค. ์ผ๊ด€์„ฑ(Consistency) : ์ผ๊ด€์„ฑ์€ ์‹œ๊ฐ„์— ๊ฑธ์ณ ์ทจํ•ด์ง„ ์ธก์ •๊ฐ’ ๋ณ€๋™์˜ ์ฐจ์ด์ด๋ฉฐ, ์ด๋Š” ์‹œ๊ฐ„์— ๋”ฐ๋ฅธ ๋ฐ˜๋ณต์„ฑ์„ ์˜๋ฏธํ•œ๋‹ค ๊ท ์ผ์„ฑ(Uniformity) : ๊ฒŒ์ด์ง€์˜ ๋™์ž‘๋ฒ”์œ„์—์„œ์˜ ๋ณ€๋™์˜ ์ฐจ์ด๋ฅผ ๋งํ•œ๋‹ค. ์ด๋Š” ์‚ฐํฌ์˜ ์„ ํ˜•์„ฑ์„ ์˜๋ฏธํ•œ๋‹ค ์ธก์ •์‹œ์Šคํ…œ๋Šฅ๋ ฅ(Measurement System Capability): ์ธก์ •์‹œ์Šคํ…œ ๋ณ€๋™์˜ ๋‹จ๊ธฐ ์ถ”์ •๊ฐ’์œผ๋กœ ๋‹จ๊ธฐ์— ๊ฑธ์นœ ์ธก์ •๊ฐ’์˜ ๋ณ€๋™์„ ์˜๋ฏธํ•œ๋‹ค ์ธก์ •์‹œ์Šคํ…œ์„ฑ๋Šฅ(Measurement System Performance) : ์ธก์ •์‹œ์Šคํ…œ ๋ณ€๋™์˜ ์žฅ๊ธฐ ์ถ”์ •๊ฐ’์œผ๋กœ ์žฅ๊ธฐ์— ๊ฑธ์นœ ์ธก์ •๊ฐ’์˜ ๋ณ€๋™์„ ์˜๋ฏธํ•œ๋‹ค

(Number of Distinct Category) ํŒ๋ณ„๋ ฅ (Discrimination) ๊ณต์ •์—์„œ ์ƒ์‚ฐ๋˜๋Š” ๋ถ€ํ’ˆ์˜ ํ•ฉ ๋ถ€ ํŒ์ •์—๋งŒ ์‚ฌ์šฉ ๊ฐ€๋Šฅ ํ•˜๋ฉฐ, ๊ณต์ •๊ด€๋ฆฌ์šฉ์œผ๋กœ ์‚ฌ์šฉํ•˜๊ธฐ์—๋Š” ๋ถ€์ ํ•ฉ ํ•จ ๊ด€๋ฆฌ๋„์—์„œ ๊ณต์ •๋ณ€ํ™”๋ฅผ ๋ฏผ๊ฐํ•˜๊ฒŒ ํƒ์ง€ํ•˜์ง€ ๋ชปํ•˜๋ฉฐ, ๋ถ€์ •ํ™•ํ•œ ๊ณต์ •๋Šฅ๋ ฅ์ง€์ˆ˜๋ฅผ ์‚ฐ์ถœํ•  ์ˆ˜ ์žˆ์Œ. ๋”ฐ๋ผ์„œ ๊ณต์ •๊ด€๋ฆฌ์šฉ์œผ๋กœ ์‚ฌ์šฉํ•˜๊ธฐ์—๋Š” ๋ถ€์ ํ•ฉ ํ•จ ๊ด€๋ฆฌ๋„์˜ ์šด์˜, ๊ณต์ •๋Šฅ๋ ฅ ๋ถ„์„ ๋“ฑ ๊ณต์ •๊ด€๋ฆฌ์— ์‚ฌ์šฉํ•˜๊ธฐ ์ ํ•ฉํ•จ ์„ธ๋ณ„๋ฒ”์ฃผ (Number of Distinct Category) ๊ณ„์ธก๊ธฐ ๋ˆˆ๊ธˆ์ด ์กฐ๋ฐ€ํ•ด์•ผ ํ•œ๋‹ค.

ํŒ๋ณ„๋ ฅ(Discrimination) - ํŒ์ • ๋ฐฉ๋ฒ• ๊ณต์ •๋ถ„ํฌ์˜ ํŒ๋ณ„ ๋ฒ”์ฃผ ์ˆ˜ ( Number of Distinct category : ndc) ndc = 1.41*(PV/GRR) โ‰ฅ5 (๋‹จ, ์†Œ์ˆ˜์  ์ดํ•˜๋Š” ๋ฒ„๋ฆฐ ์ •์ˆ˜ ๊ฐ’์ด๋‹ค.) ํŒ๋ณ„๋ ฅ 1/100 ์ธ์น˜ ํŒ๋ณ„๋ ฅ 1/1000 ์ธ์น˜ ๋‚ฎ์€ ํŒ๋ณ„๋ ฅ ๊ฐ™์€ ๋ฐ์ดํ„ฐ๋กœ๋ถ€ํ„ฐ ์ž‘์„ฑ๋œ 2๊ฐœ์˜ ๊ด€๋ฆฌ๋„ ์ž„ (์šฐ์ธก์€ ๋ฐ์ดํ„ฐ๋ฅผ 1/100 ์ธ์น˜๋‹จ์œ„๋กœ ๋ฐ˜์˜ฌ๋ฆผํ•œ ๊ฒƒ์„ ๋‚˜ํƒ€๋‚ธ ๊ฒƒ์ž„)

BIAS (ํŽธ์˜) ๊ด€์ธก(Observed)ํ‰๊ท ๊ณผ ์ฐธ๊ฐ’/๊ธฐ์ค€๊ฐ’(Reference)์˜ ์ฐจ์ด ์ธก์ •์‹œ์Šคํ…œ์˜ ๊ณ„ํ†ต์˜ค์ฐจ์˜ ์ฒ™๋„ ๋ชจ๋“  ๋ณ€๋™์˜ ์›์ธ์˜ ๊ฒฐํ•ฉ๋œ ์˜ํ–ฅ์œผ๋กœ ๋‚˜ํƒ€๋‚˜๋Š” ์ด ์˜ค์ฐจ ์‹œ๋ฃŒํ‰๊ท  ๊ธฐ์ค€ ๊ฐ’ (์ฐธ ๊ฐ’) Bias ์›์ธ ์ธก์ •๊ธฐ์˜ ๊ต์ •์ด ํ•„์š” ๊ณ„์ธก๊ธฐ์˜ ๋…ธํ›„ ๊ธฐ์ค€๊ธฐ(Reference)์˜ ๋งˆ๋ชจ, ์†์ƒ ๋˜๋Š” ์˜ค์ฐจ ๋ฐœ์ƒ ๊ธฐ์ค€๊ธฐ์˜ ๋ถ€์ ์ ˆํ•œ ๊ต์ • ๋ฐ ์‚ฌ์šฉ ์ธก์ •๋ฐฉ๋ฒ•์˜ ์ฐจ์ด(์ดˆ๊ธฐ ๋ณด์ •, ์…‹์—… ๋“ฑ) ํ™˜๊ฒฝ (์˜จ๋„, ์Šต๋„, ์ง„๋™, ์ฒญ๊ฒฐ) ์ธก์ •์‹œ์Šคํ…œ์˜ ์˜ค์ฐจ๋Š” 5๊ฐœ์˜ ๋ฒ”์ฃผ๋กœ ๋ถ„๋ฅ˜๋  ์ˆ˜ ์žˆ๋‹ค (ํŽธ์˜,๋ฐ˜๋ณต์„ฑ,์žฌํ˜„์„ฑ,์•ˆ์ •์„ฑ,์„ ํ˜•์„ฑ) 26

STABILITY (์•ˆ์ •์„ฑ) ์ธก์ •๊ธฐ์˜ ๋งˆ๋ชจ๋‚˜ ๊ธฐ์˜จ๊ณผ ๊ฐ™์€ ํ™˜๊ฒฝ์กฐ๊ฑด์˜ ๋ณ€ํ™”์— ์˜ํ•ด ์‹œ๊ฐ„์ด ์ง€๋‚จ์— ๋”ฐ๋ฅธ ๋™์ผ ์ƒ˜ํ”Œ ๋˜๋Š” ๋งˆ์Šคํ„ฐ์˜ ์ด ์‚ฐํฌ(๋ณ€๋™), ์ฆ‰ ์‹œ๊ฐ„๊ฒฝ๊ณผ์— ๋”ฐ๋ฅธ ํŽธ์˜์˜ ๋ณ€ํ™”๋ฅผ ๋‚˜ํƒ€๋‚ธ๋‹ค. ์•ˆ์ •์„ฑ์ด ํ™•๋ณด๋˜์–ด์•ผ ์ธก์ •ํŠน์„ฑ์„ ์ •๋Ÿ‰ํ™” ํ•  ์ˆ˜ ์žˆ์Œ ์‹œ๊ฐ„ 1 ์‹œ๊ฐ„ 2 ์•ˆ์ •์„ฑ ๋‹จ์ผํŠน์„ฑ์„ ์žฅ๊ธฐ๊ฐ„์— ๊ฑธ์ณ ์ธก์ • ์›์ธ ์ธก์ •๊ธฐ์˜ ๊ต์ • ํ•„์š”(๊ต์ •์ฃผ๊ธฐ ๋‹จ์ถ•) ๊ณ„์ธก๊ธฐ์˜ ๋…ธํ›„, ์—ด์•…ํ•œ ์˜ˆ๋ฐฉ๋ณด์ „, ํ™˜๊ฒฝ๋ณ€๋™ 27

LINERITY (์„ ํ˜•์„ฑ) ๊ธฐ๋Œ€๋˜๋Š” ์ธก์ •๊ธฐ์˜ ์ธก์ •๋ฒ”์œ„์—์„œ ํŽธ์˜๊ฐ„์˜ ์ฐจ์ด๋ฅผ ๋งํ•จ 0.5 cm ๋ฐ 85.0 cm์—์„œ์˜ ํŽธ์˜๋Š” ์„œ๋กœ ๊ฐ™์€๊ฐ€? ์‹œ๋ฃŒํ‰๊ท  ๊ธฐ์ค€ ๊ฐ’ (์ฐธ ๊ฐ’) Large Bias Small ์„ ํ˜•์„ฑ ์›์ธ ์ธก์ •๊ธฐ์˜ ๊ต์ • ํ•„์š”(๊ต์ •์ฃผ๊ธฐ ๋‹จ์ถ•) ๊ณ„์ธก๊ธฐ์˜ ๋…ธํ›„, ์—ด์•…ํ•œ ์˜ˆ๋ฐฉ๋ณด์ „, ํ™˜๊ฒฝ๋ณ€๋™ ๋งˆ์Šคํ„ฐ์˜ ๋งˆ๋ชจ ๋ฐ ์†์ƒ, ๋งˆ์Šคํ„ฐ์˜ ์˜ค์ฐจ 28

REPEATABILITY (๋ฐ˜๋ณต์„ฑ) ๋™์ผ ์ƒ˜ํ”Œ์˜ ๋™์ผ ํŠน์„ฑ์„ 1๋ช…์˜ ํ‰๊ฐ€์ž๊ฐ€ 1๊ฐœ์˜ ์ธก์ •๊ตฌ๋กœ ์—ฌ๋Ÿฌ๋ฒˆ ์ธก์ •ํ•˜์—ฌ ์–ป์–ด์ง„ ๋ฐ์ดํƒ€์˜ ๋ณ€๋™(์‚ฐํฌ) ์ธก์ •์ž ๋‚ด(within appraiser) ๋ณ€๋™, ํ†ต์ƒ EV (Equipment Variation)๋กœ ์–ธ๊ธ‰๋จ ์ธก์ •๊ธฐ ๊ณ ์œ  ๋ณ€๋™ ๋˜๋Š” ์ธก์ •๊ธฐ ์ž์ฒด์˜ ๋Šฅ๋ ฅ ์ •ํ•ด์ง„ ์ธก์ •์กฐ๊ฑดํ•˜์—์„œ ์—ฐ์†์ ์ธ ์ธก์ •์œผ๋กœ ๋ถ€ํ„ฐ ์–ป์–ด์ง€๋Š” ์šฐ์—ฐ์›์ธ ๋ณ€๋™์„ ์˜๋ฏธํ•œ๋‹ค ์ •๊ทœ๋ถ„ํฌ ๊ณก์„ ์—์„œ 99%ํ™•๋ฅ ์„ ๊ณ ๋ คํ•œ ๊ฐ’ GRR ํ‘œ์ค€ํŽธ์ฐจ์˜ ์‚ฌ์šฉ ์— ๊ด€ํ•˜์—ฌ ๊ณผ๊ฑฐ๊ด€๋ก€์ ์œผ๋กœ ์ธก์ •์˜ค์ฐจ์˜ ์ „์ฒด ํญ์„ ๋‚˜ํƒ€๋‚ด๊ธฐ ์œ„ํ•ด 99%ํญ์ด ์‚ฌ์šฉ๋˜์–ด์ ธ ์™”๊ณ  5.15 ์Šน์ˆ˜๋กœ ํ‘œํ˜„๋˜์—ˆ๋‹ค(์‹œ๊ทธ๋งˆGRR์€ 99%๋ฅผ ํ‘œํ˜„ ํ•˜๊ธฐ ์œ„ํ•ด 5.15๋ฅผ ๊ณฑํ•œ๋‹ค) 99.73%๋Š” 6์Šน์ˆ˜์— ์˜ํ•ด ํ‘œํ˜„๋œ๋‹ค ์ •๊ทœ๋ถ„ํฌ์˜ ์ „์ฒด ํญ์„ ๋‚˜ํƒ€๋‚ด๋Š” + - 3์‹œ๊ทธ๋งˆ ์ฆ‰ 6์‹œ๊ทธ๋งˆ๋Š” 99.73%ํญ์„ ์˜๋ฏธํ•œ๋‹ค. ์ด ์ธก์ •๋ณ€๋™์˜ 99.73%๋ฅผ ํฌํ•จํ•˜๋Š” ํญ์„ ์‚ฌ์šฉํ•˜๊ณ ์ž ํ•œ๋‹ค๋ฉด 5.15 ๋Œ€์‹  6์„ ์‚ฌ์šฉํ•˜๋ฉด ๋œ๋‹ค. 29

REPEATABILITY(๋ฐ˜๋ณต์„ฑ) - ๋ถ€ํ’ˆ(์ƒ˜ํ”Œ)๋‚ด : ๋ชจ์–‘, ์œ„์น˜, ํ‘œ๋ฉด์ฒ˜๋ฆฌ, ๊ฐ€๋Š˜์–ด์ง, ์ƒ˜ํ”Œ ์ผ๊ด€์„ฑ ๋ฐ˜๋ณต์„ฑ ๋ถ€์กฑ์˜ ์›์ธ - ์ธก์ •๊ธฐ ๋‚ด : ์ˆ˜๋ฆฌ, ๋งˆ๋ชจ, ๊ณ ์ •๊ตฌ ๊ณ ์žฅ - ํ‘œ์ค€ ๋‚ด : ํ’ˆ์งˆ, ๋“ฑ๊ธ‰, ๋งˆ๋ชจ - ๋ฐฉ๋ฒ• ๋‚ด : ์…‹์—… ๋ณ€๋™, ๊ธฐ์ˆ , ์˜์ ์กฐ์ •, ๋ฐ›์นจ, ์กฐ์ž„, ํฌ์ธํŠธ ๋ฐ€๋„ - ์ธก์ •์ž ๋‚ด : ๊ธฐ์ˆ , ์œ„์น˜, ๊ฒฝํ—˜๋ถ€์กฑ, ์กฐ์ž‘๊ธฐ์ˆ  ๋˜๋Š” ํ›ˆ๋ จ, ๊ฐ์ •, ํ”ผ๋กœ - ํ™˜๊ฒฝ ๋‚ด : ์˜จ๋„, ์Šต๋„, ์ง„๋™, ์กฐ๋ช…, ์ฒญ๊ฒฐ - ๊ฐ€์ •์˜ ์œ„๋ฐฐ - ์ธก์ •๊ธฐ ์„ค๊ณ„ ๋˜๋Š” ๋ฐฉ๋ฒ•์˜ ๊ฐ•๊ฑด์„ฑ ๋ถ€์กฑ, ๊ท ์ผ์„ฑ ๊ฒฐ์—ฌ - ์ ์šฉ์— ์žˆ์–ด์„œ ์ž˜๋ชป๋œ ๊ฒŒ์ด์ง€ ์„ ํƒ - ๋’คํ‹€๋ฆผ(๊ฒŒ์ด์ง€ ๋˜๋Š” ๋ถ€ํ’ˆ), ๊ฒฝ๋„๋ถ€์กฑ

REPRODUCIBILITY (์žฌํ˜„์„ฑ) ๋™์ผ์ƒ˜ํ”Œ์˜ ๋™์ผํŠน์„ฑ์„ ๊ฐ™์€ ์ธก์ •๊ตฌ๋กœ ์—ฌ๋Ÿฌ ํ‰๊ฐ€์ž๋กœ ๋ถ€ํ„ฐ ์–ป์€ ์ธก์ •๊ฐ’ ํ‰๊ท ์˜ ๋ณ€๋™ (์‚ฐํฌ) ์ž‘์—…์ž์˜ Skill ์— ์˜ํ–ฅ์„ ๋ฐ›๋Š” ์ˆ˜๋™ ์ธก์ •๊ธฐ์— ํ•œ์ • ์žฌํ˜„์„ฑ ์ธก์ •์ž A ์ธก์ •์ž C ์ธก์ •์ž B

Gage R&R or GRR Gage R&R or GRR์€ ๋ฐ˜๋ณต์„ฑ๊ณผ ์žฌํ˜„์„ฑ์— ์˜ํ•œ ๋ณ€๋™(์‚ฐํฌ)์˜ ์ถ”์ •์น˜ ์ด๋‹ค. ์ฆ‰, ์‹œ์Šคํ…œ ๊ตฐ๋‚ด ๋ฐ ๊ตฐ๊ฐ„ ๋ถ„์‚ฐ์˜ ํ•ฉ๊ณผ ๋™์ผํ•˜๋‹ค. (์‹œ์Šคํ…œ๋‚ด์˜ ๋ถ„์‚ฐ๊ณผ ์‹œ์Šคํ…œ๊ฐ„์˜ ๋ถ„์‚ฐ์˜ ํ•ฉ) ๊ฒŒ์ด์ง€ ๋ณ€๋™(R&R) = ๋ฐ˜๋ณต์„ฑ + ์žฌํ˜„์„ฑ ์กฐ์ •๋œ ์žฌํ˜„์„ฑ = ์žฌํ˜„์„ฑ - ๋ฐ˜๋ณต์„ฑ/์ž์œ ๋„

์ธก์ •๊ธฐ์˜ ์ •๋„ (ํŽธ์˜, ๋ฐ˜๋ณต์„ฑ) ํŽธ์˜์™€ ๋ฐ˜๋ณต์„ฑ์€ ์„œ๋กœ ๋…๋ฆฝ์ด๋‹ค.

์ธก์ •์‹œ์Šคํ…œ ๋ณ€๋™(์‚ฐํฌ) ์œ ํ˜• -์ •๋ฆฌ ์‹œ๊ฐ„ 1 ์‹œ๊ฐ„ 2 โ‘ฃ โ‘  ๊ธฐ์ค€ ๊ฐ’ (์ฐธ ๊ฐ’) ์ธก์ •์ž A โ‘ก ์ธก์ •์ž B ์‹œ๋ฃŒํ‰๊ท  โ‘ข ์‹œ๋ฃŒํ‰๊ท  1. ์•ˆ์ •์„ฑ(Stability) 2, ํŽธ์˜(Bias) 3, ์žฌํ˜„์„ฑ(Rproducibility) 4, ๋ฐ˜๋ณต์„ฑ(Repeatability) 5, ์„ ํ˜•์„ฑ(Linerity) ์‹œ๋ฃŒํ‰๊ท  โ‘ข ์‹œ๋ฃŒํ‰๊ท  ๊ธฐ์ค€ ๊ฐ’ (์ฐธ ๊ฐ’) Large Bias Small โ‘ค

3. ์ธก์ •์‹œ์Šคํ…œ ํ‰๊ฐ€์ ˆ์ฐจ Facilitator says These seven topic areas will be the focus during this course: The course must begin with some basic definitions and history. There will be discussion on ways to measure, interpret, and control variation. Participants will construct and use the various tools in the Quality Control โ€œToolboxโ€. Since processes are subject to change, the need to track their behavior through time will be emphasized. Time will be spent making and interpreting control charts. Participants will practice evaluating processes. Participants will learn that knowing how โ€œgoodโ€ measurement systems are is vital before making judgements based upon the measurements taken.

์ธก์ •์‹œ์Šคํ…œ ํ‰๊ฐ€ ๋‹จ๊ณ„ 1๋‹จ๊ณ„ : ์˜ฌ๋ฐ”๋ฅธ ๋ณ€์ˆ˜๊ฐ€ ์ ์ ˆํ•œ ํŠน์„ฑ ์œ„์น˜์—์„œ ์ธก์ •๋˜๊ณ  ์žˆ์Œ์„ ์ž…์ฆํ•˜๊ธฐ ์œ„ํ•œ ํ‰๊ฐ€ ๊ณ ์ •์ƒํƒœ๋‚˜ ์กฐ์ž„ ์ƒํƒœ๋„ ์ž…์ฆ(ํ•ด๋‹น ์‹œ) ํ™˜๊ฒฝ์  ์š”์ธ๋“ค์ด ์ธก์ •์‹œ์Šคํ…œ์— ์˜ํ–ฅ์„ ์ฃผ๋Š”์ง€ ํŒŒ์•… 2๋‹จ๊ณ„ : ์ธก์ •์‹œ์Šคํ…œ์ด ์ ์ ˆํ•œ ํ†ต๊ณ„์  ํŠน์„ฑ์„ ๊ณ„์† ์œ ์ง€ํ•˜๋Š”์ง€ ํ‰๊ฐ€ ์ผ๋ฐ˜์ ์ธ 2๋‹จ๊ณ„ ํ‰๊ฐ€์˜ ํ•œ ํ˜•ํƒœ : ๊ฒŒ์ด์ง€ R&R ํ‰๊ฐ€์ ˆ์ฐจ ๋ฌธ์„œํ™” ์š”์†Œ ์ธก์ •ํ•ญ๋ชฉ์˜ SPEC' ๋ฐ ์‹œํ—˜์ ˆ์ฐจ๊ฐ€ ์ ์šฉ๋  ํ™˜๊ฒฝ ๋ฐ์ดํ„ฐ์˜ ์ˆ˜์ง‘, ๊ธฐ๋ก, ๋ถ„์„์˜ ๊ทœ์ •๋œ ๋ฐฉ๋ฒ• ํ•ต์‹ฌ์šฉ์–ด ๋ฐ ๊ฐœ๋…์˜ ์šด์˜์ƒ์˜ ์ •์˜ ํ‘œ์ค€๊ธฐ(standards) ์ €์žฅ, ์œ ์ง€, ์‚ฌ์šฉ์— ๋Œ€ํ•œ ์„ค๋ช… (์ ์šฉ ์‹œ) ํ‰๊ฐ€์‹œ๊ธฐ, ํ‰๊ฐ€์— ๋Œ€ํ•œ ์กฐ์ง์ƒ์˜ ์ฑ…์ž„, ํ‰๊ฐ€๊ฒฐ๊ณผ์— ๋Œ€ํ•œ ์กฐ์น˜์˜ ์ฑ…์ž„ 35

์ธก์ •์‹œ์Šคํ…œ ํ‰๊ฐ€ ๋‹จ๊ณ„ โ–ถ ์ธก์ •์‹œ์Šคํ…œ ๋ถ„์„ ์ˆœ์„œ ์ธก์ • ํŒŒ๋ผ๋ฏธํ„ฐ ์„ ์ • ํ‰๊ฐ€ ๊ณ„ํš ์ˆ˜๋ฆฝ ๊ณ„์ธก ์‹ค์‹œ ์ธก์ •์‹œ์Šคํ…œ ๊ฐœ์„  NO ๊ณ„์ธก๊ธฐ์˜ Bias ๋ฐ ์•ˆ์ •์„ฑ? ์ธก์ •์‹œ์Šคํ…œ ๊ฐœ์„ ์„ ์œ„ํ•œ ์กฐ์น˜ - ์ƒˆ ๊ณ„์ธก๊ธฐ ๊ตฌ์ž… ๋‚ด์ง€ ์ œ์ž‘ - ๊ณ„์ธก๊ธฐ ๋ณด์ „ - ์ธก์ •์ž ๊ต์œก ๋“ฑ O.K NO ๊ณ„์ธก๊ธฐ์˜ ๋ฐ˜๋ณต์„ฑ๊ณผ ์žฌํ˜„์„ฑ? O.K ์ธก์ •์‹œ์Šคํ…œ ๋ถ„์„ ์™„๋ฃŒ

ํ‰๊ฐ€ ๊ณ„ํš ๋ฐ ์‹คํ–‰์‹œ ๊ณ ๋ ค์‚ฌํ•ญ ์ ‘๊ทผ๋ฒ• ๊ณ„ํš : ์ธก์ • ๋ฐ Gauging์˜ ๊ณตํ•™์  ์—ฐ๊ตฌ (GD&T ๋“ฑ) ์ธก์ •์ธ์›, ์ƒ˜ํ”Œ ์ˆ˜, ๋ฐ˜๋ณต ์ธก์ •์ˆ˜ ๊ฒฐ์ • ํ‰๊ฐ€์ž์˜ ๊ต์œก ์ „ ์ธก์ • ๋ฒ”์œ„๋ฅผ ๋Œ€ํ‘œํ•˜๋Š” ๊ณต์ •๋‚ด ์ƒ˜ํ”Œ ์ฑ„์ทจ ์ธก์ •์žฅ๋น„์˜ ํ•ด์ƒ๋„๋Š” ์˜ˆ์ƒ๋˜๋Š” ๋ณ€ํ™”์— ์ ์–ด๋„ 1/10์ •๋„ ์ด์–ด์•ผ ํ•จ ์ƒ˜ํ”Œ์ฑ„์ทจ ์ฃผ๊ธฐ ๋ฐ ์ธก์ • ์ฃผ๊ธฐ ๊ฒฐ์ • ์ธก์ •๋ฐฉ๋ฒ•์ด ํŠน์„ฑ์น˜๋ฅผ ์ธก์ •ํ•˜๊ณ  ์ •์˜๋œ ์ธก์ •์ ˆ์ฐจ๋ฅผ ์ค€์ˆ˜ํ•จ์„ ๋ณด์žฅ ์ธก์ •์น˜์˜ ์‚ฐํฌ์˜ ๋žœ๋ค์„ฑ์„ ๋ณด์žฅํ•˜๊ธฐ ์œ„ํ•ด ๋ฌด์ž‘์œ„ ์ธก์ • ์‹ค์‹œ

4. ๊ณ„๋Ÿ‰ํ˜• ์ธก์ •์‹œ์Šคํ…œ ํ‰๊ฐ€ Facilitator says These seven topic areas will be the focus during this course: The course must begin with some basic definitions and history. There will be discussion on ways to measure, interpret, and control variation. Participants will construct and use the various tools in the Quality Control โ€œToolboxโ€. Since processes are subject to change, the need to track their behavior through time will be emphasized. Time will be spent making and interpreting control charts. Participants will practice evaluating processes. Participants will learn that knowing how โ€œgoodโ€ measurement systems are is vital before making judgements based upon the measurements taken.

๋ถ€ํ’ˆ ๋ณ€๋™(์‚ฐํฌ)์˜ ๊ตฌ์„ฑ ์ด๋ณ€๋™ ๋ถ€ํ’ˆ๊ฐ„์— ๋ฐœ์ƒํ•˜๋Š” ๋ณ€๋™ ์ธก์ •์˜ค์ฐจ๋กœ ์ธํ•œ ๋ณ€๋™ ๊ตฐ๋‚ด ๋ณ€๋™ ๊ตฐ๊ฐ„ ๋ณ€๋™ ์ธก์ •์ž ๋ณ€๋™ ๊ณ„์ธก๊ธฐ ๋ณ€๋™ (Reproducibility) ๊ณ„์ธก๊ธฐ ๋ณ€๋™ ์ •ํ™•์„ฑ ๋ฐ˜๋ณต์„ฑ ์•ˆ์ •์„ฑ ์„ ํ˜•์„ฑ

๋ณ€๋™ ์œ ํ˜•๋ณ„ ์ธก์ •์‹œ์Šคํ…œ ํ‰๊ฐ€๋ฐฉ๋ฒ• โ‘  ์•ˆ์ •์„ฑ(Stability) ํ‰๊ฐ€ โ‘ก ํŽธ์˜(Bias) ํ‰๊ฐ€ โ‘ข ์„ ํ˜•์„ฑ ํ‰๊ฐ€ ๋…๋ฆฝ ์ƒ˜ํ”Œ ๋ฐฉ๋ฒ•(Independent Sample Method) ์ฐจํŠธ ๋ฐฉ๋ฒ•(Chart Method) โ‘ข ์„ ํ˜•์„ฑ ํ‰๊ฐ€ โ‘ฃ ๋ฐ˜๋ณต์„ฑ ๋ฐ ์žฌํ˜„์„ฑ(R&R) ํ‰๊ฐ€ ํ‰๊ฐ€๋ฐฉ๋ฒ• ๋ฒ”์œ„ ๋ฐฉ๋ฒ•(Range Method) ํ‰๊ท  ๋ฐ ๋ฒ”์œ„ ๋ฐฉ๋ฒ•(Average & Range Method) ANOVA ๋ฐฉ๋ฒ• ๊ฒฐ๊ณผ๋ถ„์„ ๋ฐฉ๋ฒ• Graphical ๋ฐฉ๋ฒ• ์ˆ˜๋ฆฌ์  ๋ฐฉ๋ฒ•

โ‘ฃ ๋ฐ˜๋ณต์„ฑ ๋ฐ ์žฌํ˜„์„ฑ(R&R) ํ‰๊ฐ€ ์š”๊ตฌ์‚ฌํ•ญ: ๋ณ€๋™ ์กฐ์‚ฌ ํ†ต๊ณ„์  ์•ˆ์ •์„ฑ ์ƒ์‚ฐ์ œํ’ˆ ์ค‘ ์ƒ˜ํ”Œ ์ฑ„์ทจ(๋ช‡ ์ผ ๋™์•ˆ ์ƒ˜ํ”Œ์„ ์ฑ„์ทจํ•˜๋Š” ๊ฒƒ์ด ๊ฐ€์žฅ ์ข‹์Œ) โ€œํ˜ธ๋ˆ ํšจ๊ณผโ€ ์ œ๊ฑฐ (Blind test) ์•Œ๋ ค์ง„ ํŽธ์˜ ๋ฐ ๊ฒฝํ–ฅ์„ ๊ฐ€์ง„ ๊ฒŒ์ด์ง€ ์‚ฌ์šฉ (ํŽธ์˜ ์ž‘์„์ˆ˜๋ก ์ข‹์Œ) ๋” ๋†’์€ โ€œ์ •๋ฐ€๋„โ€๋ฅผ ๊ฐ€์ง„ ๊ฒŒ์ด์ง€ ์‚ฌ์šฉ ๋ฐ˜๋ณต์„ฑ & ์žฌํ˜„์„ฑ์€ ๋ถ„๋ฆฌ๋  ์ˆ˜ ์—†์Œ ์ด ์˜ค์ฐจ๋Š” ๋‘ ๋ณ€๋™๊ทผ์›์„ ํฌํ•จํ•˜๊ณ  ์žˆ์Œ ์ •๋ณด ๋ฐ ๋ถ„์„์„ ์œ„ํ•ด ๋‘ ๋ณ€๋™๊ทผ์›์„ ๋ถ„๋ฆฌํ•ด์•ผ ํ•จ ๋ฐ˜๋ณต์„ฑ ์žฌํ˜„์„ฑ ์ด ์˜ค์ฐจ

โ…ก. Average & Range Method (๋ฒ”์œ„ ๋ฐ ํ‰๊ท  ๋ฐฉ๋ฒ•) ๋ถ„์„ ๋ฐฉ๋ฒ• : ์ˆ˜๋ฆฌ์  ๋ถ„์„(NAUMERICAL ANALYSIS) ๋ฐ˜๋ณต์„ฑ > ์žฌํ˜„์„ฑ ๊ฒŒ์ด์ง€์˜ ์ •๋น„๊ฐ€ ํ•„์š” ๋” ์ •๋ฐ€ํ•œ ๊ฒŒ์ด์ง€๊ฐ€ ํ•„์š” ์ธก์ •์„ ์œ„ํ•œ ํด๋žจํ”„ ๋˜๋Š” ์œ„์น˜๊ฐ€ ๊ฐœ์„ ์ด ํ•„์š” ๋ถ€ํ’ˆ๊ฐ„ ๋ณ€๋™์ด ๊ณผ๋‹ค ๋ฐ˜๋ณต์„ฑ < ์žฌํ˜„์„ฑ ํ‰๊ฐ€์ž๊ฐ€ ๊ณ„๊ธฐ๋ฅผ ์‚ฌ์šฉ ๋ฐ ์ฝ๋Š” ๋ฐฉ๋ฒ•์— ๋Œ€ํ•ด ๋งŽ์€ ๊ต์œก ํ•„์š” ๊ฒŒ์ด์ง€ ๋ˆˆ๊ธˆ์— ๋Œ€ํ•œ ๊ต์ • ๋ถ€์ •ํ™• ํ‰๊ฐ€์ž ์‚ฐํฌ๋ฅผ ์ค„์ด๊ธฐ ์œ„ํ•œ ์ „์šฉ FIXTURE ๊ฒ€ํ†  ํ•„์š” ํ‰๊ท ๋ฒ”์œ„๋ฒ• ์€ ์ธก์ •์‹œ์Šคํ…œ์˜ ๋ฐ˜๋ณต์„ฑ ๋ฐ ์žฌํ˜„์„ฑ์— ๋Œ€ํ•œ ์ถ”์ •๊ฐ’์„ ์ œ๊ณตํ•˜๋Š” ์ ‘๊ทผ๋ฐฉ๋ฒ•์œผ๋กœ ์ธก์ •์‹œ์Šคํ…œ์˜ ๋ณ€๋™์„ ๋ฐ˜๋ณต์„ฑ ๋ฐ ์žฌํ˜„์„ฑ์˜ ๋‘ ์š”์†Œ๋กœ ๋ถ„๋ฆฌํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•ด์ฃผ๋‚˜ ๋‘ ์š”์†Œ์˜ ๊ตํ˜ธ์ž‘์šฉ์€ ๋”ฐ๋กœ ๋ถ„๋ฆฌํ•˜์ง€ ๋ชปํ•จ.

โ…ก. Average & Range Method - Data ํ‘œ

โ…ก. Average & Range Method - Data ๋ถ„์„

R&R ๊ฒฐ๊ณผ ํ•ด์„ ๋ฐ ์กฐ์น˜ ๊ฒŒ์ด์ง€ ๋ฐ˜๋ณต์„ฑ ๋ฐ ์žฌํ˜„์„ฑ(%R&R)์˜ ์ˆ˜์šฉ์ง€์นจ ์ธก์ •์‹œ์Šคํ…œ ๊ฐœ์„ ์กฐ์น˜(๊ธฐ์ค€ ๋ฏธ๋‹ฌ ์‹œ) 10% ์˜ค์ฐจ ์ดํ•˜ ; ์ธก์ •์‹œ์Šคํ…œ์€ ์ˆ˜์šฉ ๊ฐ€๋Šฅ 10% ์—์„œ 30% ์˜ค์ฐจ ; ๊ฒŒ์ด์ง€, ๋น„์šฉ, ์ˆ˜๋ฆฌ ๋น„์šฉ, ์ ์šฉ์˜ ์ค‘์š”์„ฑ์— ๋”ฐ๋ผ ์ˆ˜์šฉ 30% ์˜ค์ฐจ ; ์ธก์ • ์‹œ์Šคํ…œ ๊ฐœ์„  ํ•„์š” ๋ถ€ํ’ˆ๋‚ด ์‚ฐํฌ๋ฅผ ํฌํ•จํ•˜์—ฌ ์ธก์ •์‹œ์Šคํ…œ ์‚ฐํฌ ๊ณ„๋Ÿ‰ํ™” ์ธก์ •์‹œ์Šคํ…œ ๊ฐœ์„ ์กฐ์น˜(๊ธฐ์ค€ ๋ฏธ๋‹ฌ ์‹œ) 1) ์ธก์ •๊ธฐ๊ธฐ์˜ ๊ฐœ์„  2) ์ œํ’ˆ์˜ ๊ฐœ์„  3) ์ œํ’ˆ SPEC'์— ๊ทผ์ ‘ํ•œ ์ œํ’ˆ์˜ ์ „์ˆ˜์„ ๋ณ„ 4) ํ•„์š”์‹œ ํŠน์ฑ„ 5) ์ธก์ •์ž ๊ต์œก

์ธก์ •์‹œ์Šคํ…œ ๋Šฅ๋ ฅํŒ๋‹จ ์ง€ํ‘œ ๊ณต์ฐจ ๊ธฐ์—ฌ์œจ (Tolerance) ์ด๋ณ€๋™ ๊ธฐ์—ฌ์œจ (Total Variation) ํŒ๋ณ„๋ ฅ ๊ตฌ ๋ถ„ ๊ณต์ฐจ ๊ธฐ์—ฌ์œจ (Tolerance) ์ด๋ณ€๋™ ๊ธฐ์—ฌ์œจ (Total Variation) ํŒ๋ณ„๋ ฅ (Discrimination) ์ธก์ •์‹œ์Šคํ…œ ๋ณ€๋™ VS ๊ณต์ฐจ๋ฒ”์œ„(Tolerance) ์ธก์ •์‹œ์Šคํ…œ ๋ณ€๋™ VS ๊ณต์ • ์ด ๋ณ€๋™ ๊ตฌ๋ถ„๋œ ๋ฒ”์ฃผ์˜ ์ˆ˜ ์ธก์ •์‹œ์Šคํ…œ ์ฑ„ํƒ < 10% < 10% > 5 ์ค‘์š”์„ฑ๊ณผ ๋น„์šฉ์— ์ขŒ์šฐ๋จ 10% ~ 30% 10% ~ 30% 2~4 (๋ฐ˜๋ณต3ํšŒ ๋ฏธ๋งŒ) ์ธก์ •์‹œ์Šคํ…œ ๊ฑฐ๋ถ€ > 30% > 30% < 2

R&R ์—ฐ์Šต - Data ํ‘œ ์šฐ์ธก์˜ ์ธก์ • DATAํ‘œ๋ฅผ ์ž‘์„ฑํ•˜์‹œ์˜ค. ๋‹จ, D4๋Š” 2.58

R&R ์—ฐ์Šต - ๋ถ„์„์‹œํŠธ ๋‹จ, %R&R ๋ถ„์„์‹œ ๊ณต์ •Variation๊ณผ Tolerance ๋‘˜ ๋‹ค ๊ตฌํ•˜์‹œ์˜ค. K1=1/D2

5. ๊ณ„์ˆ˜ํ˜• ์ธก์ • ์‹œ์Šคํ…œ ํ‰๊ฐ€

๊ณ„์ˆ˜ํ˜• ์ธก์ • ์‹œ์Šคํ…œ ํ‰๊ฐ€ ๊ณ„์ˆ˜ํ˜• ์ธก์ •์‹œํ…œ ์€ ์ธก์ •๊ฐ’์ด ์œ ํ•œ๊ฐœ์˜ ๋ฒ”์ฃผ๋“ค ์ค‘์˜ ํ•˜๋‚˜์ธ ์ธก์ •์‹œ์Šคํ…œ ๋ถ„๋ฅ˜ ์ž„ ์ด ์‹œ์Šคํ…œ์˜ ๊ฐ€์žฅ ์ผ๋ฐ˜์ ์ธ ๊ฒƒ์€ ๋‘ ๊ฐœ์˜ ๊ฐ€๋Šฅํ•œ ๊ฒฐ๊ณผ ๋งŒ์„ ๊ฐ–๋Š” GO/NOGO gage ์ž„ ์ด ๊ณ„์ˆ˜ํ˜• ๊ฒŒ์ด์ง€๋Š” ๋ถ€ํ’ˆ์ด ์–ผ๋งˆ๋‚˜ ์ข‹๊ฑฐ๋‚˜ ๋‚˜์œ์ง€๋ฅผ ๋‚˜ํƒ€๋‚ผ ์ˆ˜ ์—†๊ณ  ๋‹จ์ง€ ๊ทธ ๋ถ€ํ’ˆ์ด ์ˆ˜์šฉ๋  ๊ฒƒ์ธ๊ฐ€ ๊ธฐ๊ฐ ๋  ๊ฒƒ์ธ๊ฐ€ ๋งŒ์„ ๋‚˜ํƒ€๋‚ธ๋‹ค.

๊ณ„์ˆ˜ํ˜• ๊ฒŒ์ด์ง€์˜ ์„ฑ๋Šฅ Ideal Gage Real Gage ํ•ฉ๊ฒฉํ™•๋ฅ  LSL USL LSL USL 100% 75% 50% 25% ์˜ค์ฐจ๊ฐ€ ์—†๋Š” ์ธก์ •์— ๋Œ€ํ•œ ์ด์ƒ์ ์ธ ๊ฒŒ์ด์ง€์„ฑ๋Šฅ๊ณก์„  (GPC : Gage Performance Curve) ํŽธ์˜=0.00 GRR๋ฒ”์œ„ = 0.00 ์ธก์ •๋ถ€ํ’ˆ์˜ ๊ธฐ์ค€๊ฐ’ Ideal Gage Real Gage

๊ณ„์ˆ˜ํ˜• ๊ฒŒ์ด์ง€์˜ ํ‰๊ฐ€ ๋ฐฉ๋ฒ• GO/NO-GO ๊ฒŒ์ด์ง€ ๋‹จ์ˆœ ํ‰๊ฐ€ ๋ฒ• ์œ„ํ—˜ ํ‰๊ฐ€๋ฐฉ๋ฒ• (Risk Analysis Methods) ์ „ ๊ณต์ • ๋ฒ”์œ„๋ฅผ ๋Œ€ํ‘œํ•  ์ˆ˜ ์žˆ๋Š” ๋ถ€ํ’ˆ 20๊ฐœ ์ƒ˜ํ”Œ๋ง 2๋ช…์˜ ํ‰๊ฐ€์ž ๋ฅผ ์„ ์ • ๊ฐ ํ‰๊ฐ€์ž๊ฐ€ 20๊ฐœ์˜ ๊ฐ ๋ถ€ํ’ˆ ์„ 2๋ฒˆ ๊ฒ€์‚ฌ 20๊ฐœ ๋ชจ๋“  ๋ถ€ํ’ˆ์— ๋Œ€ํ•ด 4๊ฐœ(2X2)์˜ ํ‰๊ฐ€๊ฒฐ๊ณผ๊ฐ€ ๋ชจ๋‘ ๋™์ผํ•ด์•ผ ํ•ฉ๊ฒฉ ์œ„ํ—˜ ํ‰๊ฐ€๋ฐฉ๋ฒ• (Risk Analysis Methods) ๊ฐ€์„ค๊ฒ€์ • ๋ถ„์„๋ฐฉ๋ฒ• (Hypothesis Test Analyses โ€“ Cross Tab Methods) ์‹ ํ˜ธ๊ฒ€์ถœ ์ด๋ก  (Signal Detection Theory) ๋ถ„์„์  ๋ฐฉ๋ฒ• (Analytic Method)

GO/NO-GO ๊ฒŒ์ด์ง€ ๋‹จ์ˆœ ํ‰๊ฐ€๋ฒ•-์˜ˆ ํ•œ ๊ฐœ๋ผ๋„ ๊ฐ™์€ ๊ฒƒ์ด ์—†์œผ๋ฉด ๋ถˆํ•ฉ๊ฒฉ ์ฒ˜๋ฆฌํ•จ

์‹ฌํ™” ํ•™์Šต ์ธก์ •์‹œ์Šคํ…œ ๋ถ„์„์ด ์ค‘์š”ํ•œ ์ด์œ ๋Š”? a. ์ธก์ •๊ธฐ์— ๋ˆ์„ ๋” ์†Œ๋น„ํ–ˆ๋‹ค๋Š” ๊ฒƒ์€ ๋”๋‚˜์€ ์ธก์ •๊ธฐ์ด๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. b. %R&R์ด 20%์ด์ƒ์ธ ์ธก์ •๊ธฐ๋Š” ์‚ฌ์šฉํ•  ์ˆ˜ ์—†๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. c. ์ธก์ •๊ธฐ์— ๋Œ€ํ•œ ์˜์‚ฌ๊ฒฐ์ •์ด ํ’ˆ์งˆ์— ์˜ํ–ฅ์„ ๋ฏธ์น  ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. d. ์ธก์ •๊ธฐ์˜ ์‚ฐํฌ(๋ณ€๋™)์„ ์ œ๊ฑฐํ•  ์ˆ˜ ์žˆ๋Š” ํ”„๋กœ์„ธ์Šค์ด๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. 2. R&R ๊ฒฐ๊ณผ ๊ฐ’์ด ํฌ๋‹ค๋ฉด ์ด๊ฒƒ์ด ์˜๋ฏธํ•˜๋Š” ๋ฐ”๋Š” ? a. ๊ด€์ธก๋œ ๊ณต์ •๋ณ€๋™์„ ์‹ค์ œ ๋ณ€๋™๋ณด๋‹ค ํฌ๊ฒŒ ๋‚˜ํƒ€๋‚ธ๋‹ค. b. ๊ด€์ธก๋œ ๊ณต์ •ํ‰๊ท ์„ ์‹ค์ œ ํ‰๊ท ๋ณด๋‹ค ํฌ๊ฒŒ ๋‚˜ํƒ€๋‚ธ๋‹ค. c. ๊ด€์ธก๋œ ๊ณต์ •ํ‰๊ท ์„ ์‹ค์ œ ํ‰๊ท ๋ณด๋‹ค ๋‚ฎ๊ฒŒ ๋‚˜ํƒ€๋‚ธ๋‹ค. d. ๊ด€์ธก๋œ ๊ณต์ •๋ณ€๋™์„ ์‹ค์ œ ๋ณ€๋™๋ณด๋‹ค ๋‚ฎ๊ฒŒ ๋‚˜ํƒ€๋‚ธ๋‹ค. 3. ์ธก์ •์‹œ์Šคํ…œ ํ‰๊ฐ€ ๊ฒฐ๊ณผ๋Š” ์ข…์ข… ๋‹ค์Œ ์ค‘ ์–ด๋А ๊ฒƒ์— ์˜ํ–ฅ์„ ๋ฐ›์„ ์ˆ˜ ์žˆ๋Š”๊ฐ€? a. ์˜จ๋„ b. ์Šต๋„ c. ๋จผ์ง€ d. ์œ„ ์‚ฌํ•ญ ๋ชจ๋‘ 4. GRR์‹œ ๊ฐ€์žฅ ํ•ฉ๋ฆฌ์ ์ธ ๋ถ€ํ’ˆ ๋ฐ ํ‰๊ฐ€์ž์˜ ์ˆ˜๋Š”? a. ๋ถ€ํ’ˆ 5๊ฐœ ๋ฐ ํ‰๊ฐ€์ž 5๋ช… b. ๋ถ€ํ’ˆ 3๊ฐœ ๋ฐ ํ‰๊ฐ€์ž 10๋ช… c. ๋ถ€ํ’ˆ 10๊ฐœ ๋ฐ ํ‰๊ฐ€์ž 3๋ช… d. ๋ถ€ํ’ˆ ๋ฐ ๊ฒŒ์ด์ง€๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ APQP ๋ฐ ๊ฒŒ์ด์ง€ ์กฐ๊ธฐ ์†Œ์‹ฑ ๋™์•ˆ ๊ฒฐ์ •๋œ๋‹ค Answer C A D 5. Gage์˜ ์žฌํ˜„์„ฑ(Reproducibility)์ด๋ž€ ๋ฌด์—‡์ธ๊ฐ€? a. ํ‰๊ฐ€์ž ๊ฐ„์˜ ํŽธ์ฐจ b. ๊ฒŒ์ด์ง€์˜ ์žฌํ˜„ ๋Šฅ๋ ฅ c. ์•Œ๋ ค์ง€์ง€ ์•Š์•˜์œผ๋ฉฐ, ์•Œ ์ˆ˜ ์—†์Œ d. ํ•œ๋ช…์˜ ํ‰๊ฐ€์ž๊ฐ€ ๋™์ผ ๋ถ€ํ’ˆ์„ ์—ฌ๋Ÿฌ ๋ฒˆ ํ‰๊ฐ€ํ•œ ๊ฒฐ๊ณผ

์‹ฌํ™” ํ•™์Šต 6. PV = 1.3์ด๊ณ  GRR = 0.3์ผ๋•Œ ndc๋Š”? a. 4 b. 6.11 c.6 d. ์•Œ ์ˆ˜ ์—†์Œ 7. ๊ฒŒ์ด์ง€ ๋ฐ˜๋ณต์„ฑ์ด๋ž€ ๋ฌด์—‡์ธ๊ฐ€? a. ๋™์ผ ๊ฒŒ์ด์ง€์˜ ๋ฐ˜๋ณต ์‚ฌ์šฉ b. ๋™์ผ๋ถ€ํ’ˆ์„ ์—ฌ๋Ÿฌ ๋ฒˆ ์ธก์ •ํ•  ๋•Œ ์ธก์ •์‹œ์Šคํ…œ ๋‚ด์˜ ๋ณ€๋™์œผ๋กœ ๊ทœ์ •๋œ ์ˆ˜์ค€ (๋•Œ๋กœ๋Š” EV๋ผ๊ณ  ํ•จ) c. ๋™์ผ ์ธก์ •์žฅ์น˜๋ฅผ ์—ฌ๋Ÿฌ ๋ฒˆ ์‚ฌ์šฉ d. ๋™์ผ ๊ฒŒ์ด์ง€๋ฅผ ๋‹ค์‹œ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ๋Šฅ๋ ฅ 8. ์ธก์ • ์‹œ์Šคํ…œ ์ˆ˜๋ช… ์ฃผ๊ธฐ์—์„œ ์ค‘์š”ํ•œ ์š”์†Œ๋Š”? a. ์ธก์ •์ „๋žต ๊ฐœ๋ฐœ b. ๊ฒŒ์ด์ง€ ์†Œ์‹ฑ c. ์ธก์ •์‹œ์Šคํ…œ ๊ฒ€์ฆ d. ์œ„ ์‚ฌํ•ญ ๋ชจ๋‘ 9. GRR์ด๋ž€? a. ์ธก์ •๊ณผ์ •์—์„œ ๋ฐœ์ƒํ•˜๋Š” ๋ชจ๋“  ๋ณ€๋™(Variation)์„ ์ •์˜ํ•œ๋‹ค. b. ํ˜‘๋ ฅ์—…์ฒด๊ฐ€ ์ œ๊ณตํ•˜๋„๋ก ๊ณ ๊ฐ์ด ์š”๊ตฌํ•˜๋Š” ์œ ์ผํ•œ ๊ฐ’์ด๋‹ค. c. ๊ฒŒ์ด์ง€ ๋ฐ˜๋ณต์„ฑ ๋ฐ ์žฌํ˜„์„ฑ์˜ ์•ฝ์–ด์ด๋‹ค. d. ์œ„ ์‚ฌํ•ญ๋“ค ๋ชจ๋‘ Answer 6. C 7. B 8. D 9. C 10. B 10. ์ธก์ • ํŽธ์˜(Bias)๋ž€ ๋ฌด์—‡์ธ๊ฐ€? a. ํ‰๊ท ๊ณผ ๊ธฐ์ค€๊ฐ’(Reference Value) ๊ฐ„์˜ ์ฐจ์ด b. ๋ชจ์ง‘๋‹จ์˜ ๊ด€์ธกํ‰๊ท (Observed Average)๊ณผ ๊ธฐ์ค€๊ฐ’(Reference Value) ๊ฐ„์˜ ์ฐจ์ด c. ๋ชจ์ง‘๋‹จ์˜ ๋ณ€๋™๊ณผ ๊ธฐ์ค€๊ฐ’(Reference Value) ๊ฐ„์˜ ์ฐจ์ด d. ๋‹ต ์—†์Œ

์‹ฌํ™” ํ•™์Šต 11. ์ธก์ •์— ๋Œ€ํ•œ ์œ„ํ—˜ํ•œ ๊ฐ€์ • 3๊ฐ€์ง€๋ฅผ ์“ฐ์‹œ์˜ค 12. ์–ด๋–ค ์กฐ๊ฑด๋“ค์ด ์ธก์ •์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š”๊ฐ€? 13. ์ธก์ •๊ธฐ์˜ ์ •ํ™•๋„์™€ ์•ˆ์ •์„ฑ ๊ฐ„์˜ ์ฐจ์ด๋Š” ๋ฌด์—‡์ธ๊ฐ€? 1.์ธก์ •๊ฒฐ๊ณผ๋Š” ํ•ญ์ƒ ์˜ณ๋‹ค, ์ธก์ •์žฅ๋น„๋Š” ์‹œ๊ฐ„์˜ ํ๋ฆ„ ๋ฐ ์ธก์ •๋ฒ”์œ„์— ๋”ฐ๋ผ ๋ณ€๋™์„ ๊ฐ€์ง€์ง„ ์•Š๋Š”๋‹ค, ๋น„์‹ผ ์ธก์ •๊ธฐ๊ฐ€ ์ข‹๋‹ค, ์ธก์ •์žฅ๋น„๋Š” ์‚ฌ๋žŒ์— ์˜ํ•œ ์˜ํ–ฅ์„ ๋ฐ›์ง€ ์•Š๋Š”๋‹ค, ํ™˜๊ฒฝ์กฐ๊ฑด์€ ์ธก์ • ํ’ˆ์งˆ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€ ์•Š๋Š”๋‹ค, ์ธก์ •์žฅ๋น„์˜ ํ•ด์ƒ๋„๋Š” ์ค‘์š”ํ•˜์ง€ ์•Š๋‹ค 2. ์ธก์ •์ง€์‹, ์ˆ™๋ จ, ์ธก์ • ์ž‘์—…์ž์˜ ํŠน์„ฑ, ๋ถ€ํ’ˆ์˜ ํŠน์„ฑ, ์ธก์ •ํ™˜๊ฒฝ(์˜จ๋„, ์ž๊ธฐ์žฅ, ํ‰ํ–‰๋„, ๋จผ์ง€ ๋“ฑ) 3. ์•ˆ์ •์„ฑ์€ ์‹œ๊ฐ„์— ๋”ฐ๋ฅธ ์ธก์ •๊ธฐ์˜ ์ •ํ™•์„ฑ์ด๋‹ค. ์ •ํ™•๋„๋Š” ๋™์ผ ์ธก์ •๊ธฐ์— ์˜ํ•œ ๋™์ผ ํŠน์„ฑ์˜ ์ธก์ •ํ‰๊ท  ๊ฐ„์˜ ์ฐจ์ด, ์ฆ‰ ์ฐธ๊ฐ’๊ณผ ์ธก์ •ํ‰๊ท  ๊ฐ„์˜ ์ฐจ์ด๋ฅผ ์˜๋ฏธํ•œ๋‹ค 4. ์žฅ๊ธฐ๊ฐ„ ์‚ฌ์šฉ, ๋ถ€์ฃผ์˜ํ•œ ์ทจ๊ธ‰, ๊ณผ๋„ํ•œ ์‚ฌ์šฉ, ์‚ฌ์šฉ์— ์˜ํ•œ ์ธก์ •๊ธฐ ๊ฐ€์˜จ, ์••๋ ฅ๊ณ„ ๋ณ€ํ™” 5. ์•ˆ์ •์„ฑ, ์„ ํ˜•์„ฑ 14. ์ธก์ •๊ธฐ์˜ ์•ˆ์ •์„ฑ ๋ฌธ์ œ๋ฅผ ์•ผ๊ธฐํ•  ์ˆ˜ ์žˆ๋Š” ๊ฒƒ์€ ๋ฌด์—‡์ธ๊ฐ€? 15. ์‹œ๊ฐ„์— ๋”ฐ๋ฅธ ๊ฒŒ์ด์ง€์˜ ์ •ํ™•๋„(ํŽธ์˜)๋Š” ________________ ์ด๋‹ค; ์ธก์ •๊ธฐ์˜ ์ธก์ •๊ตฌ๊ฐ„๋ณ„ ์ •ํ™•๋„์˜ ์ฐจ์ด๋ฅผ ___________________๋ผ ํ•œ๋‹ค.

6. ๋ชจ๋ฒ” ์‚ฌ๋ก€ Facilitator says These seven topic areas will be the focus during this course: The course must begin with some basic definitions and history. There will be discussion on ways to measure, interpret, and control variation. Participants will construct and use the various tools in the Quality Control โ€œToolboxโ€. Since processes are subject to change, the need to track their behavior through time will be emphasized. Time will be spent making and interpreting control charts. Participants will practice evaluating processes. Participants will learn that knowing how โ€œgoodโ€ measurement systems are is vital before making judgements based upon the measurements taken.

์ธก์ •์‹œ์Šคํ…œ ํ‰๊ฐ€ ํ”„๋กœ์„ธ์Šค

์ธก์ •์‹œ์Šคํ…œ ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•œ ์ค€๋น„ ํ‘œ๋ณธ๋“ค์€ ๊ณต์ •์—์„œ ์ฑ„์ทจํ•œ๋‹ค --- ์ „์ฒด์ƒ์‚ฐ ์šด์˜ ๋ฒ”์œ„๋ฅผ ๋Œ€ํ‘œ ํ•ด์•ผ ํ•จ. ํ‘œ๋ณธ๋“ค์€ ํ•˜๋ฃจ์— ํ•˜๋‚˜์˜ ํ‘œ๋ณธ์„ ์—ฌ๋Ÿฌ ๋‚ ์— ๊ฑธ์ณ ์ทจํ•จ์œผ๋กœ์จ ๋ถ€ํ’ˆ๋ถ„์„์—์„œ ๊ณต์ •์—์„œ์˜ ์ƒ์‚ฐ๋ณ€๋™ ๋ฒ”์œ„๋ฅผ ๋Œ€ํ‘œํ•œ๋‹ค. 3. ๊ฐ ๋ถ€ํ’ˆ์€ ์—ฌ๋Ÿฌ ์ฐจ๋ก€ ์ธก์ •๋จ์œผ๋กœ ๊ตฌ๋ณ„์„ ์œ„ํ•ด ๋ฒˆํ˜ธ๋ฅผ ๋ถ€์—ฌํ•œ๋‹ค. 4. ์ธก์ •๊ธฐ๋Š” ์ ์–ด๋„ ํŠน์„ฑ์˜ ๊ธฐ๋Œ€ ๊ณต์ •๋ณ€๋™์˜ 1/10 ๊นŒ์ง€ ์ง์ ‘ ์ฝ์„ ์ˆ˜ ์žˆ๋Š” ํŒ๋ณ„๋ ฅ์ด ์žˆ์–ด์•ผ ํ•œ๋‹ค. (์˜ˆ : ํŠน์„ฑ๋ณ€๋™์ด 0.001์ด๋ผ๊ณ  ํ•˜๋ฉด ์ธก์ •์žฅ๋น„๋Š” 0.0001์˜ ๋ณ€๋™์„ ์ฝ์„ ์ˆ˜ ์žˆ์–ด์•ผ ํ•œ๋‹ค) ์ธก์ •์ž์˜ ์ˆ˜, ํ‘œ๋ณธ๋ถ€ํ’ˆ์˜ ์ˆ˜, ๋ฐ˜๋ณต์ธก์ •ํšŸ์ˆ˜ ๋“ค์„ ๋ฏธ๋ฆฌ ๊ฒฐ์ •ํ•œ๋‹ค. ์ธก์ •์ž ๋“ค์€ ์ธก์ •๊ธฐ๋ฅผ ์šด์˜ํ•˜์—ฌ ์ •๊ทœ์ž‘์—…์ž๋“ค๋กœ ์„ ์ •ํ•œ๋‹ค. ์ธก์ •์€ ๋žœ๋ค ํ•œ ์ˆœ์„œ๋กœ ์‹ค์‹œ โ€“ ์ง€์‹์— ์˜ํ•œ ํŽธ์˜๋ฅผ ์ œ๊ฑฐ ํ•˜๊ธฐ ์œ„ํ•ด ์ธก์ •์ž ๋“ค์€ ์–ด๋–ค ๋ฒˆํ˜ธ์˜ ๋ถ€ํ’ˆ์„ ์ธก์ •ํ•˜๋Š”์ง€ ๋ชฐ๋ผ์•ผ ํ•œ๋‹ค. ์•„๋‚ ๋กœ๊ทธ ์ธก์ •๊ธฐ๋Š” ์ตœ์†Œ๋ˆˆ๊ธˆ ๋˜๋Š” ๋ฏผ๊ฐ๋„ ๋ฐ ๋ถ„ํ•ด๋Šฅ๋ ฅ์€ ํ•œ๊ณ„์น˜์˜ ยฝ ์— ๋งž์ถ”์–ด ๊ธฐ๋ก ๋˜์–ด์•ผ ํ•œ๋‹ค. (์˜ˆ: ์•„๋‚ ๋กœ๊ทธ ์ธก์ •๊ธฐ์˜ ์ตœ์†Œ๋ˆˆ๊ธˆ์ด 0.01โ€ ์ด๋ฉด ์ธก์ •๊ฐ’์€ 0.005โ€ ๊นŒ์ง€ ๊ธฐ๋ก๋˜์–ด์•ผ ํ•œ๋‹ค) ์ธก์ •์‹œ์Šคํ…œ์˜ ์ตœ์ข…์ ์ธ ์ˆ˜์šฉ์€ ํ•œ๋ฒˆ์˜ ์ง€ํ‘œ๋“ค์— ๋งž์ถ”์–ด์„œ๋Š” ์•Š ๋˜๋ฉฐ ์‹œ๊ฐ„์„ ๋‘๊ณ  ๋„ํ‘œ๋ถ„์„์„ ํ†ตํ•ด ๊ฒ€ํ†  ๋˜์–ด์•ผ ํ•œ๋‹ค.

SPC Statistical Process Control 7. GRR ์‹ค์Šต

์‹ค์Šต ์„ค๋ช… ๊ฐ ๊ต์œก์ƒ 5๋ช…์”ฉ ํŒ€์„ ๊ตฌ์„ฑํ•œ๋‹ค. 3๋ช…์„ ํ‰๊ฐ€์ž(๊ฒ€์‚ฌ์ž)๋กœ ์„ ์ •ํ•˜๊ณ , 1๋ช…์€ ์ธก์ • Data ๊ธฐ๋ก์ž 1๋ช…์€ ๊ฒ€์‚ฌ ์šด์˜์ž๋ฅผ ์ •ํ•œ๋‹ค. 3. ๊ฒ€์‚ฌ์šด์˜์ž๋Š” 10๊ฐœ์˜ ๋ถ€ํ’ˆ์„ ํ‰๊ฐ€์ž๊ฐ€ ์•Œ ์ˆ˜ ์—†๋„๋ก ๋ฒˆํ˜ธ๋ฅผ ๋ถ€์—ฌํ•˜์—ฌ 30ํšŒ (10๊ฐœ ๋ถ€ํ’ˆ X 3ํšŒ ๋ฐ˜๋ณต)๋ฅผ ๋žœ๋คํ•˜๊ฒŒ ์ธก์ •ํ•˜๋„๋ก ๊ฒ€์‚ฌ์ˆœ์„œ๋ฅผ ๊ด€๋ฆฌํ•œ๋‹ค. Data ๊ธฐ๋ก์ž๋Š” ๊ฒ€์‚ฌ์šด์˜์ž๊ฐ€ ๋ถˆ๋Ÿฌ์ฃผ๋Š” Data๋ฅผ ํ‰๊ฐ€์ž ๋ชจ๋ฅด๊ฒŒ ๋ฐ์ดํ„ฐ ํ‘œ์— ๊ธฐ๋กํ•œ๋‹ค. 5. Data ํ‘œ ์ž‘์„ฑ์ด ์™„๋ฃŒ๋œ ํ›„, GRR ๋ถ„์„์„ ์ˆ˜ํ–‰ํ•˜๊ณ  ์ธก์ •์‹œ์Šคํ…œ์„ ํŒ์ • ํ•œ๋‹ค.

R&R ์‹ค์Šต - Data ํ‘œ 2ํšŒ ์ธก์ • ์ธ ๊ฒฝ์šฐ D4=3.27 3ํšŒ ์ธก์ • ์ธ ๊ฒฝ์šฐ D4=2.58

R&R ์‹ค์Šต - ๋ถ„์„์‹œํŠธ1

R&R ์‹ค์Šต - ๋ถ„์„์‹œํŠธ2

SPC Statistical Process Control

SPC ์šด์˜ ์ฒด๊ณ„ 1.ํ•ต์‹ฌํŠน์„ฑ ํŒŒ์•…์„์œ„ํ•œ ๋ฐ์ดํƒ€์˜ ์ˆ˜์ง‘ 2.ํ•ต์‹ฌํŠน์„ฑ ์„ ์ • 3.์ค‘์š”๊ณต์ • ๊ฒฐ์ • 4.ํ•ต์‹ฌํŠน์„ฑ ๋ฐ ๊ด€๋ฆฌ ๋ฐฉ๋ฒ•์˜ ๋ฌธ์„œํ™” 6.ํ•ต์‹ฌํŠน์„ฑ์ด ํ†ต๊ณ„์  ๊ด€๋ฆฌ ์ƒํƒœ์ธ๊ฐ€? 8.์ด์ƒ์›์ธ์— ์˜ํ•œ ์‚ฐํฌ์˜ ์š”์ธ์ด ํŒŒ์•…๋˜์—ˆ๋Š”๊ฐ€? ์•„๋‹ˆ์˜ค 5.์ธก์ • ๋ฐ ๊ด€๋ฆฌ๋„์ž‘์„ฑ ์˜ˆ 9.์‚ฐํฌ์˜ ์ด์ƒ ์›์ธ ์ œ๊ฑฐ ์˜ˆ ์•„๋‹ˆ์˜ค 10.์ธก์ • ์‹œ์Šคํ…œ ๋ถ„์„์ด ์‹ค์‹œ ๋˜์—ˆ๋Š”๊ฐ€? 7.ํ•ต์‹ฌํŠน์„ฑ ์˜ ๊ณต์ •๋Šฅ๋ ฅ์€ ์ถฉ๋ถ„ํ•œ๊ฐ€? 16.๊ณต์ •์ •๋ณด ๊ด€๋ฆฌ ์•„๋‹ˆ์˜ค 11.์ธก์ •์‹œ์Šคํ…œ ๋ถ„์„ ์‹ค์‹œ ์•„๋‹ˆ์˜ค ์˜ˆ 12.์ธก์ •์‹œ์Šคํ…œ ์— ์‹œ์ •์กฐ์น˜๊ฐ€ ์ทจํ•ด ์กŒ๋Š”๊ฐ€? 13.๊ณต์ • ์‚ฐํฌ์˜ ์ž ์žฌ์›์ธ ํŒŒ์•… ์˜ˆ ์•„๋‹ˆ์˜ค 14.๊ณต์ •๋ณ€๋™ ์š”์ธ๊ณผ ํ•ต์‹ฌํŠน์„ฑ์˜์ƒ๊ด€๊ด€๊ณ„ ๋ถ„์„ 15.์ค‘์š”๊ณต์ • ๋ณ€์ˆ˜์— ๋Œ€ํ•œ ๊ด€๋ฆฌ๋ฐฉ๋ฒ• ์ˆ˜๋ฆฝ

SPC ์šด์˜ ์ฒด๊ณ„ SPC ์šด์˜์‹œ ์ฃผ์š” ๊ณ ๋ ค์‚ฌํ•ญ ๊ฐœ์„ ์˜ ์‹คํ–‰์„ ์ „์ œ๋กœํ•œ ์ž๋ฃŒ ์ˆ˜์ง‘ ๋ฐ ํ†ต๊ณ„์  ๊ธฐ๋ฒ• ์‚ฌ์šฉ ์ œํ’ˆ๊ด€๋ฆฌ๊ฐ€ ์•„๋‹Œ ๊ณต์ •๊ด€๋ฆฌ ์‚ฌ์ „ ๊ณ ๋ ค์‚ฌํ•ญ ์ ์šฉ์‹œ ๊ณ ๋ ค์‚ฌํ•ญ ๊ฐœ์„ ์˜ ์‹คํ–‰์„ ์ „์ œ๋กœํ•œ ์ž๋ฃŒ ์ˆ˜์ง‘ ๋ฐ ํ†ต๊ณ„์  ๊ธฐ๋ฒ• ์‚ฌ์šฉ ์ œํ’ˆ๊ด€๋ฆฌ๊ฐ€ ์•„๋‹Œ ๊ณต์ •๊ด€๋ฆฌ ์‹ค์ œ๊ณต์ •๊ณผ ์—ฐ๊ณ„ํ•˜์—ฌ ์ด๋ก  ์ ์šฉ ์ธก์ •์‹œ์Šคํ…œ ๊ด€๋ฆฌ ๊ณต์ •์˜ ์ถฉ๋ถ„ํ•œ ์ดํ•ด ๊ณต์ •์„ ๊ฐ€์žฅ์ž˜ ๋Œ€ํ‘œํ•˜๋Š” ์ ์ ˆํ•œ ๋Œ€์šฉํŠน์„ฑ ์„ ์ • ๋ฐ์ดํƒ€ ์ˆ˜์ง‘ ๋ฐฉ๋ฒ• ๋ฐ ํฌ๊ธฐ ํŠน์„ฑ๊ณผ ๊ด€๋ฆฌ๋ฐฉ๋ฒ•์— ๋ถ€ํ•ฉํ•˜๋Š” ํ†ต๊ณ„์  ๊ธฐ๋ฒ• ์„ ์ • ๋ฐ ํ™œ์šฉ ๊ฐ€๋Šฅ์„ฑ ๊ฒ€ํ†  ์ธก์ •์‹œ์Šคํ…œ ์ •๋„ ๋ณด์žฅ SPC ์šด์˜ ์ฒด๊ณ„ ๋ช…ํ™•ํ™”(๋ฌธ์„œํ™”) SPC ๊ฒฐ๊ณผ ๊ณต์ •์— F/BACK ์ง€์†์  ์ ์šฉ์— ๋Œ€ํ•œ ๋ชจ๋‹ˆํ„ฐ๋ง ๋ฐ ์„ฑ๊ณผ์ธก์ • ๋ณ€๊ฒฝ์‚ฌํ•ญ ํ’ˆ์งˆ๋ฌธ์„œ ๋ฐ˜์˜

๋ถ€๋ก ํ‘œ์ค€์ •๊ทœ ๋ถ„ํฌ ํ…Œ์ด๋ธ”

ํ‘œ์ค€ ์ •๊ทœ ๋ถ„ํฌ ํ…Œ์ด๋ธ”

์ˆ˜๊ณ ํ•˜์…จ์Šต๋‹ˆ๋‹ค!