TAILIEUCHUNG - Dimensioning and Tolerancing Handbook Episode 3 Part 5

Tham khảo tài liệu 'dimensioning and tolerancing handbook episode 3 part 5', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Chapter 21 Predicting Piecepart Quality Dan A. Watson . Texas Instruments Incorporated Dallas Texas Dr. Watson is a statistician in the Silicon Technology Development Group SiTD at Texas Instruments. He is responsible for providing statistical consulting and programming support to the researchers in SiTD. His areas of expertise include design of experiments data analysis and modeling statistical simulations the Statistical Analysis System SAS and Visual Basic for Microsoft Excel. Prior to coming to SiTD Dr. Watson spent four years at the TILearning Institute heading the statistical training program for the Defense and Electronics Group. In that capacity he taught courses in Design of Experiments DOE Applied Statistics Statistical Process Control SPC and Queuing Theory. Dr. Watson has a bachelor of arts degree in physics and mathematics from Rice University in Houston Texas and a masters and . in statistics from the University of Kentucky in Lexington Kentucky. Introduction This chapter expands the ideas introduced in the paper Statistical Yield Analysis of Geometrically TolerancedFeatures presented at the Second Annual Texas Instruments Process Capability Conference Nov. 1995 . In that paper we discussed methods to statistically analyze the manufacturing yield in defects per unit of part features that are dimensioned using geometric dimensioning and tolerancing GD T . That paper specifically discussed features that are located using positional tolerancing. This chapter expands the prior statistical methods to include features that have multiple tolerancing constraints. The statistical methods presented in this paper Show how to calculate defects per unit DPU for part features that haveform and orientation controls in addition to location controls. 21-1 21-2 Chapter Twenty-one Account for material condition modifiers maximum material condition MMC least material condition LMC and regardless of feature size RFS on orientation and location constraints. .

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