ENGINEERING TOOLS & METHODS
Prerequisites Please be aware that this is not an introductory course. In order to understand the course content, students should have completed the Tolerance Stacks Using GD&T course or equivalent. Topical Outline Importance of statistical stacks • The three assumptions that apply to Worst-case tolerance stacks • The two laws of probability that apply to statistical stacks • Two common probability distribution curves used in statistical stacks • The probability of an assembly of six parts with uniform distributions reaching extreme limits • The probability of an assembly of six parts with normal distributions reaching extreme limits Statistical stacks terminology • Statistics and data • Uniform and normal frequency distributions • Range, mean, and deviation • Variance and standard deviation • Specification limits • Standard normal curve and the Empirical Rule • A Z score and parts per million rejects • Control limits • How CP and CPK relate to a normal distribution • The difference between dependent and independent variables • The Realistic Predicted Limits (RPL) method its assumptions • The Root Sum of Squares (RSS) method and its assumptions • The Motorola Six Sigma Root Sum of Squares method and its assumptions • The Motorola Six Sigma Dynamic Root Sum of Squares (DRSS) method and its assumptions • The Monte Carlo Simulation method and its assumptions • The formulas for and results of using the different statistical stack methods • Three benefits of statistical stacks • Two common reasons why statistical stacks are done The statistical stack form • How to complete the statistical stack form • The four stack consequences that must be considered when doing statistical stacks Common statistical tolerance stacks methods • What a statistical tolerance stack is
• In-process inspection methods that could be used
Course summary
Instructor: Fee: $835
ASME GDTP Senior Certified Trainer
.7 CEUs
URL:
sae.org/learn/content/et2726/
Introduction to Statistical Tolerance Stacks 1 Day | Classroom Seminar I.D.# ET2055 This course teaches an introduction to statistical tolerance stacks, a crucial skill in today’s competitive workplace. Utilizing the expertise of world-renowned GD&T expert Alex Krulikowski, the course includes a brief overview of several terms used in statistical stacks. It explains four methods for applying statistics to tolerance stacks and covers precautions about when and how to use statistics in stacks. Newly acquired learning is reinforced throughout the class with stacks that allow the student to practice applying statistical methods. Each attendee receives a robust collection of learning resources including: • A copy of “Introduction to Statistical Tolerance Stacks” workbook, by Alex Krulikowski • Class handouts Thousands of students have learned GD&T through Alex Krulikow- ski’s textbooks, self-study courses, computer-based training, and online learning center. Students who attend courses like this one walk away with more than knowledge. They gain on-the-job skills because the learning materials are performance-based. Learning Objectives By attending this class, you will be able to: • Define the terminology used with statistical tolerance stacks • Describe common statistical tolerance stacks methods • Calculate statistical tolerance stacks using the RSS method • Calculate statistical tolerance stacks using the realistic method
• Apply the RPL method to statistical tolerance stacks • Apply the Monte Carlo method to tolerance stacks • Describe precautions needed when using statistical tolerance stacks
Who Should Attend This course is valuable for individuals who create or interpret engineering drawings, product and gage designers; process, product, and manufacturing engineers; supplier quality engineers/ professionals; CMM operators; buyers/purchasers; checkers; inspectors; technicians; and sales engineers/professionals.
The RPL statistical stack method • The formula for calculating the RPL factor • A qualified dimension used in the RPL method
• How to do the RPL method using the statistical stacks form • The advantages and disadvantages of the RPL method • Calculating a statistical stacks using the RPL method
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3 ways to get a no-obligation price quote to deliver a course to your company: Call SAE Corporate Learning at +1.724.772.8529 | Fill out the online quote request at sae.org/corplearning | Email us at corplearn@sae.org
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