![]() ![]() These assumptions do not account for the breadth of conditions that exist in typical scenarios found in manufacturing. The limitation of RSS is that it assumes all inputs are normally distributed and all performance characteristics have a linear relationship with the dimension. Statistical analysis answers the question, given the distribution of variation on each dimension what is the probability that my performance characteristic will fall within defined acceptable limits. When you combine the probabilities for each dimension (each separate curve) you get the probability for the total and therefore the distribution curve of the total. Each dimension has its own distribution curve. Tool wear, operator differences, changes in material and environment all contribute to variation in the dimension value. Each dimension will have a unique distribution of values based on the manufacturing process. RSS (Root-Sum Squared) Statistical Analysis does not focus on the extreme values, but focuses on the distribution of the variation for each dimension. Worst-Case answers the question, if I take the maximum range on each input, what is the maximum range for the measurement of interest or stackup? We are therefore dealing with the limits of acceptability and not probability. In a Worst-Case Analysis, each dimension will have a minimum and maximum value that represents the range of acceptability for that dimension. Worst-Case Analysis vs RSS (Root-Sum Squared) Statistical Analysis Understanding Statistical Tolerance Analysis The impact on the product development process can be huge. These decisions not only ensure product quality and performance, but also ensure manufacturability at the right price. ![]() Looser tolerance can be applied to less important features. Critical features will be held to tighter tolerances. Utilizing the insight for variation analysis allows design engineers to allocate tolerance budgets strategically. Goals of Cpk = 1.67 for key features and Cp = 1.33 for non-key features are commonly quoted. The product development process should then become focused on defining and validating part manufacturing and assembly processes that are capable of achieving high producibility levels. Companies can now do full Assembly Variation Analysis with tolerance analysis software.Īssembly variation analysis provides insight required to identify the key part characteristics, (KPCs) that must be controlled in order to produce a product that meets the expectation of the customer. 1D, 2D, and 3D, can be created with no restriction on distribution type or quality level. This method eliminates the limitations stated above. One such method that is incorporated into CETOL 6 Sigma is called the Method of System Moments. Given the limitations of RSS, other methods for calculating assembly variation have been developed. This approach requires distributions to be normal with all parts at the same quality level, i.e. Comparing the assembly standard deviation to the assembly limits allow for the calculation of quality metrics like sigma, % yield, DPMU, etc. Statistical analysis (also called variation analysis) can be used to predict the actual variation of an assembly based on the variation of the part dimensions. Worst-case analysis (also called tolerance stack-up analysis) can be used to validate a design. It is important to understand that the inputs values for a worst-case analysis are design tolerances, but the inputs for a statistical analysis are process distribution moments (e.g., standard deviation). ![]() Instead of summing tolerances, as in worst-case analysis, statistical analysis sums dimension distributions. DISCLOSURE UNDER REGULATION 46 OF SEBI LODR.ADAS (Advanced Driver Assistance System).
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