Description / Abstract:
Introduction
Note: Nothing in this standard supercedes applicable laws and regulations.
Note: In the event of conflict between the English and domestic language, the English language shall take precedence.
Purpose.
This document is used to plan the selection of parts used for Design Validation (DV), Product Validation (PV) and Post-Validation Audit (PVA) testing. The intention is to capture as many sources of design and manufacturing variation as possible in the test parts without increasing currently specified test sample sizes or costs. The sample selection plan derived from this guideline for DV, PV and PVA shall be reviewed and approved by GM Validation Engineer.
Applicability.
The objective of this guideline is to improve a DV, PV or PVA evaluation’s ability to detect a potential cause of failure contributed to variation by strategically selecting parts to test that incorporate a particular set of defined key parameters at the lower specification limit (LSL) and/or upper specification limit (USL); plus, any relevant variation from the production manufacturing process in order to determine if requirements are met.
A key parameter(s) is a critical characteristic that significantly influences the ability of a component, subsystem, system or feature to meet a performance requirement (e.g., case depth, foam hardness, durometer, length, thickness, porosity etc.) Figure 1 shows some graphical examples of the relationship between Loss Function and a Characteristic’s Variation within Specification as defined by Taguchi. These graphs depict a gradual decrease in customer satisfaction resulting from a characteristic value change within specification. This phenomena is dependent upon understanding the loss function and the influence of the characteristic or key parameter variation on that loss function.
In order to verify that performance can be maintained across the acceptable range during DV, an evaluation may require incorporation of these key parameters at the limits based upon the Design Failure Mode and Effects Analysis (DFMEA) potential cause of failure and resulting Risk Priority Level (RPL), prior knowledge of a test driven incident due to variation, history of field issues associated to the parameter, Process Failure Mode and Effects Analysis (PFMEA), and other assessments. In PV the same approach is utilized by considering the influence of the production manufacturing process variables such as multiple plants, lines, tools, cavities, etc. as well as including any affect from the key parameter(s) variation within specification.
Remarks.
The term “Multi-Vari” comes from the Multi-Vari Chart which is a simple graphical tool used in production to detect product differences and their process-related sources. This procedure uses the Multi-Vari logic in which parts are selected non-randomly from process operations called “families of variation”. Upon completion of the various validation tests, the resulting test data is plotted on a Multi-Vari Chart to identify the source(s) of non-conformances, if any. A brief tutorial on constructing the Multi-Vari Chart is provided in 4.2.4.1, Figure 9.
Note: Nothing in this standard supercedes applicable laws and regulations.
Note: In the event of conflict between the English and domestic language, the English language shall take precedence.
Purpose.
This document is used to plan the selection of parts used for Design Validation (DV), Product Validation (PV) and Post-Validation Audit (PVA) testing. The intention is to capture as many sources of design and manufacturing variation as possible in the test parts without increasing currently specified test sample sizes or costs. The sample selection plan derived from this guideline for DV, PV and PVA shall be reviewed and approved by GM Validation Engineer.
Applicability.
The objective of this guideline is to improve a DV, PV or PVA evaluation’s ability to detect a potential cause of failure contributed to variation by strategically selecting parts to test that incorporate a particular set of defined key parameters at the lower specification limit (LSL) and/or upper specification limit (USL); plus, any relevant variation from the production manufacturing process in order to determine if requirements are met.
A key parameter(s) is a critical characteristic that significantly influences the ability of a component, subsystem, system or feature to meet a performance requirement (e.g., case depth, foam hardness, durometer, length, thickness, porosity etc.) Figure 1 shows some graphical examples of the relationship between Loss Function and a Characteristic’s Variation within Specification as defined by Taguchi. These graphs depict a gradual decrease in customer satisfaction resulting from a characteristic value change within specification. This phenomena is dependent upon understanding the loss function and the influence of the characteristic or key parameter variation on that loss function.
In order to verify that performance can be maintained across the acceptable range during DV, an evaluation may require incorporation of these key parameters at the limits based upon the Design Failure Mode and Effects Analysis (DFMEA) potential cause of failure and resulting Risk Priority Level (RPL), prior knowledge of a test driven incident due to variation, history of field issues associated to the parameter, Process Failure Mode and Effects Analysis (PFMEA), and other assessments. In PV the same approach is utilized by considering the influence of the production manufacturing process variables such as multiple plants, lines, tools, cavities, etc. as well as including any affect from the key parameter(s) variation within specification.
Remarks.
The term “Multi-Vari” comes from the Multi-Vari Chart which is a simple graphical tool used in production to detect product differences and their process-related sources. This procedure uses the Multi-Vari logic in which parts are selected non-randomly from process operations called “families of variation”. Upon completion of the various validation tests, the resulting test data is plotted on a Multi-Vari Chart to identify the source(s) of non-conformances, if any. A brief tutorial on constructing the Multi-Vari Chart is provided in 4.2.4.1, Figure 9.
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SAE AMS 5082 : 2012
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