Solder paste inspection: process control for defect reduction

2002 ◽  
Author(s):  
D. Burr
Keyword(s):  
Process Control ◽  
Solder Paste ◽  
Defect Reduction ◽  
Inspection Process

Standardization of CMM Fitting Algorithms and Development of Inspection Maps for Use in Statistical Process Control

2011 ◽  
Author(s):  
Neelakantan Mani ◽  
Jami J. Shah ◽  
Joseph K. Davidson
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Principal Component ◽  
Statistical Control ◽  
Statistical Process ◽  
Fitting Algorithm ◽  
Inspection Process ◽  
Feature Fitting ◽  
The Individual ◽  
Intelligent Methods

The choice of fitting algorithm in CMM metrology has often been based on mathematical convenience rather than the fundamental GD&T principles dictated by the ASME Y14.5 standard. Algorithms based on the least squares technique are mostly used for GD&T inspection and this wrong choice of fitting algorithm results in errors that are often overlooked and leads to deficiency in the inspection process. The efforts by organizations such as NIST and NPL and many other researchers to evaluate commercial CMM software were concerned with the mathematical correctness of the algorithms and developing efficient and intelligent methods to overcome the inherent difficulties associated with the mathematics of these algorithms. None of these works evaluate the ramifications of the choice of a particular fitting algorithm for a particular tolerance type. To illustrate the errors that can arise out of a wrong choice of fitting algorithm, a case study was done on a simple prismatic part with intentional variations and the algorithms that were employed in the software were reverse engineered. Based on the results of the experiments, a standardization of fitting algorithms is proposed in light of the definition provided in the standard and an interpretation of manual inspection methods. The standardized fitting algorithms developed for substitute feature fitting are then used to develop Inspection maps (i-Maps) for size, orientation and form tolerances that apply to planar feature types. A methodology for Statistical Process Control (SPC) using these i-Maps is developed by fitting the i-Maps for a batch of parts into the parent Tolerance Maps (T-Maps). Different methods of computing the i-Maps for a batch are explored such as the mean, standard deviations, computing the convex hull and doing a principal component analysis of the distribution of the individual parts. The control limits for the process and the SPC and process capability metrics are computed from inspection samples and the resulting i-Maps. Thus, a framework for statistical control of the manufacturing process is developed.

New Rule to Incorporate Inspection Errors in Process Control Using Markov Chains

2020 ◽  
Vol 28 (01) ◽  
pp. 2150002
Author(s):  
William S. Griffith ◽  
Michelle L. Depoy Smith
Keyword(s):  
Process Control ◽  
Markov Chains ◽  
Inspection Errors ◽  
Inspection Process ◽  
Online Process ◽  
Number Of Authors

A number of authors have considered the procedure of online process control in which every [Formula: see text]th item produced is inspected. The process is judged to be in control and continues if the inspected item is judged to conform to specifications. If the inspected item is judged to be nonconforming, then the process is judged to be out of control and stopped, examined, and adjusted as necessary. This inspection process is subject to misclassification and different schemes have been suggested for dealing with this. In this paper, we propose a new rule in which the final judgment that an item is conforming, and thus the process is in control, if and only if [Formula: see text] consecutive classifications as conforming occur before a total of [Formula: see text] classifications as nonconforming. We will examine various aspects of this protocol and provide advice on how to determine values of [Formula: see text] and [Formula: see text] for this rule.

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