How better fault detection methods improve molding quality

December 15, 2009

Statistical process control (SPC) for injection molding consists of setting alarm limits on a few important molding variables for real-time faulty part containment. Multivariate SPC detects process outliers more efficiently by analyzing the correlation structure between multiple dependent variables—often the cause of out-of-spec parts.

Process automation provides significant benefits for injection molding operations. These include improved process consistency, increased productivity, reduced operator interference, and lower labor costs. Unfortunately, quality assurance (QA) can constitute a significant barrier to the implementation of automated processing. Even with highly detailed process characterizations and extensive and continuous monitoring, it may prove difficult or even impossible to achieve the level of process understanding necessary for automated QA.

Figure 1: Comparison of control windows.

Figure 2: Univariate control space.

Figure 3: Multivariate control space.

Figure 4: Setup of the multivariate approach.

Table 1: False alarm calculations.

Table 2: Example DOE for multivariate model building.

This is a particularly difficult problem when injection molding medical and other tight-tolerance parts. Constant sampling is problematic for these processes and contamination considerations can prevent the operator from inspecting the parts.

QA approaches for these processes typically use univariate analysis (UVA) and monitoring methods such as statistical process control (SPC). In SPC, control charts for multiple process and quality variables are created. These charts are individually reviewed to detect out-of-spec parts and to identify any process parameter changes that may improve the quality. Unfortunately, such techniques are not adequate for automated QA and often allow defective parts to be released by the manufacturing operations. Much more effective methodologies for automated QA are available that employ multivariate analysis (MVA).

MVA is a statistical design tool that is effective in dealing with large data sets. It is a relatively new approach for the injection molding industry, but recent research suggests that it can provide significant improvements over existing methods of online fault detection. MVA does not attempt QA predictions based solely on multiple upper and lower single-variable limits, but rather considers the correlation structure and relationships that exist between all of the variables being monitored and modeled. MVA currently is well accepted in numerous other industries.

While different approaches for multivariate analysis exist, one of the most effective for injection molding fault detection is known as principal component analysis. PCA measures the correlation structure among the different observations in a data matrix, and then uses a projection method to provide a set of new latent variables called principal components, which are linear combinations of the various measured process variables. The principal components are created such that they explain as much variation in the data set as possible and allow for improved visualization of an entire system. The output of the PCA analysis results in a reduced set of summary statistics that can be used to more easily evaluate the process and perform QA.

Preventing false alarms
SPC measures the consistency of a process by comparing individual variables to their upper and lower control limits, which are derived from an historical calculation of standard deviation (often three-sigma) above or below the calculated average or range for

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