Abstract
Increasing raw material variability is challenging for many industries since it adversely impacts final product quality. Establishing multivariate specification regions for selecting incoming lot of raw materials is a key solution to mitigate this issue. Two data-driven approaches emerge from the literature for defining these specifications in the latent space of Projection to Latent Structure (PLS) models. The first is based on a direct mapping of good quality final product and associated lots of raw materials in the latent space, followed by selection of boundaries that minimize or best balance type I and II errors. The second rather defines specification regions by inverting the PLS model for each point lying on final product acceptance limits. The objective of this paper is to compare both methods to determine their advantages and drawbacks, and to assess their classification performance in presence of different levels of correlation between the quality attributes. The comparative analysis is performed using simulated raw materials and product quality data generated under multiple scenarios where product quality attributes have different degrees of collinearity. First, a simple case is proposed using one quality attribute to illustrate the methods. Then, the impact of collinearity is studied. It is shown that in most cases, correlation between the quality variable does not seem to influence classification performance except when the variables are highly correlated. A summary of the main advantages and disadvantages of both approaches is provided to guide the selection of the most appropriate approach for establishing multivariate specification regions for a given application.
Highlights
For many manufacturing industries, reaching market standards in terms of product quality is a priority to ensure sales
A simple example considering a single quality attribute is shown to illustrate the methodologies, and to explain the main criteria used for comparing both techniques
The second part provides an overview of the Projection to Latent Structure (PLS) model performance in validation
Summary
For many manufacturing industries, reaching market standards in terms of product quality is a priority to ensure sales. If no corrective action is applied, these fluctuations propagate directly to final product quality This is a real problem for many industries especially those processing bio-based materials using raw materials extracted from natural resources. Ensuring good quality control may attenuate the impact of raw material variability This can be performed in three ways: defining specifications for raw material properties, choosing adequate operating conditions, and characterizing final products for quality (Amsbary, 2013). Defining specifications and acceptance criteria for incoming lots of raw materials is key to achieve high and consistent quality final product. This is a useful tool to determine whether a lot of raw materials is processable, and indicates the risk of not reaching desired quality
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