An Applied Statistical Reliability Analysis of the Modulus of Elasticity and the Modulus of Rupture for Wood-Plastic Composites

Wood-plastic composites (WPC) are materials comprised of wood fiber within a thermoplastic matrix and are a growing and important source of alternative wood products in the forest products industry. WPC is gaining market share in the building industry because of durability/maintenance advantages of WPC over traditional wood products and because of the removal of chromated copper arsenate (CCA) pressuretreated wood from the market. The reliability methods outlined in this thesis can be used to improve the quality of WPC and lower manufacturing costs by reducing raw material inputs and minimizing WPC waste. Statistical methods are described for analyzing both tensile strength and bending measures of WPC. These key measures include stiffness (tangent modulus of elasticity: MOE) and flexural strength (modulus of rupture: MOR) results from both tensile strength and bending tests. As with any real data analysis, the possibility of outliers is assessed and addressed. With this data, different WPC subsets are evaluated with and without the presence of a coupling agent. Separate subsets without outliers are also reviewed. Descriptive statistics, histograms, probability plots, and survival curves from these test data are presented and interpreted. To provide a more objective assessment of appropriate parametric modeling, Akaike’s Information Criterion is used in conjunction with probability plotting. Selection of the best underlying distribution for the data is an important result that may be used to further explore and analyze the given data. In this thesis, these underlying distributional assumptions are utilized to better understand the product’s lower percentiles.

These lower percentiles provide practitioners with an evaluation of the product’s early failures along with providing information for specification limits, warranty, and cost analysis. Estimation of lower percentiles is sometimes difficult, since substantive data is often sparse in the lower tails. Bootstrap techniques provide important solutions for confidence interval assessments of these percentiles. Bootstrapping is a computer intensive resampling method that may be used for both parametric and nonparametric models. This thesis briefly describes several bootstrapping methods and applies these methods to appraise MOE and MOR test results on sampled WPC. The reliability and bootstrapping methods outlined in this thesis may directly benefit WPC manufacturers through a better evaluation of strength and stiffness measures, which can lead to process improvements with enhanced reliability, thereby creating greater manufacturer and customer satisfaction.