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A response surface methodology (RS M design and an artificial neural network (ANN) were used to model tensile strength based on processing parameters.
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Parameters, including wound contraction, epithelization time and hydroxyproline content were determined using the excision model, whereas tensile strength was measured from the incision model.
For this model, the tensile strength contrast increases by 8 MPa; the average fracture length and width increase by 14.5 and 35%, respectively.
The Pukanszky model for tensile strength of polymer nanocomposites was applied, and the dependency of Z to characteristics of constituents and interphase were explained by contour plots.
The paper presents a probabilistic model characterising tensile strength parallel to grain of Central European spruce boards without longitudinal joints.
In this regard, the simple Pukanszky model for tensile strength of polymer nanocomposites is applied and the dependency of Z to different characteristics of constituents and interphase are illustrated by contour plots.
Accounting for interfacial porosity through an average measure is found to be sufficient to model the tensile strength of boundaries with a 〈1 0 0〉 misorientation axis and many boundaries with a 〈1 1 0〉 misorientation axis.
CI models for tensile strength of tablets based on the formulation design and process parameters have been established.
The present paper consists in developing mathematical models on tensile strength and acoustic emission count of a glass fiber reinforced polyamide.
To this end, this research modeled direct tensile strength of plain concrete and steel fiber-reinforced concrete (SFRC) in Finite Element platform and are evaluated based on experimental investigation.
In this study, Response Surface Methodology (RSM) was successfully employed to establish the time dependent models between Tensile Strength Ratio (TSR) as the response parameter and independent factors such as time and anti-stripping additives (namely hydrated lime and Zycosoil).
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