The frictional performance of water-lubricated UHMWPE is influenced by the combination of structural parameters and operating conditions. To improve the efficiency of optimal design of surface texture aimed at improving frictional performance, a novel integration of the Orthogonal Array method (OAM), machine learning (ML) prediction, and Particle Swarm Optimization (PSO) is proposed for predicting and optimizing the coefficient of friction (COF) of copper ball-textured UHMWPE surfaces using a small dataset. In order to reduce manufacturing and testing cost, decrease required training samples for ML algorithm, OAM which could efficiently acquire data set with comprehensive feature information is used to determine the parameters of test samples to generate a small but effective dataset. 25 textured samples based on L16 (4⁴) and L9 (3⁴) are fabricated, with the parameter set determined using OAM. COFs of the samples are tested using RTEC tribo-tester. Trend analysis is conducted to investigate the influence of force, frequency, depth and ellipse axis ratio on COF. Multi-linear Regression (MLR) and Gaussian Process Regression are employed. MLR exhibits better prediction accuracy and is integrated with PSO to minimize COF. The error between the experimental and the theoretical results obtained by the integration method of MLR and PSO is only 1.04%, demonstrating the feasibility of predicting COF and optimizing surface texture using the integrated method with a limited dataset determined by OAM.

A novel topology/shape optimization method, Metamorphic Development, is applied to an axisymmetric thermo-elasticity design problem. Based on solid modeling and finite element analysis, optimal profiles of minimum mass turbine disks are sought by growing and degenerating simple initial structures subject to both response and geometric constraints. Radial stress, axial stress, hoop stress and von Mises stress are analyzed throughout the optimization and a constraint is imposed on von Mises stress everywhere in the disk. The optimal structures are developed metamorphically in specified infinite design domains using both quadrilateral and triangular axisymmetric finite elements. Comparisons are made of the results obtained for different optimization scenarios: (a) with and without thermal loading; (b) with and without centrifugal body forces; (c) with and without a fit pressure on the inner surface of the hub; and (d) operating at different rotational speeds.

This paper introduces Metamorphic Development (MD), a novel topology optimization method for truss structures with discrete member lengths and sizes. The method enables a structure to develop through both growth and degeneration towards an optimal topology, minimizing structural compliance and mass subject to constraints on displacement and stress. In contrast to ground structure based approaches, the optimization can start from a very simple initial structure which defines and connects the applied loads and support points using a minimal number of members. The structure is then developed metamorphically in a specified design domain using truss members of predefined sizes. The examples presented demonstrate the potential of this method to generate innovative solutions to structural design optimization problems in a very computationally efficient manner.