A simulation data-driven design approach for rapid product optimization

Traditional design optimization is an iterative process of design, simulation, and redesign, which requires extensive calculations and analysis. The designer needs to adjust and evaluate the design parameters manually and continually based on the simulation results until a satisfactory design is obtained. However, the expensive computational costs and large resource consumption of complex products hinder the wide application of simulation in industry. It is not an easy task to search the optimal design solution intelligently and efficiently. Therefore, a simulation data-driven design approach which combines dynamic simulation data mining and design optimization is proposed to achieve this purpose in this study. The dynamic simulation data mining algorithm—on-line sequential extreme learning machine with adaptive weights (WadaptiveOS-ELM)—is adopted to train the dynamic prediction model to effectively evaluate the merits of new design solutions in the optimization process. Meanwhile, the prediction model is updated incrementally by combining new “good” data set to reduce the modeling cost and improve the prediction accuracy. Furthermore, the improved heuristic optimization algorithm—adaptive and weighted center particle swarm optimization (AWCPSO)—is introduced to guide the design change direction intelligently to improve the search efficiency. In this way, the optimal design solution can be searched automatically with less actual simulation iterations and higher optimization efficiency, and thus supporting the rapid product optimization effectively. The experimental results demonstrate the feasibility and effectiveness of the proposed approach

Design and computational optimization of a flexure-based XY nano-positioning stage

This thesis presents the design and computational optimization of a two-axis nano-positioning stage. The devised stage relies on double parallelogram flexure bearings with under-constraint eliminating linkages to enable motion in the primary degrees-of-freedom. The structural parameters of the underlying flexures were optimized to provide a large-range and high bandwidth with sub-micron resolution while maintaining a compact size. A finite element model was created to establish a functional relationship between the geometry of the flexure elements and the stiffness behavior. Then, a neural network was trained from the simulation results to explore the design space with a low computational expense. The neural net was integrated with a genetic algorithm to optimize the design of the flexures for compactness and dynamic performance. The optimal solutions resulted in a reduction of stage footprint by 14% and an increase in the first natural frequency by 75% relative to a baseline design, all while preserving the same 50mm range in each axis with a factor of safety of 2. This confirms the efficacy of the proposed approach in improving stage performance through an optimization of its constituent flexures.Mechanical Engineerin

A Neural Network Meta-Model and its Application for Manufacturing

International audienceManufacturing generates a vast amount of data both from operations and simulation. Extracting appropriate information from this data can provide insights to increase a manufacturer's competitive advantage through improved sustainability, productivity, and flexibility of their operations. Manufacturers, as well as other industries, have successfully applied a promising statistical learning technique, called neural networks (NNs), to extract meaningful information from large data sets, so called big data. However, the application of NN to manufacturing problems remains limited because it involves the specialized skills of a data scientist. This paper introduces an approach to automate the application of analytical models to manufacturing problems. We present an NN meta-model (MM), which defines a set of concepts, rules, and constraints to represent NNs. An NN model can be automatically generated and manipulated based on the specifications of the NN MM. In addition, we present an algorithm to generate a predictive model from an NN and available data. The predictive model is represented in either Predictive Model Markup Language (PMML) or Portable Format for Analytics (PFA). Then we illustrate the approach in the context of a specific manufacturing system. Finally, we identify future steps planned towards later implementation of the proposed approach