Product family modeling for mass customization

With growing reliance on modeling in product development, it is imperative to describe product families in a cohesive way. In particular, mass customization calls for a close integration of product life cycle from customer recognition to delivery and services. This paper proposes a triple-view scheme for modeling product families. Technical challenges are discussed by comparing product family modeling with modeling single products. Individual modeling formalisms for different views are discussed. An example of product family modeling in power supply design is presented to illustrate the feasibility and potential of the proposed approach.     

Modeling and Simulation of Porous Elastoviscoplastic Material

Many materials exhibit elasto–visco–plastic behavior when subjected to loadings with certain strain rate. Examples include natural materials such as metals, clays, and soils and manmade materials such as some biomimic materials. Some voids may exist or be introduced in these materials. The effects of the voids on the material response are important in predicting the strength, reliability, and service life of structural systems containing these materials. This paper presents the results of applying a statistical micromechanical approach to describe the macroscopic behavior of elasto–visco–plastic materials containing many randomly dispersed spherical voids. Most existing micromechanics based models are only applicable to monotonic proportional loadings. The limitation is removed by integrating the material model into the framework of continuum plasticity. With the discrete integration algorithm and local return mapping algorithm, the proposed computation method is applicable to any loading and unloading histories and is ready for implementing into finite element analysis.     

Performance comparison between the training method and the numerical method of the orthogonal neural network in function approximation

The orthogonal neural network is a recently developed neural network based on the properties of orthogonal functions. It can avoid the drawbacks of traditional feedforward neural networks such as initial values of weights, number of processing elements, and slow convergence speed. Nevertheless, it needs many processing elements if a small training error is desired. Therefore, numerous data sets are required to train the orthogonal neural network. In the article, a least‐squares method is proposed to determine the exact weights by applying limited data sets. By using the Lagrange interpolation method, the desired data sets required to solve for the exact weights can be calculated. An experiment in approximating typical continuous and discrete functions is given. The Chebyshev polynomial is chosen to generate the processing elements of the orthogonal neural network. The experimental results show that the numerical method in determining the weights gives as good performance in approximation error as the known training method and the former has less convergence time. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 1257–1275, 2004.     

Fabrication and microstructure of the d.c.-magnetron-sputtered YBa2Cu3O7 − x superconducting thin films

Systematic investigations were conducted to determine the effect of deposition conditions on the microstructure and superconducting properties of YBa₂Cu₃O_{7 – x} thin films produced by d.c. magnetron sputtering on (001) MgO substrate. The films were c-axis preferentially oriented with respect to the (001) MgO surface at substrate temperatures of 680–700 C. X-ray diffraction patterns clearly indicated the existence of the c-axis alignment normal to the substrate surface and some second phases. The second phases, including a Cu-rich phase and Y₂O₃, were identified by energy dispersive X-ray spectrometer and the microstructures were analysed by electron and atomic force microscopes.     

Enhanced and Differential Capture of Circulating Tumor Cells from Lung Cancer Patients by Microfluidic Assays Using Aptamer Cocktail

Collecting circulating tumor cells (CTCs) shed from solid tumor through a minimally invasive approach provides an opportunity to solve a long‐standing oncology problem, the real‐time monitoring of tumor state and analysis of tumor heterogeneity. However, efficient capture and detection of CTCs with diverse phenotypes is still challenging. In this work, a microfluidic assay is developed using the rationally‐designed aptamer cocktails with synergistic effect. Enhanced and differential capture of CTCs for nonsmall cell lung cancer (NSCLC) patients is achieved. It is also demonstrated that the overall consideration of CTC counts obtained by multiple aptamer combinations can provide more comprehensive information in treatment monitoring.     

Transparent polyimide/graphene oxide nanocomposite with improved moisture barrier property

Colorless and organo-soluble polyimide (PI) films have been synthesized from an alicyclic dianhydride BCDA and aromatic diamine 3,4′-ODA in the cosolvent of DMAc and GBL via one-step process. The graphene oxide (GO) was mixed with the above PI in DMAc solution to fabricate the PI/GO nanocomposite films. With the addition of only 0.001 wt% of GO in PI matrix, the resultant nanocomposite (PI/GO-0.001) exhibits not only the enhanced resistance to moisture but also retains superior visible light transmission, enhanced mechanical strength, and excellent dimensional stability, simultaneously. The water-vapor-transmission-rate (WVTR) significantly reduced to 30 g mil m⁻² day⁻¹ for this nanocomposite compared to 181 g mil m⁻² day⁻¹ for pure PI. Notably, the PI/GO-0.001 nanocomposite also exhibits low coefficient of thermal expansion (CTE) of 41 ppm °C⁻¹, which is benefited from the homogeneous distribution of ultrathin GO nanosheets in PI matrix.     

Scene-Change Aware Dynamic Bandwidth Allocation for Real-Time VBR Video Transmission Over IEEE 802.15.3 Wireless Home Networks

IEEE 802.15.3, an emerging wireless technology, was designed to provide high-quality multimedia services at home. Dynamic bandwidth allocation for a multimedia connection should be considered in order to achieve higher channel utilization, less buffer and less delay, especially for variable bit rate (VBR) multimedia connections. For real-time VBR videos, the bandwidth requirement should be predicted adaptively for effective channel-time requests. Previously, the adaptive least-mean square (LMS) algorithm with fixed step size was applied to predict channel time requirements due to its simplicity and relatively good performance. However, the performance might degrade when scene changes occurred. In this paper, we modify the variable step-size LMS algorithm and apply it as our predictor (VSSNLMS) so that the prediction errors on scene changes can be effectively reduced. Using the prediction results of VSSNLMS, we propose a dynamic bandwidth-allocation scheme that is scene-change aware and can guarantee the delay bound of real-time VBR videos. Simulation results show that the VSSNLMS predictor is superior to previous LMS-type predictors in performance, and the proposed scheme has better performance in channel utilization, buffer usage, and packet loss     

Applying case-based reasoning for product configuration in mass customization environments

Product variation and customization is a trend in current market-oriented manufacturing environment. Companies produce products in order to satisfy customer's needs. In the customization environment, the R&D sector in an enterprise should be able to offer differentiation in product selection after they take the order. Such product differentiation should meet the requirement of cost and manufacturing procedure. In the light of this, how to generate an accurate bill of material (BOM) that meets the customer's needs and gets ready for the production is an important issue in the intensely competitive market.

The purpose of this study is to reduce effectively the time and cost of design under the premise to manufacture an accurate new product. In this study, the Case-Based Reasoning (CBR) algorithm was used to construct the new BOM. Retrieving previous cases that resemble the current problem can save a lot of time in figuring out the problem and offer a correct direction for designers. When solving a new problem, CBR technique can quickly help generate a right BOM that fits the present situation.