Philippe Vroman

A fuzzy multi-criteria group decision support system for nonwoven based cosmetic product development evaluation

Product prototype evaluation is an important phase in new product development (NPD). Such evaluation often requires multiple criteria that are within a hierarchy and a group of evaluators. The evaluation process and these evaluation criteria often involve uncertain and fuzzy data in the weights of these criteria and the judgments of these evaluators. To evaluate nonwoven cosmetic product prototypes, this study first develops a NPD evaluation model, which has evaluation criteria within three levels, based on the features of nonwoven products. It then proposes a fuzzy (multi-level) multi-criteria group decision-making (FMCGDM) method for supporting the evaluation task. A fuzzy multi-criteria group decision support system (FMCGDSS) is developed to implement the proposed method and applied in nonwoven cosmetic product development evaluation.

Visual Quality Recognition of Nonwovens using Wavelet Texture Analysis and Robust Bayesian Neural Network

An approach to recognize the visual quality of nonwovens by combining wavelet texture analysis, Bayesian neural network and outlier detection is proposed in this paper. Nonwoven images (625) of five different grades, 125 of each grade, are decomposed at four levels with wavelet base sym6 and two energy-based features, norm-1 L1 and norm-2 L2, are calculated from wavelet coefficients of each high frequency subband to train and test Bayesian neural network. To detect the outlier in the training set, scaled outlier probability of training set and outlier probability of each sample are introduced. When the nonwoven images are decomposed at level 3, with 500 samples to train the Bayesian neural network, the average recognition accuracy of test set is 98.4%. Experimental results on the 625 nonwoven images indicate that the energy-based features are expressive and powerful in characterizing texture of nonwoven images and the robust Bayesian neural network has excellent recognition performance.

A comparison of robust Bayesian and LVQ neural network for visual uniformity recognition of nonwovens

The visual uniformity recognition of nonwoven materials using image analysis and neural network is a typical application of pattern recognition in textile industry. In this paper, we try to find a solution to this problem by combining the generalized Gaussian density (GGD) model in wavelet domain and two types of neural networks, robust Bayesian and learning vector quantization (LVQ) neural network. The proposed model is constituted with two stages, i.e., texture representation and pattern recognition. For texture representation, each image is decomposed into four levels using the 9-7 bi-orthogonal wavelet base. The wavelet coefficients in each subband are independently modelled by the GGD model. Moreover, taken as textural features, the corresponding scale and shape parameters estimated from the wavelet coefficients distribution with the maximum likelihood (ML) estimation are extracted in order to train and test the neural network for visual uniformity classification. During the pattern recognition part, robust Bayesian neural network and LVQ neural network are used as classifier. Especially, the experiments based on robust Bayesian neural network are taken as the key point. Experimental results indicate that the robust Bayesian neural network perform superiorly.

Forecasting End-Uses of Nonwovens by Integrating Measured Data and Human Expertise

This paper, which deals with the forecasting of nonwovens end-uses, is divided in two parts. The first part presents optimized methods for measuring the structures of nonwovens. The raw data are extracted directly from 3D images of the accurate topographic surfaces of the materials and also from other instruments. Next, data analysis techniques are applied to select relevant structural parameters and forecast the expected end-uses of nonwovens. Relevant physical features are selected by integrating measured data and the knowledge of experts. The effectiveness of these methods has been shown through a number of nonwoven products designed for filtration.

Determination of Relevant Process Operation Setting Space for Developing Multifunctional Nonwoven-based Products

Owing to various end-uses and good performance/cost ratio, the multifunctional nonwoven-based products have been significantly developed within a decade. Such products are mostly dedicated to niche or limited markets and are preferably high valued products. Therefore, in order to face the international competition, designers are strongly involved in development of new advanced products in order to satisfy more complex requirements and specifications with limited time and cost. One of the objectives of the design procedure is to define a relevant process operation setting space (acceptable range of design factors) adapted to the selected process. This paper presents an original method for determining such relevant space. The proposed procedure can save a certain amount of trials by investigating interesting zones of process operation settings with a limited experiment design. This procedure permits to support the designers during the learning and the optimization phases.