G. Stylios

Modelling the dynamic drape of garments on synthetic humans in a virtual fashion show

Reports the dynamic modelling of garments on synthetic humans. Develops the model based on a physical analogue to a deep shell system for describing and predicting the real 3‐D shape of clothes. Determines the garment motion by fabric deformation energy, gravity and external constraints of the garment, such as collision forces, using the deformable node bar concept. Justifies the model by agreement between real fabric prediction of static and dynamic drapes using our newly developed drape metre. Demonstrates the garment simulation using garments from two different fabrics in a virtual fashion show. Also describes the work on modelling and animating a synthetic female. The advantages of this model are that engineering parameters can be used as model parameters directly and that the model is configured based on the surface co‐ordinate system, which are important for the next generation of fashion CAD systems incorporating virtual fashion shows. This consideration is fundamental in the context of global retailing and becomes an integral part of intelligent textile and garment manufacture. Proposes the consequences of this work in cinema, TV, advertising and in graphics and animation are also important, but does not examine these.

The Concept of Virtual Measurement

This paper discusses the concept of virtual measurement in textiles and describes the development of a virtual 3D fabric drape measurement system. In this system, a physical based model is used to predict the draping performance, static and dynamic drape of a given fabric sample. Fabric mechanical properties are used for simulating the virtual 3D shape of the fabric samples, which produce a time‐variable deformation of the virtual fabric drape. The 3D fabric drape can be observed under any view angle. An algorithm is developed, applied and integrated into the system for carrying out virtual fabric drape measurements in order to evaluate the drapeability of a given fabric. Important fabric aesthetic attributes such as number of fabric folds, fold variation and depth of fold are presented and implemented together with the drape co‐efficient.

The Characterisation of the Static and Dynamic Drape of Fabrics

This paper discusses and establishes the static and dynamic drape of fabrics as an aesthetic property. A new algorithm is introduced, which defines more precisely the drapability of a given fabric. Experimental results show that it is more accurate to describe the different types of fabric-drape performance by using a feature vector, and this is found to be in good agreement with subjective assessment. A computer-based vision system, the Static- and Dynamic-drape-measurement System (M3), was developed and made possible the application of the new algorithm to actual fabric measurement. The results show agreement between aesthetic judgement and subjective assessment, and consequently fabric drape is standardised into four drape grades, ranging from high to low. The M3 is able to relate fabric drape with mechanical properties, simulating intelligence or expert knowledge when property change is needed for achieving a required drape performance.

Thinking sewing machines for intelligent garment manufacture

Quantitative fabric‐needle‐sewing machine interactions at different speeds have been used to construct qualitative rules mapping fabric properties to optimum sewing machine settings for the next generation of “intelligent sewing machines”, using model free estimation. The inference procedures of fuzzy logic have been implemented in a neural network to allow for optimization of output membership functions and subsequently, self‐learning. The technique is successfully applied to industrial lockstitch and overlock sewing machines. Optimum settings were achieved under static and dynamic machine conditions from the properties of difficult fabrics and compensation for mishandling by the operator over the speed range of the sewing machine.

Investigation of Seam Pucker in Lightweight Synthetic Fabrics as an Aesthetic Property Part II: Model Implementation Using Computer ‘Vision’

This paper reports upon the development of a new measuring procedure for seam pucker which is based on computer ‘vision’. The procedure is objective and is focused on the aesthetic property of the seam assembly. The results are presented in two parts; Part I reported on the development of a cognitive model for the measurement of seam pucker and Part II, described here, explains the models implementation by using the computer ‘vision’ system.

An Investigation of the Penetration Force Profile of the Sewing Machine Needle Point

The distributions of the tangential and radial stresses acting on the yam of a fabric during sewing as the sewing needle is inserted into the fabric have been discussed by means of the mechanical principles of elasticity. The influence on the needle penetration force caused by the shape of the cross section and the profile curve at the needle point has also been investigated. It is suggested that five parameters. i.e., the mechanical properties of the textile material k, the variation ratio y of the needle radius, the contacting arc length θ. the frictional coefficient μ and the sewing machine speed v are the main factors that determine the penetration force of the sewing needle. The variation ratio y, or the slope at any point of the needle profile, is the most important factor for needle design. Depending on the analysis of the forces acting on the needle surface, an optimum profile curve has been obtained. If this curve is used as a new sewing needle profile, the penetration force can possibly be red...

A neuro‐fuzzy control system for intelligent overlock sewing machines

A neuro‐fuzzy control model has been devised for the next generation of so‐called “intelligent sewing machines”. The model incorporates discrimination of material characteristics to be stitched by automatic determination of their properties. The fabric/machine interactions at different speeds have been computed in the form of linguistic rules of a fuzzy model and implemented in a neural network to allow for optimization of fuzzy membership functions and, subsequently, self‐learning. The model is successfully applied to an instrumented industrial sewing machine.

The principles of intelligent textile and garment manufacturing systems

Despite the globalization and internalization of competition and surplus of apparel production, high labour costs and other economic pressures, apparel products are still being produced using traditional methods and machinery, the mechanics of which have not fundamentally changed since the seventeenth century, even nowadays when the materials produced are very flexible and diverse in texture and properties. In developing the industry further, the nature of interaction between machinery, fabric and operatives has to be taken into account, and this poses some real problems if one has to put forward realistic solutions for future industrial development. It is therefore important to be able to take into consideration fabric/machine/human interactions during the manufacturing process in order to propose the next generation of manufacturing systems which is much needed in the current apparel industry. Reports on findings in the area of intelligent garment manufacture which is a means of introducing flexibility, quality, production efficiency and maximization of resources to the apparel industry. Primarily emphasizes the importance of fabric properties and their interaction with the whole manufacturing process, the labour force and especially with sewing. In order to achieve this, applies computational intelligence and engineering to research, develop and implement intelligent textile and apparel environments, and introduce desired flexibility into the whole area of textile and apparel processes, especially in terms of quick response (QR) and just in time (JIT).

Modelling the dynamic drape of fabrics on synthetic humans: a physical, lumped‐parameter model

True 3‐D garment design (CAD) systems are fundamental for the next generation of intelligent textile and garment manufacture and retailing. Reports a new approach for modelling fabric. The fabric model is developed based on a physical analogue to a deep shell system for describing and predicting the real 3‐D shape of clothes. The fabric motion is determined by deformation energy, gravity and external constraints, such as collision forces, using the deformable node bar concept. The advantages of this model are that engineering parameters can be used as model parameters directly and that the model is configured based on the surface co‐ordinate system, which is believed to be important as the basis of a powerful fashion CAD system. The model successfully simulated fabric drape and has been implemented on a synthetic female model.

The mechatronic principles for intelligent sewing environments

Abstract The subjective decisions during textile and garment manufacture are mimicked and implemented in the next generation of intelligent apparel manufacturing environments. A Sewability Integrated Environment (SIE) has been devised which consists of three mechatronic systems: the Sewability Prediction System which can automatically predict material problems and advise correction of properties prior to manufacture, the Intelligent Sewing System which can automatically set the optimum static and dynamic sewing parameters of the sewing machinery, and the Safeguard Quality System to ensure high quality and consistency. These systems are integrated to form an on-line intelligent environment which is capable of self-learning (automatic updating). These systems have been designed and developed to enable on-line, automatic measurement of fabric properties such as tension, bending, thickness and compression as well as stitching quality, which are all interconnected with each other. Two different paradigms are implemented: seam pucker, as it is mostly found in woven fabrics, and sewing damage, found in densely knitted fabrics. The operation of these environments is robust and should not require special operational skill. Pilot industrial trials have identified improvements from implementing such systems in production efficiency, flexibility of manufacture quality and design enhancements of products.