Supporting Knitwear Design Using Case-Based Reasoning

Organised by: Cranfield UniversityKnitwear design is a creative activity that is hard to automate using the computer. The production of the associated knitting pattern, however, is repetitive, time-consuming and error-prone, calling for automation. Our objectives are two-fold: to facilitate the design and to ease the burden of calculations and checks in pattern production. We conduct a feasibility study for applying case-based reasoning in knitwear design: we describe appropriate methods and show how they can be implemented.Mori Seiki – The Machine Tool Compan

Knitwear customisation as repeated redesign

Producing large numbers of garment variants will only be economically viable if it requires very little human effort. But garment customisation cannot always be fully automated. Applying grading rules maintain the same details but sometimes achieves a different overall effect; but the customer expects the same overall effect and is less concerned about details. Choosing between alternative customisations requires a human designer's trained perceptual judgement. Therefore a viable mass customisation support system must support the repeated redesign of a garment by combining automatic design with fast human editing. Evaluating and modifying the suggestions of others is a natural and efficient activity for designers. This paper describes two prototype automatic design systems exploring techniques that could be used for mass customisation of knitted garments – in which the shape and patterns are indivisibly linked. An early pattern placing system that automatically altered both shape and pattern parameters in a variety of alternative ways. A shape design system that generates technically correct and consistent garment shapes from a set of measurements and a verbal description; it works independently of sizes, recalculating the shape for each new set of measurements. Starting from the system's suggestions, designers can very quickly tweak the new design to fulfil their aesthetic intentions

Design optimisation of air-fed full pressurised suits

This article is a post-print version of the published article which may be accessed at the link below.The JET machine and associated facilities require significant maintenance and enhancement installation activities in support of the experimental exploitation programme. A proportion of these activities are within radiological and respiratory hazardous environments. As such, breathing air-fed one-piece pressurised suits provide workers with protection from the inhalation of both airborne tritium and beryllium dust. The design of these suits has essentially developed empirically. There is a practical necessity to improve the design to optimise worker performance, protection and thermal comfort. This paper details the complexity of modeling the three-dimensional thermofluid domain between the inner surface of the suit and under garments that includes mass as well as heat transfer, suiting geometry, human metabolism and respiration and effects of limb movements. The methods used include computational fluid dynamics (CFD), theoretical adaptations of mixed-phase turbulent flow, profile scanning of a suit and actuating life size mannequin and data processing of the images and experimental validation trials. The achievements of the current programme and collaborations are presented in the paper and future endeavors are discussed.The author gratefully acknowledges the loan of the articulated mannequin from the Defence Science and Technology Laboratories. This work was funded jointly by EPSRC and by the European Communities under the contract of Association between EURATOM and UKAEA. The views and opinions expressed herein do not necessarily reflect those of the European Commission. This work was carried out within the framework of EFDA

Using and producing ideas in computable endogenous growth

It is shown that Paul Romer’s suggestion to model algorithmically the use and production of ideas in an endogenous growth model is formally feasible. Such a modelling exercise imparts a natural evolutionary flavour to growth models. However, it is also shown that the policy implications are formally indeterminate in a precise and effective sense.endogenous growth,algorithmic ideas,computable growth

An Algorithm Based on Wavelet Neural Network for Garment Size Selection

Size fitting problem is a main obstacle to large scale online garment sales.It is the difficult to customers to find the fit garments when they couldn’t try on. In this paper, we present an algorithm base on wavelet neural network to help customer choosing their clothing specifications automatically. After the reasonable wavelet function is selected, we established the model structure and the initial parameters. The wavelet neural network is trained by the body measures and the result of AHP algorithm after normalization. The new data are used to test the network. As a result, the error from wavelet neural network is smaller, and the prediction accuracy is proved than that from the algorithm based on traditional BP network

Evolutionary Strategies for Data Mining

Learning classifier systems (LCS) have been successful in generating rules for solving classification problems in data mining. The rules are of the form IF condition THEN action. The condition encodes the features of the input space and the action encodes the class label. What is lacking in those systems is the ability to express each feature using a function that is appropriate for that feature. The genetic algorithm is capable of doing this but cannot because only one type of membership function is provided. Thus, the genetic algorithm learns only the shape and placement of the membership function, and in some cases, the number of partitions generated by this function. The research conducted in this study employs a learning classifier system to generate the rules for solving classification problems, but also incorporates multiple types of membership functions, allowing the genetic algorithm to choose an appropriate one for each feature of the input space and determine the number of partitions generated by each function. In addition, three membership functions were introduced. This paper describes the framework and implementation of this modified learning classifier system (M-LCS). Using the M-LCS model, classifiers were simulated for two benchmark classification problems and two additional real-world problems. The results of these four simulations indicate that the M-LCS model provides an alternative approach to designing a learning classifier system. The following contributions are made to the field of computing: 1) a framework for developing a learning classifier system that employs multiple types of membership functions, 2) a model, M-LCS, that was developed from the framework, and 3) the addition of three membership functions that have not been used in the design of learning classifier systems

Automated design optimisation and simulation of stitched antennas for textile devices

This thesis describes a novel approach for designing 7-segment and 5-angle pocket and collar planar antennas (for operation at 900 MHz). The motivation for this work originates from the problem of security of children in rural Nigeria where there is risk of abduction. There is a strong potential benefit to be gained from hidden wireless tracking devices (and hence antennas) that can protect their security. An evolutionary method based on a genetic algorithm was used in conjunction with electromagnetic simulation. This method determines the segment length and angle between segments through several generations. The simulation of the antenna was implemented using heuristic crossover with non-uniform mutation. Antennas obtained from the algorithm were fabricated and measured to validate the proposed method.This first part of this research has been limited to linear wire antennas because of the wide range and flexibility of this class of antennas. Linear wire antennas are used for the design of high or low gain, broad or narrow band antennas. Wire antennas are easy and inexpensive to build. All the optimised linear wire antenna samples exhibit similar performances, most of the power is radiated within the GSM900 frequency band. The reflection coefficient (S11) is generally better than -10dB. The method of moment (MoM-NEC2) and FIT (CST Studio Suite 2015) solvers were used for this design. MATLAB is used to as an interface to control computational electromagnetic solvers for antenna designs and analysis. The genetic algorithm procedures were written in MATLAB. The second part of the work focuses on meshed ground planes for applications at 900 MHz global system for mobile communications (GSM), 2.45 GHz industrial, scientific, and medical (ISM) band and 5 GHz wearable wireless local area networks (WLAN) frequencies. Square ground planes were developed and designed using linear equations in MATLAB. The ground plane was stitched using embroidery machines. To examine the effect of meshing on the antenna performance and to normalise the meshed antenna to a reference, solid patch antenna was designed, fabricated on an FR4 substrate. A finite grid of resistors was created for numerical simulation in MATLAB. The resistance from the centre to any node of a finite grid of resistors are evaluated using nodal analysis. The probability that a node connects to each node in the grid was computed. The circuit model has been validated against the experimental model by measurement of the meshed ground plane. A set of measurement were collected from a meshed and compared with the numerical values, they show good agreement.</div

A Global Decision Support System for Garment Manufacturing by Using Genetic Algorithm

In the recent years, each industry has to face the situation of making the decisions from global markets, especially the industries within lower technicality. These industries earn money hardly in the perfectly competitive markets. Sometimes, decision makers have to decide how to allot orders in the different factories because of distinctive requests from individual consumer. It is necessary to find a way to help managers with making a decision and allotting orders effectively. The purpose of this study tries to develop a decision support system (DSS) to help the managers and decision makers of a real garment industry In Taiwan to decide order allocation, and we used genetic algorithm (GA) for analysis tools and results would be showed by visual graphs to assist managers in decision making. By decision support systems, managers and decision makers might decide order allocation quickly and save the costs. Finally, the decision support system results in a visional frame within lowest cost, and managers decide order allocation with effectiveness by graphs. With this information, decision makers might make different decisions in unlike situations for dissimilar goals. The system had developed to be used easily and suitable to the garment industries and other similar manufacturing industries