Predicting physical properties of woven fabrics via automated machine learning and textile design and finishing features

This paper presents a novel Machine Learning (ML) approach to support the creation of woven fabrics. Using data from a textile company, two CRoss-Industry Standard Process for Data Mining (CRISP-DM) iterations were executed, aiming to compare three input feature representation strategies related with fabric design and finishing processes. During the modeling stage of CRISP-DM, an Automated ML (AutoML) procedure was used to select the best regression model among six distinct state-of-the-art ML algorithms. A total of nine textile physical properties were modeled (e.g., abrasion, elasticity, pilling). Overall, the simpler yarn representation strategy obtained better predictive results. Moreover, for eight fabric properties (e.g., elasticity, pilling) the addition of finishing features improved the quality of the predictions. The best ML models obtained low predictive errors (from 2% to 7%) and are potentially valuable for the textile company, since they can be used to reduce the number of production attempts (saving time and costs).This work was carried out within the project “TexBoost: less Commodities moreSpecialities” reference POCI-01-0247-FEDER-024523, co-funded byFundo Eu-ropeu de Desenvolvimento Regional(FEDER), through Portugal 2020 (P2020)

A data-driven intelligent decision support system that combines predictive and prescriptive analytics for the design of new textile fabrics

In this paper, we propose an Intelligent Decision Support System (IDSS) for the design of new textile fabrics. The IDSS uses predictive analytics to estimate fabric properties (e.g., elasticity) and composition values (% cotton) and then prescriptive techniques to optimize the fabric design inputs that feed the predictive models (e.g., types of yarns used). Using thousands of data records from a Portuguese textile company, we compared two distinct Machine Learning (ML) predictive approaches: Single-Target Regression (STR), via an Automated ML (AutoML) tool, and Multi-target Regression, via a deep learning Artificial Neural Network. For the prescriptive analytics, we compared two Evolutionary Multi-objective Optimization (EMO) methods (NSGA-II and R-NSGA-II) when optimizing 100 new fabrics, aiming to simultaneously minimize the physical property predictive error and the distance of the optimized values when compared with the learned input space. The two EMO methods were applied to design of 100 new fabrics. Overall, the STR approach provided the best results for both prediction tasks, with Normalized Mean Absolute Error values that range from 4% (weft elasticity) to 11% (pilling) in terms of the fabric properties and a textile composition classification accuracy of 87% when adopting a small tolerance of 0.01 for predicting the percentages of six types of fibers (e.g., cotton). As for the prescriptive results, they favored the R-NSGA-II EMO method, which tends to select Pareto curves that are associated with an average 11% predictive error and 16% distance.This work was carried out within the project "TexBoost: less Commodities more Specialities" reference POCI-01-0247-FEDER-024523, co-funded by Fundo Europeu de Desenvolvimento Regional (FEDER), through Portugal 2020 (P2020)

Artificial Neural Networks and Their Applications in the Engineering of Fabrics

WOS: 00036372550000

Predicting yarn breaks in textile fabrics: a machine learning approach

In this paper, we propose a Machine Learning (ML) approach to predict faults that may occur during the production of fabrics and that often cause production downtime delays. We worked with a textile company that produces fabrics under the Industry 4.0 concept. In particular, we deal with a client customization requisite that impacts on production planning and scheduling, where there is a crucial need of limiting machine stoppage. Thus, the prediction of machine stops enables the manufacturer to react to such situation. If a specific loom is expected to have more breaks, several measures can be taken: slower loom speed, special attention by the operator, change in the used yarn, stronger sizing recipe, etc. The goal is to model three regression tasks related with the number of weft breaks, warp breaks, and yarn bursts. To reduce the modeling effort, we adopt several Automated Machine Learning (AutoML) tools (H2O, AutoGluon, AutoKeras), allowing us to compare distinct ML approaches: using a single (one model per task) and Multi-Target Regression (MTR); and using the direct output target or a logarithm transformed one. Several experiments were held by considering Internet of Things (IoT) historical data from a Portuguese textile company. Overall, the best results for the three tasks were obtained by the single-target approach with the H2O tool using logarithm transformed data, achieving an R2 of 0.73 for weft breaks. Furthermore, a Sensitivity Analysis eXplainable Artificial Intelligence (SA XAI) approach was executed over the selected H2OAutoML model, showing its potential value to extract useful explanatory knowledge for the analyzed textile domain.This work is supported by the European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project PPC4.0 - Production Planning Control 4.0; Funding Reference: POCI-01-0247-FEDER-069803]

Material relationships: the textile and the garment, the maker and the machine. Developing a composite pattern weaving system

This research brings together the disciplines of woven textile design, zero waste pattern cutting and fashion design to form the Composite Pattern Weaving system; an innovative approach to woven garment design and construction which assimilates textile and garment lay-plan design and construction to produce engineered zero waste and integrally shaped woven garments, containing multiple fabric qualities, from a single length of woven textile. The approach challenges conventional textile and fashion design processes and systems by adopting a holistic and simultaneous approach to the design and production of textile and garment components; facilitating the integration of functional and sustainable design strategies to enhance garment durability and longevity through the implementation of a multi-method lifecycle approach to design. This research adopts the Transitional Design Methodology; an alternative approach of working between traditional and advanced technologies which challenges the constraints of the two modes of production whilst capitalising on their advantages. This cyclical iterative approach emphasises the importance of the relationship between the maker, materials and the machine(s), whilst recognising the potential for a transitional dialogue and knowledge transfer between all aspects of hand and digital production. Employing both modes of production in parallel, the Transitional Design Methodology facilitates a reciprocal relationship whereby concepts, designs and ways of working evolve as the maker moves between modes. Through the production of zero waste woven garment prototypes using hand and digital weaving technologies, the research establishes new integral shaping techniques and woven garment construction methods to minimise material production, consumption and waste, and identifies some of the limitations of fully-fashioned and composite garment weaving. The garment prototypes embody the learning and knowledge derived through the application of the Transitional Design Methodology. They demonstrate the advantages of working iteratively between hand and digital modes of design and construction to produce innovative (and interconnected) design outcomes, to advance skills and processes, and enhance personal practice

MANÜEL ÖZNİTELİK ÇIKARIMI VE DERİN ÖĞRENME KULLANILARAK KUMAŞ YUMUŞAKLIĞI VE BONCUKLANMA DEĞERLERİNİN OBJEKTİF BİR ŞEKİLDE ÖLÇÜLMESİ VE SINIFLANDIRILMASI

Fabric softness is a complex tactile sensation perceived by the user even before the fabrics are worn. Softness is usually the property of surface perceived by touching or pressing a finger on the fabric surface. Fabric friction properties significantly affect the tactile sensation of the garments. The yarn used, the finishing works, and the fabric structure (weaving, knitting, etc.) affect the softness. In addition, the hardness of the water used during washing, washing movements, the amount and content of the detergent and softener used also have permanent effects on the fabric softness. Softness can be evaluated by the jury members with proven effectiveness according to the predetermined scale. Our achievement within the scope of the thesis is to eliminate the differences that may occur as a result of the subjective evaluation, which may arise from qualitative observations by basing the degree of softness evaluated qualitatively on numerical data and to obtain clearer and more precise results by adding quantitative features to the evaluation process. The methodology developed for softness assessment is also applied for another textile deterioration parameter, namely pilling, and its results are also reported.Kumaş yumuşaklığı kumaşların giyilmesinden bile önce kullanıcı tarafından algılanan karmaşık bir dokunma hissidir. Yumuşaklık genellikle kumaşın parmaklarla sıkılması veya preslenmesi ile algılanan yüzey özelliğidir. Kumaş sürtünme özellikleri, giysilerin dokunma duyumlarını büyük ölçüde etkiler. Kullanılan iplik, bitim işleri ve kumaş yapısı (dokuma, örme vb.) yumuşaklığı etkilemektedir. Bunun yanında yıkama sırasında işlem gördüğü su sertliği, yıkama hareketleri, kullanılan deterjan ve yumuşatıcının miktarı ve içeriğinden de etkilenmektedir. Görsel olarak test edilen bir diğer tekstil özelliklerinden olan yumuşaklık, etkinliği kanıtlanmış jüri üyeleri tarafından aşağıdaki skalaya göre değerlendirilebilmektedir. Tez kapsamındaki kazanımımız nitel olarak değerlendirilen yumuşaklık derecesinin, sayısal verilere dayandırılarak, nitel gözlemlerden doğabilecek görsel değerlendirme sonucu oluşacak farklılıkların giderilmesi ve değerlendirme prosesine nicel özellik kazandırarak daha net ve kesin sonuçların elde edilmesidir. Yumuşaklık için geliştirilen metodoloji değerlendirme aynı zamanda başka bir tekstil bozulma parametresi, boncuklanma için de uygulanmış ve sonuçları raporlanmıştır.M.S. - Master of Scienc

Digital laser-dyeing: coloration and patterning techniques for polyester textiles

This research explored a Digital Laser Dye (DLD) patterning process as an alternative coloration method within a textile design practice context. An interdisciplinary framework employed to carry out the study involved Optical Engineering, Dyeing Chemistry, Textile Design and Industry Interaction through collaboration with the Society of Dyers and Colourists. In doing so, combined creative, scientific and technical methods facilitated design innovation. Standardized polyester (PET) knitted jersey and plain, woven fabrics were modified with CO2 laser technology in order to engineer dye onto the fabric with high-resolution graphics. The work considered the aesthetic possibilities, production opportunities and environmental potential of the process compared to traditional and existing surface design techniques. Laser-dyed patterns were generated by a digital dyeing technique involving CAD, laser technology and dye practices to enable textile coloration and patterning. An understanding of energy density was used to define the tone of a dye in terms of colour depth in relation to the textile. In doing so, a system for calibrating levels of colour against laser energy in order to build a tonal image was found. Central to the investigation was the consideration of the laser beam spot as a dots-per-inch tool, drawing on the principles used in digital printing processes. It was therefore possible to utilise the beam as an image making instrument for modifying textile fibres with controlled laser energy. Qualitative approaches employed enabled data gathering to incorporate verbal and written dialogue based on first-hand interactions. Documented notes encompassed individual thought and expression which facilitated the ability to reflect when engaged in practical activity. As such, tacit knowledge and designerly intuition, which is implicit by nature, informed extended design experiments and the thematic documentation of samples towards a textile design collection. Quantitative measurement and analysis of the outcomes alongside creative exploration aided both a tacit understanding of, and ability to control processing parameters. This enabled repeatability of results parallel to design development and has established the potential to commercially apply the technique. Sportswear and intimate apparel prototypes produced in the study suggest suitable markets for processing polyester garments in this way

A creative journey developing an integrated high-fashion knitwear development process using computerized seamless v-bed knitting systems

This PhD applied a participatory action research approach to address the organizational problems that compromise the use of computerized seamless V-bed knitwear systems in the high-fashion knitwear sector. The research is a response to a widely acknowledged conflict between high-fashion design processes and processes by which designs are developed on computerized seamless V-bed knitting systems. The social, organizational, and technical aspects of design and manufacturing using computerized seamless V-bed knitting technology in high-fashion knitwear design were analyzed as a socio-technical system (STS). This approach led to a review of the workflows, tasks and roles; identifying and testing new design and manufacturing processes, design methods, and garment solutions; creating a theory model of a new integrated design process; and developing and testing new design processes, design methods, and fashion design education courses that teach these new fashion knitwear approaches.The research was undertaken using a Shima Seiki WholeGarment® system, a current computerized seamless V-bed knitting design and manufacturing technology. The studio workspace, yarn, use of the Shima Seiki system; involvement in fashion projects, and associate supervision were provided by the Department of Agriculture and Food Western Australia (DAFWA).The research demonstrated a high-fashion knitwear designer can undertake all aspects of managing computerized seamless V-bed knitwear design and production to the completion of 1st sample, the first successful sample of a new fabric or garment, was produced using the computer knit data. This finding was developed into a new integrated design process and design methods that remove most of the problems of computerized seamless V-bed knitting systems in high-fashion and offers additional benefits including reduction in time to market and design costs, and increases in the creative solution space for high-fashion knitwear design.The researcher has called this new role, a ‘designer-interpreter’ to denote a professional knitwear designer with additional training in managing computerized seamless knitting machines. Within the context of ‘designer-interpreter’, this research also established the feasibility of a new form of a ‘post-industrial craft-based one-person knitwear production system’