Construction of Garment Pattern Design Knowledge Base Using Sensory Analysis, Ontology and Support Vector Regression Modeling

Wang, Jianping; Zeng, Xianyi; Tao, Xuyuan; Xing, Yingmei; Bruniaux, Pascal

doi:10.2991/ijcis.d.210608.002

Cited by 5 publications

(6 citation statements)

References 32 publications

(36 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [5], the authors proposed an ontology-based knowledge base for user profiles and garment features in apparel recommender systems. In [17], the authors presented a new approach for constructing a garment pattern design knowledge base (GPDKB) using sensory analysis, ontology, and support vector regression (SVR) modeling, to formalize the complete knowledge of garment pattern design. In [6], the authors designed an advanced upcycling method in fashion practice by establishing material inventories and garment ontologies.…”

Section: Modeling Methods For Textile and Apparel Production Resourcesmentioning

confidence: 99%

Knowledge Graph-Embedded Time-Serial-Data-Driven Bottleneck Analysis of Textile and Apparel Production Processes

Wang,

Liu,

2023

Machines

View full text Add to dashboard Cite

There is a lack of high correlation and reuse potential among multiple manufacturing data for textiles and apparel. Moreover, the material flow traceability between production workstations is not clear, making it difficult to detect potential production bottlenecks. This paper proposes a knowledge graph embedded time serial data-driven bottleneck analysis of textile and apparel production processes. Firstly, a dynamic information association model is established to organize global manufacturing information, including the static data and time-series data features. Also, a textile-corpus-oriented knowledge extraction model is designed to construct a time-series knowledge graph for textile and apparel production (TKG4TA). Then, a temporal knowledge-driven production process bottleneck prediction model is presented based on manufacturing knowledge in the textile and apparel industry. Of these, textile knowledge is transformed into embeddings using a graph convolutional network (GCN). In turn, the context-associated information features are learned by the long short-term memory (LSTM) to predict the bottlenecks in the textile and apparel production process. Finally, a typical process flow in a shirt manufacturing workshop is used as a case study. It shows that the F1 value of the proposed method for named entity recognition and relationship extraction is up to 80.3%, and 50.6%, respectively. The performance of the proposed model for bottleneck prediction is improved by 8.2% and 14.92% compared to only the use of GCN or LSTM in the mean absolute error. This model may provide a solid foundation for the temporal knowledge-graph-driven bottleneck analysis of shirt manufacturing.

show abstract

Section: Modeling Methods For Textile and Apparel Production Resourcesmentioning

confidence: 99%

Knowledge Graph-Embedded Time-Serial-Data-Driven Bottleneck Analysis of Textile and Apparel Production Processes

Wang,

Liu,

2023

Machines

View full text Add to dashboard Cite

show abstract

“…Step 2: Since its excellent advantages of tackling the problems of function approximation [50,51], SVR was employed to model the relationships between the length variation of the structural line and the movements of its corresponding controlling points. Initially, we set up five SVR-based adaptation models for each structural line (see Fig.…”

Section: Adjustment Model Of Design Solution (Model 5)mentioning

confidence: 99%

Design of Customized Garments Towards Sustainable Fashion Using 3D Digital Simulation and Machine Learning-Supported Human–Product Interactions

Wang

Tao

Zeng

et al. 2023

Int J Comput Intell Syst

Self Cite

View full text Add to dashboard Cite

This paper put forward a new interactive design approach for customized garments towards sustainable fashion using machine learning techniques, including radial basis function artificial neural network (RBF ANN), genetic algorithms (GA), probabilistic neural network (PNN), and support vector regression (SVR). First, RBF ANNs were employed to estimate the detailed human body dimensions to fulfill consumers’ ergonomics requirements. Next, the GA-based models were developed to generate the formalized design solutions following the consumer profiles (demands). Afterwards, the evaluation model was established to quantitatively characterize the relations between consumer profiles and garment profiles from the generated design solutions. The design solutions would be digitally demonstrated and recommended to the consumer following the evaluation results in descending order. Meanwhile, the PNN-based models were created to predict garment fitness based on virtual try-on. Moreover, the SVR-based self-adjustment mechanism was built to estimate and control garment design parameters according to the consumer’s feedback. Based on these mathematical models, the approach enhances the interactions among digital garment demonstration, the designer’s professional knowledge and the user’s perception to find out the most relevant design solution. The effectiveness of the new approach was verified by a real application case of leisure pants customization. The results show that the proposed method can powerfully support the designers’ quality personalized design solutions for consumers more accurately, fast, intelligently, and sustainably, compared with the existing approaches. More importantly, it also establishes an effective and reliable communication channel and mechanism among consumers, fashion designers, pattern designer, and garment producer.

show abstract

“…Most respondents are Bangladeshi users and foreign buyers [14]. Most of the experiments and interview locations were chosen at the industry lab, where engineers from different industries use their CAD and CAM software [9].…”

Section: Data Assortment and Inspectmentioning

confidence: 99%

An observation toward Computer aided processes in Garments production. Comparison and analysis of CAD/CAM Software in Bangladesh

et al. 2023

View full text Add to dashboard Cite

This research paper inquires about different attributes of Computer-aided processes in garments production. This perspective Research was done by our courageous team from 2021 to 2022. It reveals adequate information on the Garments industry's Intension and criteria for choosing CAD/CAM software. For the sake of this Research, we visited more than 600 industries to gather raw data; Every Industry tried to attend this Research from a different region of Bangladesh willingly. After collecting all raw data from the garments industry. The data of the Garments industry was coordinated by Excel program. Consequently, the data was analyzed and implemented statistically to identify the Garments Industry attribute for satisfaction with CAD/CAM software. This process also detects many challenges and defines and advises a proper solution to the problems that the Garments industry is facing in the current situation. This research paper demonstrates adequate information about the Garment's criteria and demand in purchasing garments Computer-aided software.

show abstract

Construction of Garment Pattern Design Knowledge Base Using Sensory Analysis, Ontology and Support Vector Regression Modeling

Cited by 5 publications

References 32 publications

Knowledge Graph-Embedded Time-Serial-Data-Driven Bottleneck Analysis of Textile and Apparel Production Processes

Knowledge Graph-Embedded Time-Serial-Data-Driven Bottleneck Analysis of Textile and Apparel Production Processes

Design of Customized Garments Towards Sustainable Fashion Using 3D Digital Simulation and Machine Learning-Supported Human–Product Interactions

An observation toward Computer aided processes in Garments production. Comparison and analysis of CAD/CAM Software in Bangladesh

Contact Info

Product

Resources

About