Development of a conceptual framework with a smart database for fabric sewability

Fabric sewability is an important element in garment manufacturing and has a critical impact on the aesthetic qualities and value of a garment. Garment manufacturers who fail to recognise and apply appropriate sewing practices incur huge inefficiencies in resources which can have both social and economic impact. The focus of this research was to bridge the gap between the human and machine interaction by understanding the fabric handle and creating an automated system to minimise sewing defects and maximise production. In doing this, a smart database was developed to predict lower and upper limits for sewing machine settings based on the mechanical and physical properties of the fabrics. The research further establishes the relationship between the fabric and the performance of the material during the sewing process. A feasibility study was undertaken to generate data on machine settings using woven shirt materials. These lightweight fabrics, with plain weave construction, were chosen as they generally exhibit higher levels of seaming problems during sewing. The relationship between the fabric parameters were examined, by using objective and subjective methods of assessment, to determine the physical and mechanical properties of the material. A technical expert, with extensive years of experience on stitching materials in the apparel industry, was invited to assess the materials and to offer their opinion on the potential sewability and recommend sewing machine parameters to produce a high quality seam. Based on the outcomes from the feasibility study, the research widened to a representative cohort of fabrics and examined the relationship between the mechanical properties and physical characteristics of the fabric and how they influence seam appearance and seam quality. A team of experts with specialist knowledge referred to as the ‘Sewing Parameter Evaluation Committee’ (SPEC) were invited to handle the materials and offer their advice on the machine settings to reduce seam deformation. Kendall’s coefficient of concordance was used to determine the level of alignment between the experts’ ranking of twenty fabrics and their suitability for a defect free seam. It highlighted that there was little agreement with the ranking of fabrics between experts. The fabrics were stitched using a standard lockstitch ISU (Integrated Stitching Unit) sewing machine and all the machine settings were adjusted manually. The expert opinions were collated based on their advice to establish the best possible settings to produce a garment with minimal seam deformation. The fabric intelligent technology system (FIT) was created to store the data and generate reports on machine settings for the sewability of the material by combining the validated SPEC recommendations and the fabric mechanical and physical properties. During the final phase of the project, a second set of experts (different from SPEC), were identified to rank the quality of the seams using the American Association of Textile Colourists and Chemists (AATCC) chart for seam deformation. The crux of this work was to develop a conceptual framework for a sewing machine settings database that would benefit the apparel industry by providing a knowledge based system for the optimisation of seam performance, quality and aesthetic appeal. The outcomes from this study add new knowledge to the body of literature that highlights the significance of fabric sewability in garment manufacturing and the limitations of predictive. The study also contributes to a greater understanding of the behaviour of textile materials during the sewing of garments and the application of machine settings which improve the manufacturing process of sewn seams. The framework underscores the significance of the robust system that reduces seam deformation, increases productivity, and facilitates the overall efficiency of the garment manufacturing process. The implementation of an efficient quality management system (QMS) is vital to the global economy and to the overall well-being of the workforce and this novel framework and system should contribute to the successful implementation of any QM