拟肩法织物悬垂性测量的女裙造型预测研究OA

Traditional drape evaluation mainly relies on the Cusick disk method(ISO 9073-9:2008).This method assesses fabric behavior on a planar support but ignores key interactions between fabric and anthropomorphic surfaces,especially the influence of shoulder geometry,garment construction,and cutting direction on fabric drape.Theoretically,this conventional method severely hinders accurate measurement of fabric drape and the ability to predict real garment shapes. To bridge this gap and achieve accurate measurement of fabric drape performance and rapid dress shaping parameters,this paper proposed a fabric drape test method based on the shoulder-fitting method(SFM).This method aimed to characterize drape properties that aligned with human reality by replicating the geometry of the human shoulder and neck.The study selected three representative woven fabrics with different mechanical properties:denim(F1),acetate fabric(F2),and Tencel poplin(F3).These fabrics cover the textile range suitable for dresses.Based on anthropometric parameters of the Chinese standard female size 160∕84A(GB∕T 1335.2-2008),the study fabricated physical(3D-printed resin)and virtual(Style 3D∕Rhino 7)drape testers.Key dimensions included neck length,neck circumference,shoulder width,shoulder slope height,and front shoulder length.By constructing a shoulder-fitting support and conducting model analysis in Style 3D and Rhino,the study designed multiple experiments with short samples(SS),long samples(LS),and full-dresses(D).These experiments systematically investigated drape response behavior under different shoulder angles(20°-40°),shoulder widths(21-42 cm),and shoulder seam types(plain seam,superimposed seam,and overlock seam).The study also established a mapping relationship with real garment shapes.Consequently,the study obtained drape coefficient(DC)and dress shapeing parameters(Pd)in both virtual and real environments.As for DC,a new ratio formula:DC=Δ2∕Δ1 was employed,where Δ1 is the maximum front-back distance of the draped sample divided by the width of the drape tester,and Δ2 is the difference in average sample width before and after draping. Results showed that SFM exhibited significantly higher sensitivity than the disk method.Its coefficient of variation(CV)reached 57.60%in the virtual environment and 46.30%in the real environment.In contrast,the disk method gave 6.86%in the virtual environment and 15.61%in the real environment.Independent t-tests confirmed no significant difference between virtual short samples and physical short samples(p>0.05),validating the digital twin fidelity of the SFM method.Compared with the traditional disk method,DC values measured by SFM showed higher sensitivity and shape recovery performance.Short-sample DC correlated significantly with Pd parameters(maximum r=0.88,p<0.01).Regression models achieved good fit for all three fabric types(R2>0.9).Validation with physical samples of different sizes yielded prediction errors generally below 5%.Moreover,the DC difference between virtual short samples and real short samples was not significant,confirming good DC consistency of the SFM method in digital twin applications.This study established a mapping model from fabric DC to dress shape Pd.The model holds potential for fabric selection,reverse derivation of CAD parameters,and virtual sampling,providing a theoretical basis and practical pathways for digital garment design and intelligent manufacturing.