Analysis of the modeling methodologies for predicting the sewing thread consumption

Abstract

Purpose -This paper aims to provide a rapid and accurate method to predict the amount of sewing thread required to make up a garment. Design/methodology/approach -Three modeling methodologies are analyzed in this paper: theoretical model, linear regression model and artificial neural network model. The predictive power of each model is evaluated by comparing the estimated thread consumption with the actual values measured after the unstitching of the garment with regression coefficient R 2 and the root mean square error. Findings -Both the regression analysis and neural network can predict the quantity of yarn required to sew a garment. The obtained results reveal that the neural network gives the best accurate prediction.Research limitations/implications -This study is interesting for industrial application, where samples are taken for different fabrics and garments, thus a large body of data is available. Practical implications -The paper has practical implications in the clothing and other textile-making-up industry. Unused stocks can be reduced and stock rupture avoided. Originality/value -The results can be used by industry to predict the amount of yarn required to sew a garment, and hence enable a reliable estimation of the garment cost and raw material required.

Predicting Sewing Thread Consumption for Chainstitch Using Regression Model

“…In Table 1, the physical characteristics of the threads used. A 5 cm of the seam was produced on Juki chainstitch sewing machine at a speed of 3000 stitches/min on 2,4,6,8 layers of 2/1 twill denim fabric of 300 g/m 2 and 350 g/m 2 at different stitch densities. Number of fabric layers and fabric weight per unit area was used as a single independent parameter in terms of fabric assembly thickness.…”

“…Many researchers studied the effect of various factors influencing sewing thread consumption and also developed different mathematical or geometrical models to estimate the sewing thread consumption for different stitch classes by using different techniques, i.e., fuzzy logic, regression analysis, artificial neural networks etc [1][2][3][4][5][6][7]. Thread consumptions estimation are done by garment manufacturers using various charts based on various tables and formulae which uses different variables and assumptions.…”

Regression model to predict thread consumption incorporating thread-tension constraint: study on lock-stitch 301 and chain-stitch 401

“…Unraveled threads are measured following standard testing methods since they are in-build with crimp. As an example, the French standard NF G07 101 is used to determine the unraveled thread lengths (Jaouadi et al 2006). Though unraveling method gives an accurate value, the stitching operation has to be performed several attempts to improve the accuracy by optimizing sewing parameters.…”

“…Inaccurate thread consumption predictions lead to inaccurate costing and reduction of warehouse utilization for both garment manufacturers and sewing thread manufacturers. Purchase of excess or shortage quantities of thread cones make suppliers to endure those excess or shortage thread quantities, so that both parties undergo inflexible logistic process (Jaouadi et al 2006). In this context sewing thread manufacturers and garment manufacturers are looking for accurate method predicting sewing thread consumption for given stitch lengths so that wastages could be minimized.…”

Artificial Neural Networks and Their Applications in the Engineering of Fabrics

“…Indeed, prediction of the seam strength is very important, especially for parachutes (Oanl et al, 2009). The thread consumption is predicted via an ANN model (Jaouadi et al, 2006), while the seam puckering is evaluated and the sewing thread is optimized through ANN models, respectively (Mak & Li, 2007;Lin, 2004). The prediction of the sewing performance is also possible using ANNs (Hui & Ng, 2005;Hui et al, 2007;Hui & Ng, 2009).…”