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

Abstract

Prediction of sewing thread consumption requires an accurate method of calculation since it relates to the cost of manufacturing and distribution of apparel products. Previous researchers highlighted problems in existing thread consumption calculation methods; i.e. limitations in existing formulae which cause inaccurate predictions of thread amount needed for sewing operations. The existing methods of consumption calculations exhibit significant error percentages due to the ignorance of important parameters which affect on thread consumption. This paper investigates on correlation of thread tension to thread consumption of lock-stitch 301 and chain-stitch 401. The existing thread consumption formulae are optimized by considering a new parameter; thread tension, using regression analysis and geometrical modeling techniques. For the chain-stitch 401, results indicate that the thread tension significantly affects in determination of the thread consumption. The error analysis of proposed formulae was performed to indicate that the proposed formulae more accurate compared to the available methods of predicting sewing thread consumption. In addition, there are combined effects of thread tensions together with parameters such as fabric thickness and stitch density which determines accurate consumption values considering the properties of the stitch. In comparison, inclusion of the proposed thread tension variable depicts reduction in error percentages, so that the proposed formulae are expected to be a better approach to calculate thread consumption of lock-stitch 301 and chain-stitch 401.

Development of a color matching algorithm for digital transfer textile printing using an artificial neural network and multiple regression

“…Those values and the initially intended sRGB values are used as input and output values, respectively, to train artificial neural networks and to setup multiple regression equations, which can establish a relationship between input and output values that are assumed to have a nonlinear relationship. 6 A total of 100 random color samples were used to verify the performance of the proposed color matching algorithm. By this method, corrected color values, which can reproduce the target color intended by the designers accurately on the fabric, can be obtained reliably.…”

Artificial neural network and regression models for prediction of sewing thread consumption for multilayered fabric assembly at lockstitch 301 seam

“…The regression model 5 was proposed by considering needle thread as an additional parameter (Abeysooriya and Wickramasinghe, 2014), the general equation of which is represented in equation ( 4) as follows:…”

Predicting Sewing Thread Consumption for Chainstitch Using Regression Model

“…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.…”