A prediction method of material consumption for flat-knitted sweaters

Abstract

Purpose

A method for predicting the material consumption of a sweater is presented before it is knitted. It can be achieved with the five basic models combined with the parameters related to the dimensions of the knitting machine and needles. The paper aims to discuss these issues.

Design/methodology/approach

Based on the parameters of the needle bar flat knitting machine, the sweater is modeled with five basic structures. The mathematical expression of each basic structure can be derived with corresponding parameters under some consumptions. In following, the predictive weight of the sweater can be formulated with the expression of the length of the basic structures and the linear density of the yarn.

Findings

To evaluate the performance of the proposed scheme, experiments of three types of sweaters on four different knitting machines are carried out. The results show that the proposed method can achieve the performance with the bias values by percentage ranging from −1.54 to −2.84 percent.

Research limitations/implications

Due to the present limited research, more experiments could not be carried out. To improve the performance and robustness of the proposed method, statistical performance measures such as the statistical mean and variance in massive experiments will be studied in the further research.

Practical implications

The evaluation of the material consumption can be obtained before it is knitted with the known basic parameters related to the machine and yarn.

Originality/value

This paper derives the general expressions of five basic structures based on the corresponding parameters of knitting machine. The predictive weight of the sweater is expressed according to the above basic structures before the sweater is knitted.

Citation

Luo, X., Jiang, G. and Cong, H. (2017), "A prediction method of material consumption for flat-knitted sweaters", International Journal of Clothing Science and Technology, Vol. 29 No. 5, pp. 661-672. https://doi.org/10.1108/IJCST-06-2016-0065