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This is the second part of our study on needle heating in heavy industrial sewing. In this part, a finite element analysis (FEA) model is presented. Using a commercial FEA software system, ANSYS, the needle is modeled by a number of 3D bar elements and the sewing process is modeled by a series of time and space dependent boundary conditions. The model considers various important factors such as the needle geometry (including the point angle and point length of the needle), the friction between the needle and the fabric, the friction between the needle eye and the thread, the fabrics’ material property, and the sewing conditions. It can predict needle heating in high accuracy. In order to validate the model, a large number of experiments were conducted, in which the needle temperatures were measured using infrared radiometry. It is found that the simulation results match the experiment results very well. Finally, a number of suggestions to reduce the needle heating are presented.

In heavy industrial sewing, needle heating has become a serious problem that limits the further increase of the sewing speed, and hence the productivity. The high temperature in the needle can degrade the strength of the thread. At the same, it may cause the wear of the needle eye, which would further damage the thread. It can also scorch the fabric, as well as temper and weaken the needle itself. Therefore, it is important to develop a model that can predict the needle heating and, hence, find remedies to minimize its effects. According to a literature survey, most research on needle heating focuses on experimental methods, such as infrared radiometry, infrared pyrometry, etc. This paper is the first part of our research on needle heating. In this paper, two analytical models are presented: the sliding contact model and the lumped variable model. These models are relatively simple and easy to use. Given needle geometry, sewing condition, and fabric characteristic, they can predict the needle temperature rise starting from initial heating to steady state. The simulation results are rather accurate. Hence, the models can be used to quickly identify the potential needle heating problems on the shop floor. In Part 2 of our study, a finite element analysis (FEA) model is presented together with the experiment results.

This paper intends to construct a new adaptive grey seasonal model (AGSM) to promote the application of the grey forecasting model in quarterly GDP.

Firstly, this paper constructs a new accumulation operation that embodies the new information priority by using a hyperparameter. Then, a new AGSM is constructed by using a new grey action quantity, nonlinear Bernoulli operator, discretization operation, moving average trend elimination method and the proposed new accumulation operation. Subsequently, the marine predators algorithm is used to quickly obtain the hyperparameters used to build the AGSM. Finally, comparative analysis experiments and ablation experiments based on China's quarterly GDP confirm the validity of the proposed model.

AGSM can be degraded to some classical grey prediction models by replacing its own structural parameters. The proposed accumulation operation satisfies the new information priority rule. In the comparative analysis experiments, AGSM shows better prediction performance than other competitive algorithms, and the proposed accumulation operation is also better than the existing accumulation operations. Ablation experiments show that each component in the AGSM is effective in enhancing the predictive performance of the model.

A new AGSM with new information priority accumulation operation is proposed.