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.

An integrated knowledge representation model, namely the topology structure behaviour function (TSBF) model, is presented for the computer-aided conceptual design of mechanisms (MCACD) in this paper. The model covers both qualitative and quantitative knowledge representations of generic mechanisms. A class hierarchy consists of the abstract mechanism, the embryo mechanism, and the concrete mechanism is then proposed for object-oriented modelling. Based on the TSBF model, several reasoning techniques are integrated to achieve a relatively comprehensive environment for MCACD. The corresponding reasoning process is mainly based on a backward chaining of solutions representation and retrieval, a forward chaining of compositional behaviour reasoning with constraint propagation and satisfaction, and a forward chaining of type synthesis. Coarse optimizations for certain mechanisms are also integrated on the quantitative level. The applicability of the new model is demonstrated by the conceptual design of a zigzag mechanism.

This paper presents a simple and effective method to solve the position of higher-class Assur groups by means of virtual variable searching. It transforms the higher-class Assur groups into a constraint link, Class II Assur group(s), and virtual driving link(s), defined by the virtual variable. The constraint link is reassembled by one-dimensional searching of the virtual variable, and the potential solutions of the position of the higher-class Assur group are achieved with rapid mathematic convergence. Detailed criteria are set up for complicated higherclass Assur groups, including how to select the virtual driving link and constraint Link, and how to decide the solving sequence of converted Class II Assur groups. A versatile visual program has been developed to simulate higher-class planar mechanisms. Finally, an example of the feeding mechanism of a multifunction domestic sewing machine demonstrates the new method.

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Extracting entities and relations from unstructured text is the basic task of building knowledge graphs. Various methods based on sequence labeling have been proposed recently, but most of them use uniform token representations to identify entities. It is clear that the first and last words of the head and tail entities should have different representations. For this reason, we propose a method based on word interaction that provides different representations for the first and last words of entities. Specifically, initial token representation is obtained by BERT. Each token interacts with the next k tokens for the entity’s first token recognition, interacts with the previous k tokens for the entity’s last token recognition, and merges them to obtain the head entity. Then recognize the tail entities based on the head entities and form entity pairs. Finally, get the triples based on a simple multi-classification for relation determination. Extensive experimental results on four datasets show that THT performs better than state-of-the-art baselines.