Yuanhao Yang, Guangyu Chen, Zhuo Luo, Liuqing Huang, Chentong Zhang, Xuetao Luo, Haixiang Luo and Weiwei Yu
The purpose of this study is to prepare thermal transfer ribbons with good alcohol resistance.
Abstract
Purpose
The purpose of this study is to prepare thermal transfer ribbons with good alcohol resistance.
Design/methodology/approach
A variety of alcohol-resistant thermal transfer inks were prepared using different polyester resins. The printing temperature, printing effect, adhesion and alcohol resistance of the inks on the label were studied to determine the feasibility of using the ink for manufacturing thermal transfer ribbons. The ink formulations were prepared by a simple and stable grinding technology, and then use mature coating technology to make the ink into a thermal transfer ribbon.
Findings
The results show that the thermal transfer ink has good scratch resistance, good alcohol resistance and low printing temperature when the three resins coexist. Notably, the performance of the ribbon produced by 500 mesh anilox roller was better than that of other meshes. Specifically, the ink on the matte silver polyethylene terephthalate (PET) label surface was wiped with a cotton cloth soaked in isopropyl alcohol under 500 g of pressure. After 50 wiping cycles, the ink remained intact.
Originality/value
The proposed method not only ensures good alcohol resistance but also has lower printing temperature and wider label applicability. Therefore, it can effectively reduce the loss of printhead and reduce production costs, because of the low printing temperature.
Details
Keywords
Liwei Xu, Guodong Yin, Guangmin Li, Athar Hanif and Chentong Bian
The purpose of this paper is to investigate problems in performing stable lane changes and to find a solution to reduce energy consumption of autonomous electric vehicles.
Abstract
Purpose
The purpose of this paper is to investigate problems in performing stable lane changes and to find a solution to reduce energy consumption of autonomous electric vehicles.
Design/methodology/approach
An optimization algorithm, model predictive control (MPC) and Karush–Kuhn–Tucker (KKT) conditions are adopted to resolve the problems of obtaining optimal lane time, tracking dynamic reference and energy-efficient allocation. In this paper, the dynamic constraints of vehicles during lane change are first established based on the longitudinal and lateral force coupling characteristics and the nominal reference trajectory. Then, by optimizing the lane change time, the yaw rate and lateral acceleration that connect with the lane change time are limed. Furthermore, to assure the dynamic properties of autonomous vehicles, the real system inputs under the restraints are obtained by using the MPC method. Based on the gained inputs and the efficient map of brushless direct-current in-wheel motors (BLDC IWMs), the nonlinear cost function which combines vehicle dynamic and energy consumption is given and the KKT-based method is adopted.
Findings
The effectiveness of the proposed control system is verified by numerical simulations. Consequently, the proposed control system can successfully achieve stable trajectory planning, which means that the yaw rate and longitudinal and lateral acceleration of vehicle are within stability boundaries, which accomplishes accurate tracking control and decreases obvious energy consumption.
Originality/value
This paper proposes a solution to simultaneously satisfy stable lane change maneuvering and reduction of energy consumption for autonomous electric vehicles. Different from previous path planning researches in which only the geometric constraints are involved, this paper considers vehicle dynamics, and stability boundaries are established in path planning to ensure the feasibility of the generated reference path.
Details
Keywords
Miaoxian Guo, Shouheng Wei, Chentong Han, Wanliang Xia, Chao Luo and Zhijian Lin
Surface roughness has a serious impact on the fatigue strength, wear resistance and life of mechanical products. Realizing the evolution of surface quality through theoretical…
Abstract
Purpose
Surface roughness has a serious impact on the fatigue strength, wear resistance and life of mechanical products. Realizing the evolution of surface quality through theoretical modeling takes a lot of effort. To predict the surface roughness of milling processing, this paper aims to construct a neural network based on deep learning and data augmentation.
Design/methodology/approach
This study proposes a method consisting of three steps. Firstly, the machine tool multisource data acquisition platform is established, which combines sensor monitoring with machine tool communication to collect processing signals. Secondly, the feature parameters are extracted to reduce the interference and improve the model generalization ability. Thirdly, for different expectations, the parameters of the deep belief network (DBN) model are optimized by the tent-SSA algorithm to achieve more accurate roughness classification and regression prediction.
Findings
The adaptive synthetic sampling (ADASYN) algorithm can improve the classification prediction accuracy of DBN from 80.67% to 94.23%. After the DBN parameters were optimized by Tent-SSA, the roughness prediction accuracy was significantly improved. For the classification model, the prediction accuracy is improved by 5.77% based on ADASYN optimization. For regression models, different objective functions can be set according to production requirements, such as root-mean-square error (RMSE) or MaxAE, and the error is reduced by more than 40% compared to the original model.
Originality/value
A roughness prediction model based on multiple monitoring signals is proposed, which reduces the dependence on the acquisition of environmental variables and enhances the model's applicability. Furthermore, with the ADASYN algorithm, the Tent-SSA intelligent optimization algorithm is introduced to optimize the hyperparameters of the DBN model and improve the optimization performance.
Details
Keywords
Song Lin, Edward G. Rogoff, Check-Teck Foo and Xiaoyuan Liu
This empirical study aims to test the impact of four types of entrepreneurial context on the growth and success rates of new ventures in China and related the findings to the…
Abstract
Purpose
This empirical study aims to test the impact of four types of entrepreneurial context on the growth and success rates of new ventures in China and related the findings to the theory and practice of entrepreneurship dating back 2,500 years to ancient China.
Design/methodology/approach
After describing the business guidelines given by Fan Li, an entrepreneurial merchant selling Chinese medicines in ancient times, a conceptual framework was extracted as the basis for a discussion of the relationship between entrepreneurial context and entrepreneurial activity. Entrepreneurial context was conceptualized as being composed of family, social, business and institutional components. Five hypotheses about the influence of these different context variables on entrepreneurial activities were developed. From data compiled from the sampling of 239 business entrepreneurs in Beijing, a hierarchical regression was formed and the hypotheses tested.
Findings
The impact of entrepreneurial context on entrepreneurial activity can be divided into two layers, internal factors (e.g. family context) which are similar to “yin” (?) in the traditional Chinese philosophy while external factors (e.g. business, social and institutional contexts) were like “yang” (?). The two factors play different roles in entrepreneurial activities, while different contexts mediate and moderate each other in complex ways.
Research limitations/implications
Research limitations pertain to the size and locale of the sample. A larger sample that involved subjects from different regions would facilitate a wider understanding of the effects of entrepreneurial context upon the entrepreneurial process.
Originality/value
The theory of entrepreneurial context is in its beginning stages, and the paper completed a systematic study of entrepreneurial context through theoretical model building using large-sample empirical research. In addition, the paper is the first ever to relate the theory and practice of entrepreneurship back 2,500 years. Through a multi-research methodology, the study clearly shows the critical importance of integrating Chinese history into the development of management theory.