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To overcome the difficulties in previous researches about energy-efficient design of parts, a method to estimate machining-related energy consumption of parts at the design phase is proposed. The binary tree is constructed to describe the structure of a part, and each node in the binary tree represents one feature in the part. The material embodied energy, theoretical cutting energy consumption and air-cutting energy consumption of a feature can be calculated based on its design and manufacturing parameters. At the design phase, manufacturing parameters of a feature can be obtained by the method of feature mapping from design parameters. By adding up above three types of energy consumption, total energy consumption of a feature can be calculated. Further, by adding up total energy consumption of all features in a part, the energy consumption of this part can be estimated. The proposed method was demonstrated by estimating the energy consumption of a shaft part designed by an auto parts manufacturer, and meanwhile the measured energy consumption of the shaft part was acquired by experimental measurement. The estimation accuracy is analysed and verified by comparing the estimated value and measured value. IntroductionEnergy is indispensible for the development of this modern society. Due to energy crisis and environmental pollution in energy generation (e.g. electricity generation by combustion of fossil fuels), the reduction of energy consumption not only benefits energy security and sustainable development of society but also improves environmental performances (Li, Hong, and Xing 2013; Rajemi, Mativenga, and Aramcharoen 2010). Therefore, energy efficiency has become increasingly vital (Seow, Rahimifard, and Woolley 2013). Particularly, products, for example machine tools, automobiles, turbines, account for a significant high proportion of total global energy consumption in their lifecycles. It has been proved that the reasonability of product design can exert a profound influence on reducing energy consumption of products in their future phases, such as manufacturing, transportation, usage and maintenance (Bonvoisin et al. 2013;Ingeneer, Mathieux, and Brissaud 2012), so energy-efficient design of products can play a key role in energy saving.Research suggests that energy-efficient design of products could start with predicting energy consumption of product design schemes in future phases, such as manufacturing phase, transportation phase and use phase, and further the product design schemes can be optimised based on the prediction information (Cao et al. 2012). In previous researches, energy-efficient design of products tends to be considered from the perspective of the whole life cycle rather than one or few phases, and these researches can be categorised into three aspects: (1) modelling the energy consumption for the product design scheme(Vinodh and Rathod 2010; Yang and Song 2009), (2) calculating the energy consumption for the product design scheme (Chen et al. 2012; Song and Lee 2010) and (3) optimising th...

Introduction: Tributyltin (TBT) is a largely diffused environmental pollutant. Several studies have demonstrated that TBT is involved in the development of obesity. However, few studies addressing the effects of TBT on the brain neuropeptides involved in appetite and body weight homeostasis have been published. Material and methods: Experiments were carried out on female and male Sprague-Dawley rats. Animals were exposed to TBT (0.5 μg/kg body weight) for 54 days. The hepatic triglyceride and total cholesterol were determined using commercial enzyme kits. The NPY, AgRP, POMC and CART mRNA expression in brains were quantified by real-time PCR. Results: TBT exposure resulted in significant increases in the hepatic total cholesterol and triglyceride concentration of both male and female rats. Interestingly, increases in body weight and fat mass were only found in the TBT-treated male rats. TBT exposure also led to a significant increase in food intake by the female rats, while no change was observed in the male rats. Moreover, the neuropeptides expression was different between males and females after TBT exposure. TBT induced brain NPY expression in the female rats, and depressed brain POMC, AgRP and CART expression in the males. Conclusions: TBT can increase food intake in female rats, which is associated with the disturbance of NPY in brains. TBT had sex-different effects on brain NPY, AgRP, POMC and CART mRNA expression, which indicates a complex neuroendocrine mechanism of TBT.

A photoelectric detection system is a typical type of device widely used for detecting purposes based on unmanned aerial vehicles (UAV). Stability accuracy is the key performance index. Compared to traditional analysis methods aimed at unpredictable error-causing sources, assembly errors can be easily controlled during the manufacturing processes. In this research, an analysis method of assembly error effect on stability accuracy is proposed. First, by using kinematics analysis of homogeneous coordinate transformation, stability accuracy is comprehensively modeled and simulated. Then, by analyzing the manufacturing process, assembly errors of axis perpendicularities, run-outs and gyroscopes are defined and modeled. By simulating different carrier movements, the effects caused by assembly errors under various environments are studied. Finally, error sensitivity is proposed by using standard deviation analysis. Results show that the most sensitive assembly errors are identified, and ranked in order of sensitivity as follows: x-component of pitch axis perpendicularity, y-component of the azimuth gyroscope assembly, and z-component of the pitch gyroscope assembly. In conclusion, the results can be used as standards of manufacturing process improvements, and the proposed methods can be used to provide valuable references for real application scenarios.

Machining allowance distribution and related parameter optimization of machining processes have been well-discussed. However, for energy saving purposes, the optimization priorities of different machining phases should be different. There are often significant incoherencies between the existing research and real applications. This paper presents an improved method to optimize machining allowance distribution and parameters comprehensively, considering energy-saving strategy and other multi-objectives of different phases. The empirical parametric models of different machining phases were established, with the allowance distribution problem properly addressed. Based on previous analysis work of algorithm performance, non-dominated sorting genetic algorithm II and multi-objective evolutionary algorithm based on decomposition were chosen to obtain Pareto solutions. Algorithm performances were compared based on the efficiency of finding the Pareto fronts. Two case studies of a cylindrical turning and a face milling were carried out. Results demonstrate that the proposed method is effective in trading-off and finding precise application scopes of machining allowances and parameters used in real production. Cutting tool life and surface roughness can be greatly improved for turning. Energy consumption of rough milling can be greatly reduced to around 20% of traditional methods. The optimum algorithm of each case is also recognized. The proposed method can be easily extended to other machining scenarios and can be used as guidance of process planning for meeting various engineering demands.

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Keyword spotting (KWS) plays a crucial role in human–machine interactions involving smart devices. In recent years, temporal convolutional networks (TCNs) have performed outstandingly with less computational complexity, in comparison with classical convolutional neural network (CNN) methods. However, it remains challenging to achieve a trade-off between a small-footprint model and high accuracy for the edge deployment of the KWS system. In this article, we propose a small-footprint model based on a modified temporal efficient neural network (TENet) and a simplified mel-frequency cepstrum coefficient (MFCC) algorithm. With the batch-norm folding and int8 quantization of the network, our model achieves the accuracy of 95.36% on Google Speech Command Dataset (GSCD) with only 18 K parameters and 461 K multiplications. Furthermore, following a hardware/model co-design approach, we propose an optimized dataflow and a configurable hardware architecture for TENet inference. The proposed accelerator implemented on Xilinx zynq 7z020 achieves an energy efficiency of 25.6 GOPS/W and reduces the runtime by 3.1× compared with state-of-the-art work.