Top-Rem grinding tool modification considering loaded edge contact for spiral bevel gears

Focusing on the collaborative manufacturing requiring meshing interface physical performances, an innovative Top-Rem grinding tool modification considering loaded edge contact is proposed for spiral bevel gears. Accurate Top-Rem grinding tool parameters can instead of machine-tool settings and their impact in the sophisticated flank topography correction technique. At first, the whole Top-Rem tool modification includes: i) arc-shaped blade part; ii) Top-Rem part; iii) top fillet part. Then, grinding tool modification model considering both loaded contact and geometric accuracy is established. Where, three important constraint conditions were taken into accounts: i) geometric boundary considering loaded edge contact, ii) root overcutting, and iii) flank geometric accuracy. Moreover, two kinds of optimization strategies are proposed to improve the accuracy and efficiency of the whole modification: i) two types of modification by the different tool modification parameters; ii) sensitivity analysis with respect to ease-off for optimization operation of the unknown variables. Finally, the given instances can verify that it can get accurate and effective loaded contact performance optimization in early stage of full life cycle product development.     

Digital twin modeling for loaded contact pattern-based grinding of spiral bevel gears

To distinguish with the conventional tooth flank grinding only considering geometric accuracy, an innovative digital twin modeling is proposed for loaded contact pattern based grinding of spiral bevel gears. Where, data-driven grinding simulation, sensitivity analysis strategy, adaptive decision and control are developed. Focusing on loaded contact pattern optimization, numerical loaded tooth contact analysis (NLTCA) considering noncentrosymmetric problem and tooth flank roughness is developed for data-driven relationship establishment. Then, an adaptive data-driven tooth flank grinding decision and control model is established. Where, the universal motion concept (UMC) machine settings is selected as the optimal design variable. It is actually an infinite approximation to the target tooth flank in form of an adaptive control system. Moreover, with point-to-point material removal distribution, the different optimization strategies are proposed for accurate tooth flank grinding. In particular, the overcutting problem on the tooth flank grinding programming is investigated. Finally, Levenberg-Marquardt method is applied to solve the established nonlinear lease square model for the accurate machine tool settings having modification variations. Thus, this accurate data-driven digital twin modeling can achieve loaded contact pattern-based grinding. The provided numerical and test instances can verify the proposed digital twin modeling.     

An accurate model of high-performance manufacturing spiral bevel and hypoid gears based on machine setting modification

Machine setting modification especially considering the tooth contact performance is always vital to design and manufacture for the spiral bevel and hypoid gears. With the application of new manufacture concepts and advanced design methods, a closed-loop complex model is established to execute the high-performance manufacture. Firstly, an optimization model of machine settings modification considering the residual ease-off is established by correlating the tooth flank represented by universal machine settings. Furthermore, a trust region algorithm with control strategies concerning: (i) double Dogleg algorithm for the iteration step and (ii) updating damping efficient for the trust region radius is employed to obtain the target flank. Finally, a closed-loop measurement-modification-manufacturing (3M) model for high-performance manufacture of the spiral bevel and hypoid gear is established. Where, it mainly describes the accurate machine setting modification considering tooth contact performances. Some numerical examples in practical application can verify the accuracy and performance of the proposed model.     

An innovative approach to NC programming for accurate five-axis flank milling of spiral bevel or hypoid gears

To transfer power, a pair of spiral bevel or hypoid gears engages. From beginning to end of two tooth surfaces engaging with each other: for their rigid property, they contact at different points; and for their plastic property, they contact at small ellipses around the points. On each surface, the contact line (or called as contact path) by connecting these points and the contact area by joining these ellipses are critical to driving performance. Therefore, to machine these surfaces, it is important to machine the contact line and area with higher accuracy than other areas. Five-axis flank milling is efficient and is widely used in industry. However, tool paths for flank milling the gears, which are generated with the existing methods, can cause overcuts on the contact area with large machining errors. To overcome this problem, an innovative approach to NC programming for accurate and efficient five-axis flank milling of spiral bevel or hypoid gears is proposed. First, the necessary conditions of the cutter envelope surface tangent with the designed surface along a designed line are derived to address the overcut problem of five-axis milling. Second, the tooth surface including the contact line and area are represented using their machining and meshing models. Third, according to the tooth surface model, an optimization method based on the necessary conditions is proposed to plan the cutter location and orientation for flank milling the tooth surface. By using these planned tool paths, the overcut problem is eliminated and the machining errors of contact area are reduced. The proposed approach can significantly promote flank milling in the gear manufacturing industry.     

A data-driven optimization model to collaborative manufacturing system considering geometric and physical performances for hypoid gear product

A data-driven optimization model to collaborative manufacturing system considering geometric and physical performances is proposed to improve competitiveness of hypoid gear product development facing with economic globalization. Firstly, to deal with the vagueness or impreciseness of the voice of customer (VOC), the fuzzy quality function deployment (fuzzy-QFD) using fuzzy weighted average method in the fuzzy expected value operator is introduced into hypoid gear manufacturing. It can convert them into the critical to qualities (CTQs), and the technical geometric and physical performance requirements. And then, the multi-objective optimization (MOO) modification of machine settings is proposed to establish a basic data-driven model for collaborative system. Different with the conventional modification only considering geometric performance, it provides an improved modification also considering the physical performances. Finally, a double-curved shell model of hypoid gear finite element is used to perform the numerical loaded tooth contact analysis (NLTCA), as well as to establish the data-driven relationships between machine settings and physical performance evaluations. Immediately, whole development is divided into three sub-problems: i) optimal operations of the noise factors by measurement and numerical control (NC) compensation, ii) identification of the prescribed ease-off topography by multi-objective optimization using iterative reference point approach and iii) modification considering geometric performance by a trust region algorithm with step strategy. The numerical instance in practical applications is given to verify the proposed methodology.     

螺旋锥齿轮球面渐开线齿面形成原理及推论

从螺旋锥齿轮的精确齿面形状出发,详细地论述了球面渐开线齿面的基本 组成。基于球面渐开线理论在螺旋锥齿轮方面已取得的研究成果,分析并总结其特点与不足, 提出了螺旋锥齿轮的球面渐开线齿面形成原理。并对球面渐开线齿面形成过程中的关键环节 产形线、工作齿廓和球面渐开线齿面的基本方程进行了推导和论述,为其设计及加工提出了 一些方案,以便为螺旋锥齿轮的研究提供理论基础和思路。     

Reducing bending stress in external spur gears by redesign of the standard cutting tool

For the design of gears the stress due to bending plays a significant role. The stress from bending is largest in the root of the gear teeth, and the magnitude of the maximum stress is controlled by the nominal bending stress and stress concentration due to the geometric shape of the tooth. In this work the bending stress of involute teeth is minimized by shape optimizing the tip of the standard cutting tool. By redesign of the tip of the standard cutting tool we achieve that the functional part of the teeth stays the same while at the same time the root shape is changed so that a reduction of the stresses results. The tool tip shape is described by different parameterizations that use the super ellipse as the central shape. For shape optimization it is important that the shape is given analytically. The shape of the cut tooth that is the envelope of the cutting tool is found analytically. The parameterization includes the standard ISO tooth. Practical simple changes in the design of the tool tip is shown to result in large reduction of the bending stress, keeping at the same time the engage part of the tooth unchanged. This leads to gears that have unchanged functionality based on the involute design, and these can be engaged with existing designs. The presented new cutting tools are custom tools specific for a given gear.     

锥齿轮齿根应力有限元模型和分析

从齿轮加工原理出发,利用渐开线和齿根过渡曲线方程生成轮齿的精确齿形,建立2种等效直齿锥齿轮三维轮齿几何模型,研究渐开线直齿锥齿轮的精确建模方法.分别使用h-单元和p-单元分析计算直齿锥齿轮齿根应力,建立直齿锥齿轮三维轮齿齿根应力有限元计算模型和计算基准.计算结果与ISO国标公式比较,证明模型的正确性、精确性和可靠性.     

In-process tool condition forecasting based on a deep learning method

It is widely acknowledged that machining precision and surface integrity are greatly affected by cutting tool conditions. In order to enable early cutting tool replacement and proactive actions, tool wear conditions should be estimated in advance and updated in real-time. In this work, an approach to in-process tool condition forecasting is proposed based on a deep learning method. A long short-term memory network is designed to forecast multiple flank wear values based on historical data. A residual convolutional neural network is built to enable in-process tool condition monitoring, using raw signals acquired during the machining process. The integration of them enables in-process tool condition forecasting. Median-based correction and mean-based correction are adopted to improve the accuracy. IEEE PHM 2010 challenge data has been used to illustrate and validate this approach. Experimental study and quantitative comparisons showed that future flank wear values could be precisely forecasted during the machining process. The proposed approach contributes to prompt and reliable cutting tool condition forecasting, which will support the decision-making about cutting tool replacement in data-driven smart manufacturing.     

A data-model-fusion prognostic framework for dynamic system state forecasting

A novel data-model-fusion prognostic framework is developed in this paper to improve the accuracy of system state long-horizon forecasting. This framework strategically integrates the strengths of the data-driven prognostic method and the model-based particle filtering approach in system state prediction while alleviating their limitations. In the proposed methodology, particle filtering is applied for system state estimation in parallel with parameter identification of the prediction model (with unknown parameters) based on Bayesian learning. Simultaneously, a data-driven predictor is employed to learn the system degradation pattern from history data so as to predict system evolution (or future measurements). An innovative feature of the proposed fusion prognostic framework is that the predicted measurements (with uncertainties) from the data-driven predictor will be properly managed and utilized by the particle filtering to further update the prediction model parameters, thereby enabling markedly better prognosis as well as improved forecasting transparency. As an application example, the developed fusion prognostic framework is employed to predict the remaining useful life of lithium ion batteries through electrochemical impedance spectroscopy tests. The investigation results demonstrate that the proposed fusion prognostic framework is an effective forecasting tool that can integrate the strengths of both the data-driven method and the particle filtering approach to achieve more accurate state forecasting.     

齿轮传动的CAD软件

在机械传动装置中,广泛地应用着齿轮传动。外啮合圆柱齿轮、内啮合圆柱齿轮、圆维齿轮和蜗轮蜗杆传动是齿轮传动的基本形式。我们在长期工作研究的基础上,建立了一套CAD软件。该软件可自动进行以上四种传动形式的优化设计,可通过人机交互完成齿轮的结构设计,用有限元分析计算齿轮的强度,并可在绘图机上绘制齿轮的工作图。该软件适用性强、使用方便,经过简单的修改便可用于其它机械产品的CAD。     

直齿圆柱齿轮弯曲强度等几何分析

齿轮作为一种重要的机械零件,其设计与制造直接影响着机械设备的实际性能与工作质量.由于齿轮的工作寿命与齿轮的最大弯曲应力的六次方成反比,因此精准地计算齿轮齿根的弯曲强度,是延长其使用寿命的必要保证.为了更精准地实现齿轮的弯曲应力的求解计算,本文提出了基于等几何法的齿轮弯曲强度分析算法,实现了对平面齿轮结构力学性能的等几何...     

Aspects of burr formation in bandsaw teeth manufactured by milling operation

Steel manufacturers and stockholders prefer bandsawing for cutting off raw materials compared to other techniques as it enjoys competitive advantages of higher accuracy of cut, better surface finish, lower kerf loss, better straightness of cut, long tool life and high metal removal rate. Along with the geometries of the bandsaw tooth, bandsaw cutting edge condition (e.g., edge sharpness and burr) significantly affects the cutting performance of a bandsaw. Currently the production of bandsaw is largely done by milling operation due to the scale of manufacturing and the economics of milling compared to other processes (e.g., grinding). Ideally, the bandsaw teeth should possess sharp cutting edges with no burr. In general, two types of burr are commonly seen in the bandsaw teeth manufactured by milling operation namely tooth tip burr and side burr. Current research undertaken at Northumbria University in collaboration with a major bandsaw producer is focused on the mechanism of burr formation in the bandsaw teeth. This paper briefly outlines the factors affecting the burr formation in bimetal (High Speed Steel edge wire and soft steel backing material) bandsaw teeth manufactured by milling process and suggests the necessary steps to be considered for manufacturing burr free bandsaw with sharp cutting edges. The investigation showed that flank wear in the milling cutter has a major influence on the side burr formation in the bandsaw teeth, whereas tooth tip burr was influenced by both flank wear and “V” type notch wear found at the crossover point on the flank face. It was also concluded that TiN coating on the milling cutter could control the burr formation in bandsaw teeth to some degree.     

Modified involute helical gears: computerized design,simulation of meshing and stress analysis

The contents of the paper cover: (i) computerized design, (ii) methods for generation, (iii) simulation of meshing, and (iv) enhanced stress analysis of modified involute helical gears. The approaches proposed for modification of conventional involute helical gears are based on conjugation of double-crowned pinion with a conventional helical involute gear. Double-crowning of the pinion means deviation of cross-profile from an involute one and deviation in longitudinal direction from a helicoid surface. The pinion-gear tooth surfaces are in point contact, the bearing contact is localized and oriented longitudinally, edge contact is avoided, the influence of errors of alignment on the shift of bearing contact and vibration and noise are reduced substantially. The developed theory is illustrated with numerical examples that confirm the advantages of the gear drives of the modified geometry in comparison with conventional helical involute gears.     

Monthly Runoff Prediction of Zhengyixia in the Heihe River based on Singular Spectrum Analysis-grey Wolf Optimizer-support Vector Regression Hybrid Model

Hydrologic prediction is an important prerequisite for optimal allocation of water resources, but the traditional forecasting methods generally have the problem of low forecasting accuracy. To improve the accuracy of hydrologic prediction, a hybrid data-driven model is proposed for monthly runoff forecasting, namely, Singular Spectrum Analysis-Grey Wolf Optimizer-Support Vector Regression (SSA-GWO-SVR) model. The proposed model uses SSA to denoise the runoff data to improve the stability and predictability of runoff series, and uses GWO to optimize the parameters of SVR model to enhance the generalization ability of the model. This model is validated by monthly runoff prediction of Zhengyixia in the Heihe River Basin, and the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), correlation coefficient (R) and Nash-Sutcliffe Efficiency Coefficien (NSEC) are used as evaluation criteria. The experimental results show that the prediction accuracy of the proposed model is significantly higher than those of Autoregressive Integrated Moving Average model (ARIMA), Persistent Model (PM), Cross Validation(CV)-SVR and GWO-SVR models, and the can predict the runoff peak well, which indicates that the model is a reliable runoff forecasting model, can capture the intrinsic characteristics of hydrologic runoff more deeply, and provides a new method for hydrologic prediction based on data-driven model.     

基于奇异谱分析-灰狼优化-支持向量回归混合模型的黑河正义峡月径流预测

水文预报是水资源优化配置的重要前提,而传统预报方法普遍存在预测精度低的问题,为提高水文预报的准确性,提出了一种混合数据驱动模型用于月径流预测,即奇异谱分析-灰狼优化-支持向量回归(SSA-GWO-SVR)模型。该模型通过SSA对径流数据进行去噪处理来提高径流序列的平稳性和可预测性,采用GWO对SVR模型的参数进行联合选优,从而增强模型的泛化能力。通过黑河正义峡的月径流预测进行模型验证,以平均绝对误差(MAE)、均方根误差(RMSE)、相关系数(R)和纳什效率系数(NSEC)为模型评价标准。实验结果表明该模型的预测精度明显高于自回归积分滑动平均模型(ARIMA)、持续性模型(PM)、交叉验证-SVR(CV-SVR)和GWOSVR模型,并且它能很好地预测径流峰值,说明该模型是一种可靠的径流预测模型,能够更深入地捕获水文径流的内在特性,为基于数据驱动模型的水文预报提供了一种新方法。     

双不确定参数作用下双稳态能量采集系统的随机响应分析

为改善纯电动汽车齿轮传动在整个工况范围内的工作性能,提出一种计及负载扭矩和转速工况影响的齿面修形方法.考虑时变啮合刚度、啮合冲击、齿面摩擦激励,建立了系统动力学分析模型.结合轮齿几何接触分析和承载接触分析,采用遗传算法优化获得全工况最优的齿廓、齿向抛物线修形系数,并对比分析了不同修形方案的抑振效果.结果表明：计及工况影响的齿面修形在全工况下的振动加速度均方根都保持在较低水平,说明这一齿面修形策略具有更好的全局抑振效果.     

纯电动汽车高速齿轮传动全工况抑振修形方法

为改善纯电动汽车齿轮传动在整个工况范围内的工作性能,提出一种计及负载扭矩和转速工况影响的齿面修形方法.考虑时变啮合刚度、啮合冲击、齿面摩擦激励,建立了系统动力学分析模型.结合轮齿几何接触分析和承载接触分析,采用遗传算法优化获得全工况最优的齿廓、齿向抛物线修形系数,并对比分析了不同修形方案的抑振效果.结果表明：计及工况影响的齿面修形在全工况下的振动加速度均方根都保持在较低水平,说明这一齿面修形策略具有更好的全局抑振效果.     

Geometric-thermal error control system for gear profile grinding machine

Geometric and thermal errors, which are the main error factors for reducing the machining accuracy, should be controlled. But the control effect is poor, which is a stumbling block to limit the wide application of the error control. In this study, a geometric-thermal error control system (GTECS) is designed for gear profile grinding machines. For the mist layer of GTECS, the wireless sensor network is designed to realize the data collection and transfer. For the edge layer of GTECS, the edge controller is designed to conduct the sensitive error analysis. For the fog layer, the control module is designed to conduct the geometric and thermal error prediction. In this layer, the analytical model of the rolling guide/slider system is proposed to calculate geometric errors of X- and Z- axes, and the thermal boundary conditions are calculated, and the thermal error models of the spindle and C-axis are proposed based on transfer learning model (TLM) of the sooty tern optimization (STO)-bilinear temporal convolutional network (BTCN). For the cloud layer, the data computation and management are realized by Hadoop and Yet Another Resource Negotiator (YARN), respectively. The geometric and thermal error models of X- and Z-axes, thermal errors models of the spindle and C-axis, and multi-source error model are embedded into it. With the execution of GTECS, the geometric precision for the total tooth profile deviation and tooth profile deviation are increased from ISO level 8 to ISO level 5 and from ISO level 5 to ISO level 3, respectively.     

A novel combined model based on hybrid optimization algorithm for electrical load forecasting

Accurate electrical load forecasting always plays a vital role in power system administration and energy dispatch, which are the foundation of the smooth operation of the national economy and people’s daily life. Thinking from this vision, many scholars have made great efforts to seek suitable optimization algorithms to improve the performance of existing forecasting algorithm. However, most of the studies ignore the inherent disadvantages of single optimization algorithm, which leads to sub-optimal forecasting performance. Therefore, a novel electric load forecasting system was successfully proposed in this paper by the combination of data preprocessing, hybrid optimization algorithms, and several single classical forecasting methods, which successfully overcomes the defects of single traditional forecasting models and achieves higher forecasting accuracy than that of single model optimization. Besides, the 30 min interval data of Queensland, Australia from March to April is used as illustrative examples to evaluate the performance of the developed model. The results of tests demonstrate that the proposed hybrid model can better approximate the actual value, and it can also be employed as a useful tool for smart grids dispatching planning.