Beizhi Li
Donghua University
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Featured researches published by Beizhi Li.
Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture | 2016
Chongjun Wu; Beizhi Li; Steven Y. Liang
The ability to predict the critical depth for ductile-mode grinding of brittle materials is important to grinding process optimization and quality control. The traditional models for predicting the critical depth are mainly concerned with the material properties without considering the operation parameters. This article presents a new critical energy model for brittle–ductile transition by considering the strain rate effect brought by the grinding wheel speed and chip thickness. The experiments will be conducted through a high-speed diamond grinder on reaction-sintered silicon carbide materials under different grinding speed and chip thickness. Through detailed analysis of the strain rate effect on the dynamic fracture toughness, a new fracture toughness model will be established based on the Johnson–Holmquist material model (JH-2) and calibrated through experiments based on the indentation fracture mechanics. Then, the new critical model for brittle–ductile transition will be established by introducing the dynamic facture toughness model considering the wheel speed and chip thickness. According to scanning electron microscope observations, the results show that ductile-mode grinding can be obtained through a combination of higher grinding speed and smaller chip thickness. Moreover, the critical value for ductile grinding of brittle materials can be improved through the elevation of the grinding speed or reduction in the chip thickness.
Entropy | 2016
Qing Li; Steven Y. Liang; Jianguo Yang; Beizhi Li
According to the chaotic features and typical fractional order characteristics of the bearing vibration intensity time series, a forecasting approach based on long range dependence (LRD) is proposed. In order to reveal the internal chaotic properties, vibration intensity time series are reconstructed based on chaos theory in phase-space, the delay time is computed with C-C method and the optimal embedding dimension and saturated correlation dimension are calculated via the Grassberger–Procaccia (G-P) method, respectively, so that the chaotic characteristics of vibration intensity time series can be jointly determined by the largest Lyapunov exponent and phase plane trajectory of vibration intensity time series, meanwhile, the largest Lyapunov exponent is calculated by the Wolf method and phase plane trajectory is illustrated using Duffing-Holmes Oscillator (DHO). The Hurst exponent and long range dependence prediction method are proposed to verify the typical fractional order features and improve the prediction accuracy of bearing vibration intensity time series, respectively. Experience shows that the vibration intensity time series have chaotic properties and the LRD prediction method is better than the other prediction methods (largest Lyapunov, auto regressive moving average (ARMA) and BP neural network (BPNN) model) in prediction accuracy and prediction performance, which provides a new approach for running tendency predictions for rotating machinery and provide some guidance value to the engineering practice.
Machining Science and Technology | 2015
Yamin Shao; Beizhi Li; Steven Y. Liang
The surface roughness represents the quality of ground surface since irregularities on the surface may form nucleation for cracks or corrosion and thus degrade the mechanical properties of the component. The surface generation mechanism in grinding of ceramic materials could behave as a mixture of plastic flow and brittle fracture, while the extent of the mixture hinges upon certain process parameters and material properties. The resulting surface profile can be distinctively different from these two mechanisms. In this article, a physics-based model is proposed to predict the surface roughness in grinding of ceramic materials considering the combined effect of brittle and ductile material removal. The random distribution of cutting edges is first described by a Rayleigh probability function. Afterwards, surface profile generated by brittle mode grinding is characterized via indentation mechanics approach. Last, the surface roughness is modeled through a probabilistic analysis of ductile and brittle generated surface profile. The model expresses the surface finish as a function of the wheel microstructure, the process conditions, and the material properties. The predictions are compared with experimental results from grinding of silicon carbide and silicon nitride workpieces (SiC and Si3N4, respectively) using a diamond wheel.
Machining Science and Technology | 2014
Xia Ji; Xueping Zhang; Beizhi Li; Steven Y. Liang
□ Residual stress is one of the critical characteristics for assessing the qualities and functionalities of machined products in light of its direct effect on endurance limit, distortion, and corrosion resistance. Primary factors responsible for residual stresses distribution include mechanical effects, thermal effects, microstructure evolutions, and a combination of these mechanisms. This study investigates the effects of minimum quantity lubrication (MQL) on machining force, temperature and residual stress through a physics-based modeling method. Both the lubrication and cooling effects caused by MQL air-oil mixture contribute to changes in friction due to boundary lubrication as well as variations in the thermal stress due to heat loss. The modified Oxleys model is employed to predict the cutting force and temperature directly from cutting conditions. The predicted cutting force and temperature are then coupled into a thermal-mechanical model which incorporates the kinematic hardening and strain compatibility to predict the machining-induced residual stress under lubricated conditions. The proposed analytical method is experimentally verified by orthogonal cutting tests for AISI 4130 alloy steel in the context of forces, temperatures, and residual stresses.
Materials and Manufacturing Processes | 2016
Zishan Ding; Beizhi Li; Yamin Shao; Steven Y. Liang
In this study, maraging steel kinetics of both diffusion-controlled and diffusionless phase transitions under thermo-mechanical conditions was predicted using a physics-based model via the investigation of heating rate, stain rate, and contact zone temperature during grinding. It was assumed that high heating rate and high strain rate will affect phase transition. The theory of phase transition nucleation was combined with analysis of heating rate, stain rate, and contact zone temperature to predict phase transition during the grinding. The effects of heating rate and strain rate on phase transition were verified through maraging steel grinding experiments, X-ray diffractometry, and regression analyses. The results of post-grinding phase volume fractions of martensite and ferrite were compared with the results predicted from the neural network model, and models without consideration of heating rate or strain rate. Validation tests proved that the proposed physics-based model successfully predicted the occurrence and extent of phase transition associated with heating rate and strain rate. This physics-based model can be used to reduce phase transition during grinding of maraging steel, or to cause a predefined phase transition by controlling the thermo-mechanical loading.
AIP Advances | 2018
Yao Liu; Beizhi Li; Lingfei Kong
The precision and crack-free surface of brittle silicon carbide (SiC) ceramic was achieved in the nanoscale ductile grinding. However, the nanoscale scratching mechanism and the root causes of SiC ductile response, especially in the atomistic aspects, have not been fully understood yet. In this study, the SiC atomistic scale scratching mechanism was investigated by single diamond grain scratching simulation based on molecular dynamics. The results indicated that the ductile scratching process of SiC could be achieved in the nanoscale depth of cut through the phase transition to an amorphous structure with few hexagonal diamond structure. Furthermore, the silicon atoms in SiC could penetrate into diamond grain which may cause wear of diamond grain. It was further found out that the chip material in the front of grain flowed along the grain side surface to form the groove protrusion as the scratching speed increases. The higher scratching speed promoted more atoms to transfer into the amorphous structure an...
Machining Science and Technology | 2016
Miaoxian Guo; Beizhi Li
ABSTRACT This article provides an integrated approach to evaluate grinding machines dynamic performance in speed range by examining both the manufacturing process and the machine tool. This approach considers the dynamic grinding force as a starting point. The dynamic change of the cutting depth in the grinding process is studied first. Then, based on the dynamic model of grinding process developed in previous studies, the dynamic forces of the manufacturing process are predicted. By analyzing the grinding force model in the frequency-domain and the frequency response function, the root mean square values of the machine vibration are calculated. The evaluation works by comparing the variation and trend of the vibration level. The proposed evaluation method is then applied to a physical grinding machine. The dynamic performance obtained from the experiment matches the predicted trend in the models. In addition, this method can be used to inspect and optimize the design of the machines spindle system. For the grinding process that requires a high dynamic performance, this method also provides reference for the speed selection within all speed range.
industrial engineering and engineering management | 2010
S.S. Wu; Beizhi Li; Jianguo Yang; Sanjay Kumar Shukla
High-performance concrete (HPC) is a very complex material and hence very hard to predict its compressive strength. This paper deals with building a regression model for predicting concretes compressive strength. First of all, eight process variables are identified as determinants of Concrete Compressive Strength (CCS). These variables are Cement, Blast Furnace Slag, Fly Ash, Water, Superplasticizer, Coarse Aggregate, Fine Aggregate, and Age. Further, correlation among these variables is computed and it is found that a few of them are highly correlated. Therefore, interactions among these variables are taken into account. After that, a regression model is developed by regressing CCS against all process variables and significant interactions. Finally, diagnostics are conducted to fine tune the model and a parsimonious model is obtained with 84.37% coefficient of determination. Appropriateness of the model is investigated by testing it against unseen data points.
International Journal of Manufacturing Technology and Management | 2007
Yaqin Zhou; Beizhi Li; Jianguo Yang
Non-cutting time or auxiliary time may not be ignored in solving job shop scheduling problems, which includes the time for transportation or transferring of job pieces and for adjustment of cutting tools and fixtures, etc. while considering batch that can also reduce the number of transporting and adjusting times. In this paper, a complicated scheduling problem is studied, which fully considers batch size, the available time of jobs and non-cutting time as the necessary operating conditions based on practical production. Firstly, a model of this problem is given, along with a biological immune algorithm for solving it. Then the key techniques for realising the intelligent algorithm are introduced, including the design of antibody encoding, the computation and optimisation method of the starting time of each job, and the operation of crossover and mutation. Results from the trial solutions of the problem with considerations of non-cutting time, batch size and multiconstraints show that the biological intelligent scheduling algorithm proposed is effective in solving this kind of scheduling problem.
SpringerPlus | 2016
Manik Rajora; Pan Zou; Yao Guang Yang; Zhi Wen Fan; Hungyi Chen; Wen Chieh Wu; Beizhi Li; Steven Y. Liang
Abstract It can be observed from the experimental data of different processes that different process parameter combinations can lead to the same performance indicators, but during the optimization of process parameters, using current techniques, only one of these combinations can be found when a given objective function is specified. The combination of process parameters obtained after optimization may not always be applicable in actual production or may lead to undesired experimental conditions. In this paper, a split-optimization approach is proposed for obtaining multiple solutions in a single-objective process parameter optimization problem. This is accomplished by splitting the original search space into smaller sub-search spaces and using GA in each sub-search space to optimize the process parameters. Two different methods, i.e., cluster centers and hill and valley splitting strategy, were used to split the original search space, and their efficiency was measured against a method in which the original search space is split into equal smaller sub-search spaces. The proposed approach was used to obtain multiple optimal process parameter combinations for electrochemical micro-machining. The result obtained from the case study showed that the cluster centers and hill and valley splitting strategies were more efficient in splitting the original search space than the method in which the original search space is divided into smaller equal sub-search spaces.