Hui Jing

Estimation of the measurement uncertainty based on quasi Monte-Carlo method in optical measurement

Because measurement uncertainty is an important parameter to evaluate the reliability of measurement results, it is essential to present reliable methods to evaluate the measurement uncertainty especially in precise optical measurement. Though Monte-Carlo (MC) method has been applied to estimate the measurement uncertainty in recent years, this method, however, has some shortcomings such as low convergence and unstable results. Therefore its application is limited. To evaluate the measurement uncertainty in a fast and robust way, Quasi Monte-Carlo (QMC) method is adopted in this paper. In the estimating process, more homogeneous random numbers (quasi random numbers) are generated based on Haltons sequence, and then these random numbers are transformed into the desired distribution random numbers. An experiment of cylinder measurement is given. The results show that the Quasi Monte-Carlo method has higher convergence rate and more stable evaluation results than that of Monte-Carlo method. Therefore, the quasi Monte-Carlo method can be applied efficiently to evaluate the measurement uncertainty.

The Fault Diagnosis of Garbage Crusher Based on Ant Colony Algorithm and Neural Network

The garbage crusher is one of the important parts in recoverable coal production line. To diagnose its faults during the working process, Back Propagation algorithm is used. However, it has some shortcomings, such as low precision solution, slow searching speed and easy convergence to the local minimum points. To overcome this problem, a novel method which integrates Back Propagation neural network (BP NN) and Ant Colony Algorithm(ACA) is proposed in this paper. ACA has the advantages such as positive feedback, distributed computation and using a constructive greedy heuristic. In this paper, ACA is used to train the weights and the thresholds of BP NN, so the searching speed and the precision can be improved. An case study is given. The result shows that the proposed method improves the training efficiency and the fault classification accuracy.

Evaluation of measurement uncertainty based on quasi-Monte-Carlo method in the large diameter measurement

Abstract. The way of measuring diameter by use of measuring bow height and chord length is commonly adopted for the large diameter work piece. In the process of computing the diameter of large work piece, measurement uncertainty is an important parameter and is always employed to evaluate the reliability of the measurement results. Therefore, it is essential to present reliable methods to evaluate the measurement uncertainty, especially in precise measurement. Because of the limitations of low convergence and unstable results of the Monte-Carlo (MC) method, the quasi-Monte-Carlo (QMC) method is used to estimate the measurement uncertainty. The QMC method is an improvement of the ordinary MC method which employs highly uniform quasi random numbers to replace MC’s pseudo random numbers. In the process of evaluation, first, more homogeneous random numbers (quasi random numbers) are generated based on Halton’s sequence. Then these random numbers are transformed into the desired distribution random numbers. The desired distribution random numbers are used to simulate the measurement errors. By computing the simulation results, measurement uncertainty can be obtained. An experiment of cylinder diameter measurement and its uncertainty evaluation are given. In the experiment, the guide to the expression of uncertainty in measurement method, MC method, and QMC method are validated. The result shows that the QMC method has a higher convergence rate and more stable evaluation results than that of the MC method. Therefore, the QMC method can be applied effectively to evaluate the measurement uncertainty.

A Fast Retrieval Method Based on K-Means Clustering for Mechanical Product Design

Radius Angle Histogram (RAH) is useful method of retrieving 3D mechanical models. This method, however, not completely uses the information of radius angle histogram of the model. As a result, the retrieval precision is not very high enough. To improve the retrieval efficiency, K-means clustering method (KMM) is proposed in this paper. The radius angle histograms of the models are established first and then be served as inputs as KMM, respectively. By using KMM, the models can be classified and the results can be obtained. To validate the proposed method, an experiment is given. The results show that the retrieval precision of the proposed method is higher than that of single using RAH method.

Optimal design of work stage mounting system of precision packaging equipment

High precision packaging equipments need high precision isolator to reduce the vibration transmission from outside to work stage. Nowadays, a useful and effective way for vibration isolating is to employ the proper mounting systems to reduce the vibration. One useful method for helping the design engineer to develop more effective mounting systems is through optimization techniques. Sequential Quadratic Programming (SQP) is an effective optimization technique in many engineering fields. In this paper, design optimization of work stage mounting system based on SQP method for vibration control is presented. The optimization objective is to find the highest decoupling ratio of the each mount while selecting the stiffness and orientations of individual mount. The constraints are imposed to keep the desired decoupled ratio in each orientation and the frequency corresponding to the decoupled ratio. A case study is given to validate the proposed method. The result shows that the value of optimized system, such as decoupling ratio, is improved significantly. Therefore, the method proposed in this paper is effective for the optimization of work stage mounting system.

Roller Element Bearing of Mine Ventilating Fan with Fault Diagnosis Based on Mechanics Properties and RBF Neural Network

Roller element bearing is an important part of mine ventilating fan. The management and maintenance of the equipment is very important. Therefore, it is necessary to employ fault diagnosis process to the roller element bearing. In this paper, mechanics properties of roller element bearing are analyzed. Then, Radial Basis Function (RBF) neural network is used for the fault diagnosis of the roller element bearing. The structure and inference of RBF network are discussed in detail. The roller element bearing fault diagnosis model is established based on RBF network. A case study is given. The proposed method is applied to the fault diagnosis of roller element bearing. The result shows that the proposed method can improve efficiency of the fault diagnosis.

Vibration Reduction by Optimal Design of Powertrain Mounting System of Heavy Truck Based on SQP Method

Heavy truck needs to use the vibration reduction technology to improve its quality. Nowadays, it is a useful and effective way for vibration reduction that by employing the proper Powertrain Mounting Systems (PMS) to reduce the vibration. One useful method to develop more effective mounting systems is through optimization techniques. Sequential Quadratic Programming (SQP) is an effective optimization technique. In this paper, design optimization of powertrain mounting system based on SQP method for vibration control is presented. The optimization objective is to find the highest decoupling ratio of the each mount while selecting the stiffness and orientations of individual mount. The constraints are imposed to keep the desired decoupled ratio in each orientation and the frequency corresponding to the decoupled ratio. A case study is given to validate the proposed method. The result shows that the value of optimized system, such as decoupling ratio, is improved significantly. Therefore, the method proposed in this paper is effective for the optimization of powertrain mounting system.

A Fuzzy C-Means Clustering Method Based for 3D Mechanical Models Retrieval

To accelerate the development of new product, one important method is to use the information of the existed products. In this paper, Fuzzy C-Means clustering method (FCM) is proposed to retrieve the desired model in the given model database. In the retrieval process, first, the shape distribution histograms of the model are established and transformed into the proper format of FCM. Then, the shape distribution histograms of the model are served as inputs of FCM and classified into several groups by this algorithm. Last, all the models that belong to the classification of the query model are returned as the retrieval results. To validate the proposed method, a case study is presented. The results show that the result of proposed method is better than that of Shape Distribution. Thus, this method is suitable for 3D mechanical models retrieval.

Tolerance Modeling and Optimization of XY-Table for LED Die Bonder Based on Multi-Body Systems

High performance XY-table is commonly used in packaging equipments which needs rational tolerance to ensure its high performance requirements and low manufacturing costs. The related studies mainly focus on high speed motion control algorithms and high precision structure design. The proper tolerance is essential in the development process of new products. In this paper, a new method for tolerance optimization based on Multi-body systems is presented. The volumetric error model for XY-table is established based on Multi-body systems. And then, by identifying the basic geometric error components, the relationships between the final output precision of the XY-table and the geometry error sources are established. The tolerance optimization model of XY-table using the above relationships is presented. In this model, objective function is the total manufacturing costs and the constraints are the above relationships. By solving the optimization model, the optimal tolerances are obtained. Finally, a tolerance design example of LED die bonder is given to illustrate the proposed method.

The Tolerance Modeling of LED Die Bonder Based on Multi-Body Systems

LED die bonder is a high precision packaging machine and needs proper tolerance design to ensure its high performance requirements and low manufacturing costs. The related tolerance design methods, such as worst-case method and statistical method, mainly focus on two-dimensional cases in tolerance design. However, tolerance design for LED die bonder needs to deal with three-dimensional cases. To solve the problem, a new method for tolerance design based on Multi-body systems is presented. According to the motion characteristics of the related assembly component parts of LED die bonder, the motion equations in ideal and actual situation for die bonding are given respectively which use the principle of Multi-body systems and the homogeneous transform matrix. By solving the motion equation, we can obtain the tolerances of the related assembly component parts.