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.
Uncertainty in verification results of Ra (arithmetic mean deviation specification) of surface roughness is usually not
considered in actual measurement and evaluation processes. To overcome this problem, a new method for calculation of
expanded uncertainty of Ra is proposed to assure the integrity and validity in verification results in terms of new
generation of international GPS (geometrical product specifications and verification) standards. The method calculates
basic verification results of Raof surface roughness by using principle of least-square. And the expanded uncertainty of
verification results is computed by the relations between information entropy and uncertainty. With the measured result,
the measured parts can be judged if it is acceptable by using the decision rules provided by standard ISO 14253-1.
Finally, the comparison is given between expanded uncertainty and combined standard uncertainty through using a tested
practical measured part. The combined standard uncertainty is computed according to the propagation formula given by
standard ISO 14253-2. Experiment results indicate that expanded uncertainty can be immediately computed by
Information Entropy Principle. Thus the decision can be made whether to accept or to reject the measured component by
the result of Ra and uncertainty. Therefore, this method could reduce the probability both for mis-acceptance rate and the
mis-rejection rate of the measured parts.
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 Halton's 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.
Shape Distribution is fast, simple, and robust method in 3D model retrieve. This method, however, only considers
distances between the objects' shape distribution histograms and ignores the information included. As the result, the
retrieval precision is low. To enhance the retrieve efficiency, a novel method which integrates Shape Distribution and
Self-Organizing Feature Map (SOFM) is proposed. The models' shape distribution histograms are established by Shape
Distribution and transformed into the proper format of SOFM. The similar models are grouped in neighboring neurons of
SOFM by using competitive learning approach. In addition, the dissimilar models are indexed in far away neurons. With
the given query model, SOFM classifies it into the proper cluster and exports the retrieval results. A case study is
presented and the results show that the retrieval precision of the proposed method is higher than that of the Shape
Distribution method.
KEYWORDS: Global Positioning System, Standards development, 3D modeling, 3D metrology, Metrology, Manufacturing, Reliability, Rule based systems, Tolerancing, Calibration
Improved Geometrical Product Specifications (GPS) standards system is the foundation of the technology standards and
metrology specifications of mechanical and electric products. GPS estimation of uncertainty should assure the integrity
and reliability of the verification result of products. According to the requirements of the improved GPS system, the
decision rule based on compliance uncertainty is adopted in this paper to decide whether the flatness can be accepted or
not. Then the calculation equation of compliance uncertainty in three-dimensional flatness measuring process is deduced
based on the basic principle of least-square verification and the transparent box model given in ISO/TS 14253-2. An
experimental research is also given to validate the method proposed in this paper.
In this paper, a particular mechanical servo system is presented based on the design requirement of scanning wafer stage of 0.1 μm lithography. In order to achieve high accuracy and high speed, linear motor and voice coil motor is employed to control long stroke motions and short stroke motions, respectively. Considering extraneous forces resident in the system, a composite movement model with disturbing compensation employing single neuron and multiple inputs is then given. The results of actual application demonstrate that the given system with better robustness can enhance the real-time tracing ability and can satisfy high accuracy at high speed along specified trajectories.
Ordered binary decision diagram (OBDD) is a new kind of typical graph-based data structures for representing Boolean
expressions. Combinatorial explosions are inevitable when storing and manipulating large graphs by conventional data
structures. OBDD provides the deleting and merging rules to realize the compressed storage of graphs. This paper
presents a new method of storage and manipulations of directed graph based on OBDD. The vertices of directed graph
are coded in binary scale so as to express the vertices in Boolean expressions. The edges of directed graph are therefore
expressed in Boolean relations. In this way, the directed graph can be represented by OBDD. With such representations,
we can use the available algorithms based on OBDD and the manipulations of directed graph are implemented by
employing the operations of Boolean functions. The manipulations realized in this paper include computing the input
and/or output degrees of the vertices of directed graphs, inserting edges into the graph, deleting edges from the graph,
locating the edges, and traversing the graph in breadth-first search. A simulation experiment is provided with various
kinds of random generated directed graph. The experimental results demonstrate that the storage of directed graph based
on OBDD is more efficient than that of by adjacency list when dealing with large directed graph.
This paper researches into the modeling method of elevator control system and the algorithm of generating PLC program based on Signal Interpreted Petri Net (SIPN). We also analyze the properties of the SIPN model in the system. SIPN is obtained by adding input and output signals into the ordinary Petri Net. Input signals are related to every transition in SIPN, which express the firing conditions of the transition, while output signals related to the corresponding place, which express the control information of the place. In the SIPN model of the system, the relationships are established from one to one correspondence between the Input and Output (I/O) signals of SIPN and the I/O points of PLC. The places of the SIPN stand for the statuses of the elevator, and the transitions represent the changes of the statuses. The input signals of system are the actions of pressing buttons and some sensor signals, which represent the conditions of changing the states of elevator. The output signals are the actions of elevator control functions, such as stop, open, and close the door, which describe the output control information of the elevator in every place. A case study demonstrates the validity both of the SIPN model and the algorithm.
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