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The entropy evaluation method of assembly stress has become a hot topic in recent years. However, the current research can only evaluate the maximum stress magnitude and stress magnitude uniformity, and it cannot evaluate the stress position distribution. In this paper, an evaluation method of stress distribution characterized by strain energy dens...
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Context 1
... hypothetical ideal case and two error cases are shown in Figure 5. The entropy results calculated in the two error cases of Figure 5 are equal, as shown in Table 2. ...
Context 2
... hypothetical ideal case and two error cases are shown in Figure 5. The entropy results calculated in the two error cases of Figure 5 are equal, as shown in Table 2. However, the position distributions of the points in two error cases are totally different. ...
Context 3
... relative entropy of error case 2 is smaller than that of error case 1, which means the point position distribution of error case 2 is more similar with the hypothetical ideal case than error case 1. Table 2. The entropy and relative entropy calculation results of the two error cases in Figure 5. ...
Citations
... Previous research showed that the assembly stress formed during the assembly process was one of the main factors affecting the precision and performance stability of precision electromechanical systems such as high-precision fiber-optic gyroscopes (FOGs) and space synthetic aperture radar (SAR) antennas [2][3][4]. The relation between stress distribution and performance stability of precision electromechanical systems was further evaluated quantitatively based on entropy theory [5,6]. It was concluded that the more uniform the stress distribution, the better the performance stability of precision electromechanical products. ...
... The interface stress and strain of the connection structure need to be monitored in real-time during the assembly process and then should be visualized. This can establish an intuitive and quantitative relationship between the setup parameters and the stress-strain distribution [126,127]. The visual analysis of stress and strain results in auxiliary assembly parameters that help in achieving the target assembly performance of high-end equipment. ...
Aerospace, marine energy, and other high-end equipment have a higher and higher demand for performance indicators. The realization of high-performance manufacturing of equipment is particularly critical, and the assembly is the key link. The continuous expansion of application requirements and the continuous improvement of service performance have resulted in increased difficulty and complexity of assembly. Currently, it is a challenge to effectively realize the initial assembly performance of equipment and its long-term stability. The traditional way of assembly is driven by experience and geometric tolerances have certain randomness and uncertainty. These issues result in low assembly efficiency and accuracy and low one-time pass rate. In service, the service performance is inconsistent with the initial assembly performance, resulting in abnormal vibration or failure of equipment. Thus, performance-oriented digital twin (DT) assembly can overcome the shortcomings of traditional assembly under the high assembly performance (HAP) requirements of high-end equipment. The DT can potentially realize the HAP of high-end equipment using real-time mapping and control of physical entities in virtual space through virtual–real interaction, data fusion, iterative optimization, and other means. However, the performance-oriented DT assembly technology lacks systematic research. Thus, this paper expounds on the connotation of performance-oriented DT assembly technology based on the research status of high-end equipment assembly technology. This article presents a technical framework for performance-oriented DT assembly and discusses the realization approach and key technologies of performance-oriented DT assembly. Finally, its research direction and suggestions are given. The research will reveal the urgent engineering problems of performance-driven active and accurate assembly of high-end equipment and provide a reference for improving assembly performance, consistency, and efficiency.
... For example, the micro constant force locking mechanism in a pendulum accelerometer assembles parts with multiple materials and cross-scale characteristics together through threaded pairs [2]. However, due to the irreversibility of precision product assembly, as well as the unavoidable geometric deviations during thread machining and manufacturing, it is easy to cause the problem of discrete distribution of preload force [3] and uneven distribution of stress [4,5]; the slight deformation and stress concentration of the instrument parts caused can have a great impact on the accuracy [6,7]. For example, for a precision optical system [8], the peak-to-valley (PV) of the wavefront is usually required to be less than 63 nm, and a 1 N change in the preload force will change the wavefront peak-to-valley value (PV) by 2 nm, i.e., a few percentage points of error in the preload force will also result in a considerable change in the PV value. ...
The small-size threaded connection is one of the main connection methods for complex and precision electromechanical products such as aerospace equipment. The geometric deviation is unavoidable in the thread manufacturing process, which will lead to the problem of excessive dispersion of the connection preload and uneven stress distribution, resulting in a low product qualification rate and a short stability period. In this paper, the influence of thread geometric deviation on preload and stress distribution is studied by taking the small-size M2 thread commonly used in high-end aerospace precision products. Firstly, the thread engagement model with geometric parameters is established, and the influence of different types of geometric deviations on the preload is analyzed. Secondly, the mechanism of non-uniform stress distribution on the connector in the process of thread engagement is analyzed. Finally, the accuracy of the simulation model and the analysis results are verified by the small-size thread-tightening experiment. The results show that the pitch diameter deviation, profile angle deviation, and pitch deviation of precision thread affect the preload and stress distribution, among which the profile angle deviation has a significant influence on the preload and stress distribution.
... The assembly accuracy of precision instruments determines their performance accuracy as well as stability. Non-uniformly distributed assembly features significantly decrease the assembly accuracy, thereby causing a reduction in the accuracy of precision instruments [1][2][3]. Form error and stress distributions are two types of assembly features that are mainly concerned with precision assembly processes [2][3][4][5][6][7]. Non-uniform form errors cause position errors and non-uniform contacts on assembly surfaces, leading to stress concentrations and reduction in position accuracy [1,2,8]; non-uniform stress causes deformation in the assembly, and when ambient vibration and temperature variations occur, non-uniform stress is released over time, further changing the assembly state [4,5,9,10]. ...
... Non-uniformly distributed assembly features significantly decrease the assembly accuracy, thereby causing a reduction in the accuracy of precision instruments [1][2][3]. Form error and stress distributions are two types of assembly features that are mainly concerned with precision assembly processes [2][3][4][5][6][7]. Non-uniform form errors cause position errors and non-uniform contacts on assembly surfaces, leading to stress concentrations and reduction in position accuracy [1,2,8]; non-uniform stress causes deformation in the assembly, and when ambient vibration and temperature variations occur, non-uniform stress is released over time, further changing the assembly state [4,5,9,10]. ...
... Recently, Zhang et al. [4] adopted the information entropy theory (IET) to evaluate the distribution characteristics of form errors, and proposed an entropy-based evaluation method to determine the non-uniformity of various assembly features such as form error and stress [2][3][4]6,7]. In comparison with the traditional flatness error evaluation index, the entropy-based evaluation index considers the variability among the overall values of the assembly features; the traditional flatness error evaluation index is only related to a few form error data, while the remaining large amount of form error data is ignored. ...
The non-uniform distribution of assembly features such as form error and stress has a significant influence on the accuracy and stability of precision instruments. Therefore, to improve assembly precision, non-uniformly distributed assembly features must be evaluated in terms of value non-uniformity as well as location non-uniformity. However, the current effective non-uniformity evaluation methods for assembly features only consider the value non-uniformity without considering the influence of the data location. This study proposes a multidimensional entropy evaluation method (MDEEM) and establishes multidimensional entropy evaluation indexes (MDEEIs) to evaluate the non-uniformity of assembly features in a plane region considering both their value and location. First, the mathematical model and MDEEIs for non-uniform data in a one-dimensional (1D) geometric space were developed and verified, wherein the concept of control distance was introduced and the information entropy theory was adopted. Then, the MDEEIs for non-uniform data in a two-dimensional (2D) geometric space were established and verified based on those in the 1D space. Subsequently, the MDEEM was proposed to demonstrate the non-uniformity evaluation process for assembly features in a 2D plane region. Finally, the proposed MDEEM was applied to reveal the distribution characteristics of two types of assembly features that mainly affect the assembly quality, namely form error and stress. This study provides an evaluation method for the non-uniform distribution of various assembly factors in a plane region and aids in the optimization of precision assembly processes.
... The assembly accuracy of precision instruments determines their performance accuracy as well as stability. Non-uniformly distributed assembly features significantly decrease the assembly accuracy, thereby causing a reduction in the accuracy of precision instruments [1][2][3]. Form error and stress distributions are two types of assembly features that are mainly concerned with precision assembly processes [2][3][4][5][6][7]. Non-uniform form errors cause position errors and non-uniform contacts on assembly surfaces, leading to stress concentrations and reduction in position accuracy [1,2,8]; non-uniform stress causes deformation in the assembly, and when ambient vibration and temperature variations occur, non-uniform stress is released over time, further changing the assembly state [4,5,9,10]. ...
... Non-uniformly distributed assembly features significantly decrease the assembly accuracy, thereby causing a reduction in the accuracy of precision instruments [1][2][3]. Form error and stress distributions are two types of assembly features that are mainly concerned with precision assembly processes [2][3][4][5][6][7]. Non-uniform form errors cause position errors and non-uniform contacts on assembly surfaces, leading to stress concentrations and reduction in position accuracy [1,2,8]; non-uniform stress causes deformation in the assembly, and when ambient vibration and temperature variations occur, non-uniform stress is released over time, further changing the assembly state [4,5,9,10]. ...
... Recently, Zhang et al. [4] adopted the information entropy theory (IET) to evaluate the distribution characteristics of form errors, and proposed an entropy-based evaluation method to determine the non-uniformity of various assembly features such as form error and stress [2][3][4]6,7]. In comparison with the traditional flatness error evaluation index, the entropy-based evaluation index considers the variability among the overall values of the assembly features; the traditional flatness error evaluation index is only related to a few form error data, while the remaining large amount of form error data is ignored. ...
In optical precision assembly, most optical components are fixed by multi-point adhesive bonding. However, the stress fields generated in lenses by radial stress in the adhesive significantly influence the imaging quality of precision optical lenses. To date, the adhesive bonding process is conducted empirically, meaning that the stress in lenses cannot be actively or quantitatively controlled. Therefore, to improve the current passive process, a theoretical mechanical model of an optical lens was established, and a novel stress potential function was proposed to derive the analytical solution of stress components in lenses under an arbitrary number of multi-point radial stresses. The results revealed a quantitative relationship between the radial stress in the adhesive and the stress field in the optical lens. Furthermore, a new experimental device was developed, and a systematic experimental method was proposed to apply multi-point quantitative radial loads on an optical lens and measure its real-time stress distribution, which further verifies the accuracy and validity of the proposed theoretical model. This study provides a theoretical approach for the quantitative control of lens stress and optimization of adhesive bonding configuration in optical precision assembly process, which is a basis to enhance the imaging accuracy of precision optical instruments.
... These methods are mostly influenced by subjective factors and cannot fully guarantee the objectivity of the green campus sustainability assessment process and results [16]. The objective evaluation methods also only consider the degree of variation of indicators in the overall index and the degree of influence on other indicators and fail to assign different weights to different assessment objects [17]. Therefore, this paper introduces the self-learning comprehensive evaluation method, which is the application of self-learning techniques in evaluation. ...
With the change in energy utilization, a fast and accurate evaluation method is of great importance to promote green campus sustainability. In order to improve the feasibility and timeliness of evaluation, an intelligent evaluation model based on dynamic Bayesian inference and adaptive network fuzzy inference system (DBN-ANFIS) is proposed. Firstly, from the perspective of sustainability and considering the changes in energy utilization, a green campus evaluation index system is constructed from four levels: campus resource utilization, campus environment creation, campus usage management, and campus eco-efficiency. On this basis, the parameters of the adaptive network fuzzy inference system (ANFIS) are optimized based on dynamic Bayesian inference (DBN), so as to apply the modified model to the green campus evaluation work of the Spark big data operation platform. Finally, the scientificity of the model proposed in this paper is verified through example analysis, which is conducive to the real-time and effective evaluation of green campus sustainability and provides scientific and rational decision support to improve its management.
... 33 Wang et al. took RE as the optimization objective to optimize the precise assembly process. 34 In order to assess the energy loss in the side channel pump, the entropy production method was carried out by Zhang et al. 35,36 Zhao et al. used relative layer entropy to select the appropriate feature layer of image representation. 37 The aforementioned explorations have achieved laudable results. ...
In condition monitoring and prognostics health management, it is very important to extract the useful components of equipment state signals. In this paper, combining variational mode decomposition (VMD) and relative entropy (RE), a novel approach is proposed for extracting signal useful components. By using VMD, the original vibration signal can be adaptively decomposed, and its effective constituents can be acquired through the assessment of RE. The proposed method is further applied into some simulated and measured signals of a hydraulic axial piston pump. The effectiveness and feasibility of the proposed method are demonstrated through the numerical and tested vibration signals. The results show that the proposed method possesses laudable capability to extract the effective component of vibration signals for a hydraulic axial piston pump under normal state, slipper wear, and slipper luxation. The interference of background noise is effectively overcome. Furthermore, the expected useful signals are precisely reconstituted.
Assembly affects the product's performance and reliability directly. The current assembly method based on geometric deviation quantity controlling, cannot guarantee the physical performance for complex aeronautical thin-walled structures effectively, such as assembly geometry accuracy and internal interactive stress. And assembly performance controlling is taken as the bottleneck problem that restricting the new aviation requirement of sub-millimeter assembly. In this paper, by proposing the accurate prediction and process-oriented adjustment&controlling strategies on assembly quality, construction on working mode with “quantifiable and controllable” characteristic was proposed, whose aim was to reduce the phenomenon of out-of-tolerance and deformation rebound error, and the ultimate goal is to reduce the uncertainty of assembly performance parameters. At the technical level, the academic development context and existing problems for assembling thin-walled structures were reviewed and analyzed, such as assembly process parameters optimization, assembly error transfer and accumulation, comprehensive adjustment on assembly quality, and virtual assembly simulation validation. Then the key future research trends for aeronautical structure assembly were also put forward, i.e. the force/deformation coordination among multi-type finite units for non-ideal assemblies, the dynamic construction of stiffness matrix for intermediate assemblies considering geometric nonlinearity, the adaptive balance on assembly performance driven by physical modeling and measured data, and the inverse optimization on assembly quality and parameters with intelligent data processing. This paper would lay a solid foundation for achieving the accurate assembly mode with the characteristics of “intelligent/scientific, and active/collaborative controlling on geometric shape and physical performance”, and higher assembly quality and efficiency could also be gained.
