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The assembly simulation in tolerance analysis is one of the most important steps for the tolerance design of mechanical products. However, most assembly simulation methods are based on the rigid body assumption, and those assembly simulation methods considering deformation have a poor efficiency. This paper aims to propose a novel efficient and precise tolerance analysis method based on stable contact to improve the efficiency and reliability of assembly deformation simulation.

The proposed method comprehensively considers the initial rigid assembly state, the assembly deformation and the stability examination of assembly simulation to improve the reliability of tolerance analysis results. The assembly deformation of mating surfaces was first calculated based on the boundary element method with optimal initial assembly state, then the stability of assembly simulation results was assessed by the density-based spatial clustering of applications with noise algorithm to improve the reliability of tolerance analysis. Finally, combining the small displacement torsor theory, the tolerance scheme was statistically analyzed based on sufficient samples.

A case study of a guide rail model demonstrated the efficiency and effectiveness of the proposed method.

The present study only considered the form error when generating the skin model shape, and the waviness and the roughness of the matching surface were not considered.

To the best of the authors’ knowledge, the proposed method is original in the assembly simulation considering stable contact, which can effectively ensure the reliability of the assembly simulation while taking into account the computational efficiency.

This paper aims to develop a method to improve the accuracy of tolerance analysis considering the spatial distribution characteristics of part surface morphology (SDCPSM) and local surface deformations (LSD) of planar mating surfaces during the assembly process.

First, this paper proposes a skin modeling method considering SDCPSM based on Non-Gaussian random field. Second, based on the skin model shapes, an improved boundary element method is adopted to solve LSD of nonideal planar mating surfaces, and the progressive contact method is adopted to obtain relative positioning deviation of mating surfaces. Finally, the case study is given to verify the proposed approach.

Through the case study, the results show that different SDCPSM have different influences on tolerance analysis, and LSD have nonnegligible and different influence on tolerance analysis considering different SDCPSM. In addition, the LSD have a greater influence on translational deviation along the z-axis than rotational deviation around the x- and y-axes.

The surface morphology with different spatial distribution characteristics leads to different contact behavior of planar mating surfaces, especially when considering the LSD of mating surfaces during the assembly process, which will have further influence on tolerance analysis. To address the above problem, this paper proposes a tolerance analysis method with skin modeling considering SDCPSM and LSD of mating surfaces, which can help to improve the accuracy of tolerance analysis.

This paper aims to propose a framework for optimizing the pose in the assembly process of the non-ideal parts considering the manufacturing deviations and contact deformations. Furthermore, the accuracy of the method would be verified by comparing it with the other conventional methods for calculating the optimal assembly pose.

First, the surface morphology of the parts with manufacturing deviations would be modeled to obtain the skin model shapes that can characterize the specific geometric features of the part. The model can provide the basis for the subsequent contact deformation analysis. Second, the simulated non-nominal components are discretized into point cloud data, and the spatial position of the feature points is corrected. Furthermore, the evaluation index to measure the assembly quality has been established, which integrates the contact deformations and the spatial relationship of the non-nominal parts’ key feature points. Third, the improved particle swarm optimization (PSO) algorithm combined with the finite element method is applied to the process of solving the optimal pose of the assembly, and further deformation calculations are conducted based on interference detection. Finally, the feasibility of the optimal pose prediction method is verified by a case.

The proposed method has been well suited to solve the problem of the assembly process for the non-ideal parts with complex geometric deviations. It can obtain the reasonable assembly optimal pose considering the constraints of the surface morphological features and contact deformations. This paper has verified the effectiveness of the method with an example of the shaft-hole assembly.

The method proposed in this paper has been well suited to the problem of the assembly process for the non-ideal parts with complex geometric deviations. It can obtain the reasonable assembly optimal pose considering the constraints of the surface morphological features and contact deformations. This paper has verified the method with an example of the shaft-hole assembly.

The different surface morphology influenced by manufacturing deviations will lead to the various contact behaviors of the mating surfaces. The assembly problem for the components with complex geometry is usually accompanied by deformation due to the loading during the contact process, which may further affect the accuracy of the assembly. Traditional approaches often use worst-case methods such as tolerance offsets to analyze and optimize the assembly pose. In this paper, it is able to characterize the specific parts in detail by introducing the skin model shapes represented with the point cloud data. The dynamic changes in the parts' contact during the fitting process are also considered. Using the PSO method that takes into account the contact deformations improve the accuracy by 60.7% over the original method that uses geometric alignment alone. Moreover, it can optimize the range control of the contact to the maximum extent to prevent excessive deformations.

The goal is to perform an in‐situ measurement of dust layer thickness on bag filters.

Presents a machine vision system in terms of hard‐ and software which is capable of obtaining the relative height difference of a cloth filter surface in different states of operation. The measurement process starts with a stereo reconstruction of filter surface patches. The patches are aligned to form a continuous model using the iterative closest point algorithm. Models acquired at different states of operation are registered using thin plate spline interpolation and afterwards subtracted from each other to obtain the relative height difference.

Relative thickness measurement is possible with an accuracy ranging from 50 to 300 μm, depending on the operational states.

In‐situ measurement of dust deposition is a new tool for examining the behaviour of commercial bag filters.

Provides the foundation for a system measuring both thickness and density of dust deposition.

Applies findings in the field of machine vision to a problem in process engineering and filter design.

Complex and exquisite patterns are sculpted on the surface to beautify the parts. Due to the thin-walled nature, the blank of the part is often deformed by the forming and clamping processes, disabling the nominal numerical control (NC) sculpting programs. To address this problem, a fast adaptive sculpting method of the complex surface is proposed.

The geometry of the blank surface is measured using on-machine measurement (OMM). The real blank surface is reconstructed using the non-uniform rational basis spline (NURBS) method. The angle-based flattening (ABF) algorithm is used to flatten the reconstructed blank surface. The dense points are extracted from the pattern on the image using the OpenCV library. Then, the dense points are quickly located on the complex surfaces to generate the tool paths.

By flattening the reconstructed surface and creating the mapping between the contour points and the planar mesh triangular patches, the tool paths can be regenerated to keep the contour of the pattern on the deformed thin-walled surface.

The proposed method can adjust the tool paths according to the deformation of the thin-walled part. The consistency of sculpting patterns is improved.

Non‐linear transformation of freeform curves and surfaces is useful in computer‐aided design and computer graphics. It is highly desirable that the original and transformed curves or surfaces are defined using the same representation. But freeform curves and surfaces defined by control points are invariant only under affine transformations, and not so under non‐linear transformations. This paper develops a method that can perform non‐linear transformations of freeform curves to specific accuracies, while retaining the same representation. It involves first applying the transformation to the control points and then modifying them so that the resulting curve and the exact transformed curve are equal at a specific number of points, which is the number of control points. Refinement to the approximation is made by increasing the number of control points. A method for measuring the maximum positional error has been implemented and this is used to facilitate an algorithm for automatic refinement. Extension of the method for surfaces is also given.

Precision aluminium moulding makes possible the production of large‐size, complex and high‐technology cast parts. However, industrial requirements linked to economic and safety reasons call into question the manual performance of finishing operations. The purpose of this paper is to enhance industrial robot applications by using vision and redundancy optimization to improve their capability.

After having presented the concepts associated with machine and kinematics capability, the paper first describes the finishing constraints related to the process and the study of inaccuracy factors. Adjusting the trajectory by vision minimizes some inaccuracy factors but does not take into account the structure loading. Therefore, the authors present the optimization, kinematics and precision criteria as well as the multi‐objective method developed by integrating the loading aspect. This method has been verified by simulation and the results validated on industrial parts.

The paper presents an improvement in machine capacities based on redundancy and an optical 3D measurement system. It develops the strategies, sensors and cell architecture to perform finishing operations.

The finishing of high‐technology structural cast parts requires the completion of the machining and polishing processes adapted to each part. The choice was made to develop a robotic cell dedicated to integrating specific features, in contrast to machine tools.

This study was carried out within the framework of the Eureka SANDCAST project in cooperation with the Alcan group, specialized in high‐technology moulded aluminum parts.

The paper presents an approach to robotic cell capability improvement. The robotic cell is dedicated to finishing operations, by machining and polishing large cast aluminum parts; the objectives are to improve machine capability and kinematics capacity with vision and redundancy management.

The paper's aim is to predict numerically the contact temperatures between two rough sliding bodies and to compare with the experimental results.

An elastic contact algorithm is used to analyze the normal contact between two nominally smooth surfaces. The algorithm evaluates real contact area using digitized roughness data and the corresponding contact pressure distribution. Using finite element method a steady state 3D temperature distribution at the interface between the sliding bodies is obtained. Using infrared (IR) imaging technique, experiments were carried out to measure the contact temperature distribution between rough rubbing bodies with a systematic variation of surface roughness and operating variables.

Contact temperature distributions over a wide range of normal load, sliding velocity and surface roughness have been obtained. It was seen that the maximum contact temperature expectedly increases with surface roughness (S_a values), normal load and sliding velocity. The results also indicate that the “hot spots” are located exactly at the positions where the contact pressures are extremely high. Temperatures can be seen to fall drastically at areas where no asperity contacts were established. The temperature contours at different depths were also plotted and it was observed that the temperatures fall away from the actual contact zone and relatively high temperatures persist at the “hot spot” zones much below the contact surface. Finally it is encouraging to find a good correlation between the numerical and experimental results and this indicates the strength of the present analysis.

Experimental accuracy can be improved by using a thermal imaging camera that measures emissivity in situ and uses it to find the contact temperature. The spatial resolution and the response time of the camera also need to be improved. This can improve the correlation between numerical and experimental results.

One of the major factors attributed to the failure of sliding components is the frictional heating and the resulting flash temperatures at the sliding interface. However, it is not easy to measure such temperatures owing to the inherent difficulties in accessing the contact zone. Besides, thermal imaging techniques can be applied only with such tribo‐pairs where at least one of the contacting materials is transparent to IR radiation. In practice, such cases are a rarity. However, the good correlation observed between the numerical and experimental results in this work would give the practicing engineer a confidence to apply the numerical model directly and calculate contact temperatures for any tribo‐material pairs that are generally seen around.

A good correlation between the numerical and experimental results gives credence to the fact that the numerical model can be used to predict contact temperatures between any sliding tribo‐pairs.

Assembly accuracy is the guarantee of mechanical product performance, and the characterization of the part with geometrical deviations is the basis of assembly accuracy analysis.

The existed small displacement torsors (SDT) model cannot fully describe the part with multiple mating surfaces, which increases the difficulty of accuracy analysis. This paper proposed an integrated characterization method for accuracy analysis. By analyzing the internal coupling relationship of the different geometrical deviations in a single part, the Monomer Model was established.

The effectiveness of the Monomer Model is verified through an analysis of a simulated rotor assembly analysis, and the corresponding accuracy analysis method based on the model reasonably predicts the assembly deviation of the rotor.

The Monomer Model realizes the reverse calculation of assembly deformation for the first time, which can be used to identify the weak links that affect the assembly accuracy, thus support the accuracy improvement in the re-assembly stage.

The purpose of this paper is to study the wave propagation in a non-homogenous semiconducting medium through the photothermal process using the fractional order photo-thermoelastic without neglecting the coupling between the plasma and thermoelastic waves that photogenerated through traction free and loaded thermally by exponentially decaying pulse boundary heat flux.

The analytical solutions in the transformed domain by the eigenvalue approach were observed through the transform techniques of Laplace.

Silicon-like semiconductor was used to achieve the numerical computations.

Some comparisons are shown in the figures to estimate the effects of the fractional order and non-homogeneous parameters.