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This work aims at constructing a continuous mathematical, linear elastic, model for the thermal contact conductance (TCC) of two rough surfaces in contact.

The rough surfaces, known to be physical fractal, are modelled using a deterministic Cantor structure. Such structure shows several levels of imperfections and including, therefore, several scales in the constriction of the flux lines. The proposed model will study the effect of the deformation (approach) of the two rough surfaces on the TCC as a function of the remotely applied load.

An asymptotic power law, derived using approximate iterative relations, is used to express the area of contact and, consequently, the thermal conductance as a function of the applied load. The model is valid only when the approach of the two surface in contact is of the order of the surface roughness. The results obtained using this model, which admits closed form solution, are displayed graphically for selected values of the system parameters; the fractal surface roughness and various material properties. The obtained results showed good agreement with published experimental results both in trend and the numerical values.

The model obtained provides further insight into the effect that surface texture has on the heat conductance process. The proposed model could be used to conduct an analytical investigation of the thermal conductance of rough surfaces in contact. This model, although simple (composed of springs), nevertheless works well.

To calculate the volume deviation between a CAD model and built‐up part in 5‐axis laminated object manufacturing employing direct slicing with first‐order approximation.

It is proposed here that the deviation between the CAD model and the built‐up part, which is normally calculated as a linear dimension in specific 2D sections of the CAD model, be treated as a volume (as it actually is), for higher accuracy in subsequent calculations. An algorithm has been developed and implemented for identification and calculation of volume deviation, considering all possibilities.

It has been conclusively shown that volume deviation consideration results in improved feature recognition and less approximation.

Increase in complexity of the CAD model leads to a considerable increase in the volume deviation computation time. Future research in this area would focus on optimization and calculation of the slice heights based on volume deviation.

Calculation of volume deviation would help eliminate the loss of intricate features in a complex surface and thus improve feature recognition. Slice height calculations based on volume deviation would reduce the deviation between the actual model and the built‐up part.

A new method has been developed for the calculation of volume deviation that could be implemented in the rapid prototyping software packages so as to build prototypes with higher accuracy.

The surface energy per unit area of material is known to be proportional to the thermal energy at the melting point of the material. The purpose of this paper is to employ the values of the melting points of metals to develop a model that estimates the average surface energies of metals. Average surface energy estimator (ASEE) was developed with the aid of computational intelligence technique on the platform of support vector regression (SVR) using the values of the melting point of the materials as the descriptor.

The development of ASEE which involves 12 data set was conducted by training and testing SVR model using test-set-cross-validation technique. The developed model (ASEE) was used to estimate average surface energies of 3d, 4d, 5d and other selected metals in the periodic table. The average surface energies obtained from ASEE are in good agreement with the experimental values and with the values from other theoretical models.

The accuracy of this developed model coupled with its adoption of descriptor that can be easily obtained makes it a viable alternative in circumventing the difficulty experienced in experimental determination of average surface energies of materials.

Modeling of ASEE has never been reported in the literature. Meanwhile, the use of ASEE will help circumvent the difficulties involved in the experimental determination of average surface energies of materials.

The implied volatility surface is the collection of volatilities implied by option contracts for different strike prices and time-to-maturity. We study factor models to capture the dynamics of this three-dimensional implied volatility surface. Three model types are considered to examine desirable features for representing the surface and its dynamics: a general dynamic factor model, restricted factor models designed to capture the key features of the surface along the moneyness and maturity dimensions, and in-between spline-based methods. Key findings are that: (i) the restricted and spline-based models are both rejected against the general dynamic factor model, (ii) the factors driving the surface are highly persistent, and (iii) for the restricted models option Δ is preferred over the more often used strike relative to spot price as measure for moneyness.

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.

This paper aims to characterize the mineral composition of Martian surfaces based on Thermal Emission Spectrometer (TES; Mars Global Surveyor) as measured in the infrared thermal range. It presents modeling and interpreting of TES spectral data from selected Martian regions from which the atmospheric influences had been removed using radiative transfer algorithm and deconvolution algorithm. The spectra from the dark area of Cimmeria Terra and the bright Isidis Planitia were developed in Philip Christensen’s and Joshua Bandfield’s publications, where these spectra were subjected to spectral deconvolution to estimate the mineral composition of the Martian surface. The results of the analyses of these spectra were used for the modeling of dusty and non-dusty surface of Mars. As an additional source, the mineral compositions of Polish basalts and mafic rocks were used for these surfaces as well as for modeling Martian meteorites Shergottites, Nakhlites and Chassignites. Finally, the spectra for the modeling of the Hellas region were obtained from the Planetary Fourier Spectrometer (PFS) – (Mars Express) and the mineralogical compositions of basalts from the southern part of Poland were used for this purpose. The Hellas region was modeled also using simulated Martian soil samples Phyllosilicatic Mars Regolith Simulant and Sulfatic Mars Regolith Simulant, showing as a result that the composition of this selected area has a high content of sulfates. Linear spectral combination was chosen as the best modeling method. The modeling was performed using PFSLook software written in the Space Research Centre of the Polish Academy of Sciences. Additional measurements were made with an infrared spectrometer in thermal infrared spectroscopy, for comparison with the measurements of PFS and TES. The research uses a kind of modeling that successfully matches mineralogical composition to the measured spectrum from the surface of Mars, which is the main goal of the publication. This method is used for areas where sample collection is not yet possible. The areas have been chosen based on public availability of the data.

The infrared spectra of the Martian surface were modeled by applying the linear combination of the spectra of selected minerals, which then are normalized against the measured surface area with previously separated atmosphere. The minerals for modeling are selected based on the expected composition of the Martian rocks, such as basalt. The software used for this purpose was PFSLook, a program written in C++ at the Space Research Centre of the Polish Academy of Sciences, which is based on adding the spectra of minerals in the relevant percentage, resulting in a final spectrum containing 100 per cent of the minerals.

The results of this work confirmed that there is a relationship between the modeled, altered and unaltered, basaltic surface and the measured spectrum from Martian instruments. Spectral deconvolution makes it possible to interpret the measured spectra from areas that are potentially difficult to explore or to choose interesting areas to explore on site. The method is described for mid-infrared because of software availability, but it can be successfully applied to shortwave spectra in near-infrared (NIR) band for data from the currently functioning Martian spectroscopes.

This work is the only one attempting modeling the spectra of the surface of Mars with a separated atmosphere and to determine the mineralogical composition.

This study aims to establish a friction coefficient model relative to the rotation speed of a wet clutch engagement, which can predict friction coefficient under different stages of slipping velocity and different load pressures. In particular, the model has been improved by accounting the speed effect for the perdition of wet friction-element boundary friction, which is significant for understanding the friction mechanisms and for supporting the development of more efficient and related products.

This research investigated the mechanism of wet friction in a wet clutch engagement. A mixed friction model is established based on the asperity model and Newton’s law of viscosity. To obtain a friction coefficient computed by the model, the normal load shared by both asperities and lubrication fluid needs to be determined. Therefore, rough surface contact mechanism is analysed; a surface topography model is established; and surface parameters are obtained by means of surface topography measurement and reconstruction. Finally, verification of the mixed friction model is achieved.

Friction will be generated by both the asperity contact and the lubrication film shear relative to the rotation speed. And, the higher the relative speed, the larger the shearing power of lubrication film. It is caused by decrease in contact area of asperity. Surface morphology of a sintered bronze friction disk was obtained by a Laser-Micro-Test. The predicted results by the established model show that the total friction coefficient slightly reduced and then increased suddenly with speed. The surface topography model is responsible for the nonlinear behaviour of the asperity friction. Results of the simulation model are in agreement with those of the wet clutch engagement experiments.

This research is original and it is supported by the national defence project. The wet friction element which is applied on tracked vehicles is analysed for the first time. Through the model, the trend of the friction coefficient can be more accurately predicted. The problem of the wet friction plate modelling difficult is solved by using the mixed friction model.

This study aims to compute 3D model similarity by extracting and comparing shape features from the neutral files.

In this work, the clear text encoding document STEP (Standard for The Exchange of Product model data) of 3D models was analysed, and the models were characterized by two-depth trees consisting of both surface and shell nodes. All surfaces in the STEP files can be subdivided into three kinds, namely, free, analytical and loop surfaces. Surface similarity is defined by the variation coefficients of distances between data points on two surfaces, and subsequently, the shell similarity and 3D model similarity are determined using an optimal algorithm for bipartite graph matching.

This approach is used to experimentally verify the effectiveness of the 3D model similarity algorithm.

The novelty of this study research lies in the computation of 3D model similarity by comparison of all surfaces. In addition, the study makes several key observations: surfaces reflect the most information concerning the functions and attributes of a 3D model and so the similarity between surfaces generates more comprehensive content (both external and internal); semantic-based 3D retrieval can be obtained under the premise of comparison of surface semantics; and more accurate similarity of 3D models can be obtained using the optimal algorithm of bipartite graph matching for all surfaces.

To improve the quality of the additive manufacturing (AM) products, it is necessary to estimate surface roughness distribution in advance. Although surface roughness estimation has been previously studied, factors leading to the creation of a rough surface and a comprehensive test for model validation have not been adequately investigated. Therefore, this paper aims to establish a robust model using empirical data based on optimized artificial neural networks (ANNs) to estimate the surface roughness distribution in fused deposition modelling parts. Accordingly, process parameters such as time, cost and quality should be optimized in the process planning stage.

Process parameters were selected via a literature review of surface roughness estimation modelling by analytical and empirical methods, and then a specific test part was fabricated to provide a complete evaluation of the proposed model. The ANN structure was optimized by trial and error method and evolutionary algorithms. A novel methodology based on the combination of the intelligent algorithms including the ANN, linked to the particle swarm optimization (PSO) and imperialist competitive algorithm (ICA), was developed. The PSOICA algorithm was implemented to increase the capability of the ANN to perform much faster and converge more precisely to favorable results. The performances of the ANN models were compared to the most well-known analytical models at build angle intervals of equal size. The most effective process variable was found by sensitivity analysis. The validity of proposed model was studied comprehensively where different truncheon parts and medical case studies including molar tooth, skull, femur and a custom-made hip stem were built.

This paper presents several improvements in surface roughness distribution modelling including a more suitable method for process parameter selection according to the design criteria and improvements in the overall surface roughness of parts as compared to analytical methods. The optimized ANN based on the proposed advanced algorithm (PSOICA) represents precise estimation and faster convergence. The validity assessment confirms that the proposed methodology performs better in varied conditions and complex shapes.

This research fills an important gap in surface roughness distribution estimation modelling by using a test part designed for that purpose and optimized ANN models which uses purely empirical data. The novel PSOICA combination enhances the ability of the ANN to perform more accurately and quickly. The advantage in using actual surface roughness values is that all factors resulting in the creation of a rough surface are included, which is impossible if other methods are used.

TruSurf is a new system for building solid objects from layers with sloping surfaces that closely match the designed surface shape. The advantages of using sloping surfaces over stepped edges are improved surface finish, and decreased build time through the use of thicker layers. TruSurf uses B‐spline surfaces to describe the part, and calculate the sloped path of the layer cutting medium. Describes operation of the system in detail and presents results from the production of some test parts. Discusses some ways for improving accuracy, including using principal directions of minimum surface curvature, and using a curved cutting medium to produce layers.