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This study aims to using Fourier transform infrared (FTIR) spectroscopy, Raman spectroscopy and scanning electron microscope/energy dispersive Xray spectrometer to identify different automotive coatings for forensic purpose.

Two four-layered samples in a hit-and-run case were compared layer by layer with three different methods. FTIR spectroscopy was used to primarily identify the organic and inorganic compositions. Raman spectrum and scanning electron microscope/energy dispersive Xray spectrometer (SEM-EDS) were further used to complement the FTIR results.

Two weak and tiny peaks in one layer found between two samples by FTIR, Raman microscope and SEM-EDS verified the result of differences. The study used the three instruments in combination and found it’s effective in sensing coatings, especially in the inorganic additives.

Using these three instruments in combination is more accurate than individually in multilayered coating analysis for forensic purpose.

The three different instruments all present unique information on the composition, and provided similar and mutually verifiable results on the two samples.

With this method, scientists could identify and discriminate important coating evidences with tiny but characteristic differences.

Ballpoint pen ink analysis is important for forensic scientist which can collect valuable information on questioned document. Inks widely used now constitute many substances to improve needed ink characteristics, including the coloring material (dyes or pigments), inks, solvents and resins. The complex components of the inks confront forensic ink chemists with a big challenge to carry out this analysis. However, because of the great importance of determining whether two pieces of written text originated from the same ink, therefore, comparison of different writing inks on a document to forensic scientist, many techniques have been tentatively employed in the ink analysis. In this study, Raman microscope was employed successfully in the analysis of inks. The paper aims to discuss these issues.

Damped or wet samples cannot be analyzed by IR if dehydration is not carried out FTIR observation, because of the extremely strong absorbance of water. Raman spectroscopy can cover the shortage of FTIR spectroscopy because water has weak absorbance in Raman spectroscopy. Raman shifts can give extra peak information because it usually has different positions with FTIR spectroscopy. A Renishaw inVia confocal Raman microscope system with two lasers emitting at 532/633 nm, charge-coupled device detector was employed collecting the Raman spectrum. Laser was chosen for emission in 1-100 percent power depending on the proper energy to get signal strong enough and protect the samples from burning. At least triple-tests were performed for each sample.

The result indicated that Raman spectroscopy could easily remove the interference caused by the paper which could hardly be removed with infrared spectroscopy. No sample preparation was required and only 30 s was needed for each sample in the optimized method. The method proved to be fast, accurate, non-destructive and could be easily applied to the real cases. The primary survey on inks from different market indicated five kinds of inks could be identified. The indicative peaks of each kind of ink (693, 1,150-1,210, 1,280-1,300, 1,360-1,400 and 1,531 cm−1) were summarized. The distributions of the inks in markets were also illustrated. Ink using alkyd resin as connecting materials or copper phthalocyanine as pigment decreased significantly. The data in this study would be helpful for the forensic scientists to identify these inks and examine the questioned document.

No previous studies on the analysis of inks in China market were found to the authors’ best, so the forensic scientists have no idea about the method using Raman for discrimination which proved to be effective, accurate and fast, the indicative peaks of each inks, the distributions in the markets, and therefore, the possibility to encounter specific ink in cases The data in this study would be helpful for the forensic scientists to identify these inks and examine the questioned document.

Three adjustment modes are alternatives for mixed-model assembly lines (MMALs) to improve their production plans according to constantly changing customer requirements. The purpose of this paper is to deal with the decision-making problem between these modes by proposing a novel multi-classification method. This method recommends appropriate adjustment modes for the assembly lines faced with different customer orders through machine learning from historical data.

The decision-making method uses the classification model composed of an input layer, two intermediate layers and an output layer. The input layer describes the assembly line in a knowledge-intensive manner by presenting the impact degrees of production parameters on line performances. The first intermediate layer provides the support vector data description (SVDD) of each adjustment mode through historical data training. The second intermediate layer employs the Dempster–Shafer (D–S) theory to combine the posterior classification possibilities generated from different SVDDs. The output layer gives the adjustment mode with the maximum posterior possibility as the classification result according to Bayesian decision theory.

The proposed method achieves higher classification accuracies than the support vector machine methods and the traditional SVDD method in the numerical test consisting of data sets from the machine-learning repository and the case study of a diesel engine assembly line.

This research recommends appropriate adjustment modes for MMALs in response to customer demand changes. According to the suggested adjustment mode, the managers can improve the line performance more effectively by using the well-designed optimization methods for a specific scope.

The adjustment mode decision belongs to the multi-classification problem featured with limited historical data. Although traditional SVDD methods can solve these problems by providing the posterior possibility of each classification result, they might have poor classification accuracies owing to the conflicts and uncertainties of these possibilities. This paper develops a novel classification model that integrates the SVDD method with the D–S theory. By handling the conflicts and uncertainties appropriately, this model achieves higher classification accuracies than traditional methods.

The electromechanical planetary transmission system has the advantages of high transmission power and fast running speed, which is one of the important development directions in the future. However, during the operation of the electromechanical planetary transmission system, friction and other factors will lead to an increase in gear temperature and thermal deformation, which will affect the transmission performance of the system, and it is of great significance to study the influence of the temperature effect on the nonlinear dynamics of the electromechanical planetary system.

The effects of temperature change, motor speed, time-varying meshing stiffness, meshing damping ratio and error amplitude on the nonlinear dynamic characteristics of electromechanical planetary systems are studied by using bifurcation diagrams, time-domain diagrams, phase diagrams, Poincaré cross-sectional diagrams, spectra, etc.

The results show that when the temperature rise is less than 70 °C, the system will exhibit chaotic motion. When the motor speed is greater than 900r/min, the system enters a chaotic state. The changes in time-varying meshing stiffness, meshing damping ratio, and error amplitude will also make the system exhibit abundant bifurcation characteristics.

Based on the principle of thermal deformation, taking into account the temperature effect and nonlinear parameters, including time-varying meshing stiffness and tooth side clearance as well as comprehensive errors, a dynamic model of the electromechanical planetary gear system was established.