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A three-dimensional (3D) thermomechanical coupled model for Ti-6Al-4V alloy has been calibrated through experiments of 40-layers metal deposition using different scanning strategies. The sensitivity analysis of the mechanical parameters shows that the thermal expansion coefficient as well as the elastic limit of Ti-6Al-4V have a great impact on the mechanical behavior. Using the validated model and optimal mechanical parameters, the evolution of thermo-mechanical fields in LSF has been analyzed. It has been found that the stresses and distortions are developed in two stages, after the deposition of the first layer and during the cooling phase after the manufacturing of the component. The cooling phase is the responsible of 70% of the residual stresses and 60% of the total distortions, respectively. These analyses indicate that by controlling the initial substrate temperature (pre-heating phase) and the final cooling phase it is possible to mitigate both distortion and residual stresses. Hence, the influence of different pre-heating procedures on the mechanical fields has been analyzed. The results show that increasing the pre-heating temperature of substrate is the most effective way to reduce the distortions and residual stresses in Additive Manufacturing.

a conjunction of triboelectric effect and electrostatic induction. The high sensitivity of TENG toward tiny vibration, the cost-effective fabrication process, and the flexible device structure provide much possibilities for the smart and interesting applications, such as humidity sensor, [10] pressure sensor, [11] active acoustic sensor, [12] ultraviolet detector, [13] Keystroke dynamics identification, [14] handheld printer, [15] and so on.Machine learning (ML) [16,17] is usually referred to statistical models and algorithms used by computer systems to conduct a specific task without too much complex instructions. ML algorithms are widely used in computer vision, disease diagnosis, email filtering, and signal recognition. ML has close relationship with computational statistics focusing on making predictions using computers. Support vector machine (SVM) [18] is a supervised ML algorithm for linear and nonlinear classification. The SVM training algorithm builds a model based on functional margin, which generates a line or a hyperplane to separate the data into classes. In information theory and ML, by obtaining a set of principal variables, dimension reduction can reduce the dimension of random variables. t-distributed stochastic neighbor embedding (t-SNE) [19] is a approach to reduce high-dimensional datasets and widely used in speech processing, genomic data, natural language processing, and image processing. t-SNE starts by calculating the probability of high-dimensional space points similarity as well as the probability of the corresponding low-dimensional space points similarity to map the multi-dimensional data to a lower dimensional space.With the development of artificial intelligence, it is urgent to empower traditional electronics with the ability to "think," to "analyze," and to "advise." Here, a new product concept namely triboelectric nanogenerator (TENG) based smart electronics via the automatic machine learning data analysis algorithm is proposed. In this work, a simple water processing technique is used to fabricate porous polydimethylsiloxane, together with the weaving copper mesh, forming a high sensitivity flexible TENG. The as-prepared TENG presents high sensitivity for the voice signal and handwriting signal detection with ≈0.2 V amplitude in the common talking and writing condition. Three words' pronunciation are recorded and the ensemble method is used as the machine learning model for the voice signal recognition with a recognition accuracy of 93.3%. To further demonstrate the possibility of applying machine learning algorithm for automatic analysis and recognition, larger database is analyzed. Twenty-six letters' handwriting signals with total 520 samples are collected and a letter fingerprint library is established for further analysis. Hierarchical clustering and similarity matrix are used to study the intrinsic relationship between letters. "Medium Gaussian support vector machine" is used as machine learning model for the 26-letter fingerprint identification with recognition accuracy of...

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The present study aims to reveal the mechanism of element vaporization of Ti-6Al-4V alloy during selective laser melting (SLM). The equations of Redlich–Kister and the thermodynamics principles were employed to calculate the vaporization thermodynamics, which contributes to the obtaining the vaporization kinetic based on the Chapman-Enskog theory and the diffusion model. According to the achieved vaporization model, the elements with the most prominent tendency and flux to vaporize were distinguished. Moreover, the effect of the process parameters on the vaporization of Al and Ti is experimentally investigated using inductively coupled plasma optical emission spectrometer (ICP) technology. The analyzed results of the chemical composition of the powders and builds show a great agreement with the kinetic results calculated by the vaporization model. Notably, the element vaporization can be curbed by regulating the laser energy input.