A Deep Learning Method for Material Performance Recognition in Laser Additive Manufacturing With the development of intelligent manufacturing and Industry 4.0, the traditional methods of material mechanical properties evaluation cannot meet the needs of industrial production due to the shortcomings of wasted materials, tedious processes, and poor accuracy. This paper combines artificial intelligence technology to propose a new material performance evaluation method. The laser additive manufacturing is taken as the research background, three kinds of Ti6Al4V material microstructure images with different properties are used as data sets, based on DenseNet model, a deep convolution neural network NDenseNet is trained to optimize the network model memory and improve the recognition accuracy. The experimental results show that the accuracy of the model reaches 90.4%, loss value remains at 25%. Params and FLOPs are significantly reduced compared with DenseNet model. It only takes 0.1 seconds to process a microstructural image on a GPU processor. This method can greatly reduce the work of researchers, improve product development efficiency in industrial environment, reduce human errors, save production materials, and has guiding significance for the development of highperformance materials.

A RealTime Iterative Machine Learning Approach for Temperature Profile Prediction in Additive Manufacturing Processes Additive Manufacturing (AM) is a manufacturing paradigm that builds threedimensional objects from a computeraided design model by successively adding material layer by layer. AM has become very popular in the past decade due to its utility for fast prototyping such as 3D printing as well as manufacturing functional parts with complex geometries using processes such as laser metal deposition that would be difficult to create using traditional machining. As the process for creating an intricate part for an expensive metal such as Titanium is prohibitive with respect to cost, computational models are used to simulate the behavior of AM processes before the experimental run. However, as the simulations are computationally costly and timeconsuming for predicting multiscale multiphysics phenomena in AM, physicsinformed datadriven machinelearning systems for predicting the behavior of AM processes are immensely beneficial. Such models accelerate not only multiscale simulation tools but also empower realtime control systems using insitu data. In this paper, we design and develop essential components of a scientific framework for developing a datadriven modelbased realtime control system. Finite element methods are employed for solving timedependent heat equations and developing the database. The proposed framework uses extremely randomized trees  an ensemble of bagged decision trees as the regression algorithm iteratively using temperatures of prior voxels and laser information as inputs to predict temperatures of subsequent voxels. The models achieve mean absolute percentage errors below 1% for predicting temperature profiles for AM processes. The code is made available for the research community at https:github.compaularindammliteradditive.