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High-resolution metrology is a critical area of development for nanoscale manufacturing, especially as it affects production throughput and fabrication quality. Atomic force microscopy (AFM) is one of the most popular tools for nanometrology, and high-resolution AFM often requires a significant time commitment and produces datasets of several million points. It is therefore critical for the development of data processing techniques to keep pace with the requirements of analyzing this type of data, and for these techniques to be portable as miniaturization in AFM is becoming more common. This work presents a data fitting algorithm designed for reducing the parameters of large-area data sets which utilizes well-established spline fitting techniques. In this paper we show that basis-spline fitting can be used to accurately represent large AFM data sets, including data sets with noisy data and sharp features, while achieving at least 90% parameter reduction in all test cases.

 

Current metal additive manufacturing (AM) systems suffer from limitations on the minimum feature sizes they can produce during part formation. The microscale selective laser sintering (μ-SLS) system addresses this drawback by enabling the production of parts with minimum feature resolutions of the order of a single micrometer. However, the production of microscale parts is challenging due to unwanted heat conduction within the nanoparticle powder bed. As a result, finite element (FE) thermal models have been developed to predict the evolution of temperature within the particle bed during laser sintering. These thermal models are not only computationally expensive but also must be integrated into an iterative model-based control framework to optimize the digital mask used to control the distribution of laser power. These limitations necessitate the development of a machine learning (ML) surrogate model to quickly and accurately predict the temperature evolution within the μ-SLS particle bed using minimal training data. The regression model presented in this work uses an “Element-by-Element” approach, where models are trained on individual finite elements to learn the relationship between thermal conditions experienced by each element at a given time-step and the element's temperature at the next time-step. An existing bed-scale FE thermal model of the μ-SLS system is used to generate element-by-element tabular training data for the ML model. A data-efficient artificial neural network (NN) is then trained to predict the temperature evolution of a 2D powder-bed over a 2 s sintering window with high accuracy.

 

One of the main challenges facing the expansion of Additive Manufacturing (AM) is the minimum feature sizes which these processes are able to achieve. Microscale Selective Laser Sintering (μ-SLS) is a novel Additive Manufacturing process created to meet this limitation by precisely laser sintering nanoparticles to give a better control over feature sizes. With the development of this new process, there is a concurrent need for models, which can predict the material properties of the sintering nanoparticles. To this end, this paper presents a novel simulation created to predict the electrical resistivity of sintered copper nanoparticles. Understanding the electrical resistivity of nanoparticles under sintering is useful for quantifying the rate of sintering and has applications such as predicting how the nanoparticles will fuse together when subjected to laser irradiation. Such a prediction allows for in situ corrections to be made to the sintering process to account for heat spreading beyond the intended laser irradiation targets. For these applications, it is important to ensure that the predictions of electrical resistivity from the simulations are accurate. This validation must be done against experimental data and since such experimental data does not currently exist, this paper also presents electrical resistivity data for the laser sintering of copper nanoparticles. In summary, this paper details the simulation methodology for predicting electrical resistivity of laser-sintered copper nanoparticles as well as validation of these simulations using electrical resistivity data from original sintering experiments. The key findings of this work are that the simulations can be used to predict electrical resistivity measurements for sintering of actual copper nanoparticles when the copper nanoparticles do not include other materials such as polymer coatings.