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Simulations quantifying the uncertainty in structural response and damage evolution require accurate representation of the randomness of the underlying material stiffness and strength behaviors. In this paper, the mean and variance descriptions of variability of strength and stiffness of additively manufactured composite specimens are augmented with random field correlation descriptors that represent the process dependence on the property heterogeneity through microstructure variations. Two correlation lengths and a rotation parameter are introduced into randomized stiffness and strength distribution fields to capture the local heterogeneities in the microstructure of Additively Manufactured (AM) composites. We formulated a simulation and Artificial Intelligence (AI)-based technique to calibrate the correlation length and rotation parameter measures from relatively few samples of experimentally obtained strain field observations using Digital Image Correlation (DIC). The neural networks used for calibrating the correlation lengths of Karhunen-Loève Expansion (KL expansion) from the DIC images are trained using simulated stiffness and strength fields that have known correlation coefficients. A virtual DIC filter is used to add the noise and artifacts from typical DIC analysis to the simulated strain fields. A Deep Neural Network (DNN), whose architecture is optimized using Efficient Neural Architecture Search (ENAS), is trained on 150,000 simulated DIC images. The trained DNN is then used for calibration of KL expansion correlation lengths for additively manufactured composite specimens. The AM composites are loaded in tension and DIC images of the strain fields are generated and presented to the DNNs, which produce the correlation coefficients for the random fields as outputs. Compared to classical optimization methods to calibrate model parameters iteratively, neural networks, once trained, efficiently and quickly predict parameters without the need for a robust simulator and optimization methods.

Over the last three years we have been running a large-scale data processing platform for applying analytics to corporate data on a private cloud instance. We control every level in the stack from the processing engines down to the hardware. One very common pattern of usage is for data scientists to use SQL/Hadoop to explore and analysis data sets. Data scientists are free to run whatever queries they want on this shared environment. Here we report on the patterns of usage of data scientists and the measured performance of the queries they create. We motivate why it is difficult to estimate the resource usage of a SQL query on such a system ahead of time and explain the consequences for the design of enterprise datalakes.

The Kafka streaming platform has at its heart a distributed commit log. This resembles the change log that exists in every relational database system. It has been suggested that Kafka be viewed not just as a messaging system, but as the core of a database. The database is in effect “turned inside out” as the normally hidden change log becomes the first class entity of the system, while what is normally considered primary, i.e. the table, view, indexes etc. are just derived from this log. This is appealing as a vision, but raises challenges when applied within an actual enterprise system. The challenges arise from the conflicting interests and requirements of analytics and transactional systems. Running everything on a single system leads to tradeoffs; our intentions here is to identify some of the practical problems with using Kafka as a single data store within an enterprise and to describe our initial approach to resolving them. In particular we present preliminary approaches to ensure consistency and coherence of data from multiple database tables when distributed over Kafka and how to address compliance by encrypting/decrypting data at the Kafka producers and consumers.

We explain our experiences in designing, building and running a large corporate Datalake. Our platform has been running for over two years and makes a wide variety of corporate data assets, such as sales, marketing, customer information, as well as data from less conventional sources such as weather, news and social media available for analytics purposes to many teams across the company. We focus on describing the management of data and in particular how it is transferred and ingested into the platform.

The open-loop process by which 3D printers operate often leads to significant time and material losses due to print failures. A challenge in Additive Manufacturing (AM) is assessing 3D-printed parts mid-print for the dimensional stability of the internal structures and their microstructure and material properties. These internal features are typically opaque to visual inspections after print and can only be accessed for property characterization if the part is cut open. An assessment of methods for evaluating parts in situ using air-coupled ultrasonic transducers in this paper. The purpose is to determine the microstructure and limited effective properties (density and modulus) of internal structures during the print process using acoustic methods. We examine the effectiveness of acoustic wave reflection, absorption and propagation through the infill channels, which make up a part on a fused deposition modeling printer to obtain dimensional measurements while the part is on the print bed. We simulated the acoustic wave response for common frequencies of commercially-available air-coupled transducers (25–300 kHz) placed near simulated 3D-printed parts. We compared the return signals using the k-Wave Acoustics Toolbox, a time-domain model for acoustic wave propagation, and validated these simulations with physical readings. The results showed the behavior of the peak amplitude of the received signals for various materials and infill channel lengths. Simulations and physical experiments were conducted for both through-transmission and pulse-echo approaches. The simulations optimize parameters for a maximized peak received signal strength. The results will limit the scope of computationally expensive methods of observing obfuscated structural deficiencies and deformations in a 3D-printed part in situ using air-coupled ultrasonic transducers and similar ultrasonic NDE technologies.