Laser powder bed additive manufacturing: A review on the four drivers for an online control

Online control of Additive Manufacturing (AM) processes appears to be the next challenge in the transition toward Industry 4.0 (I4.0). Although many efforts have been dedicated by industry and research in the last decades, there remains substantial room for improvement. Additionally, the existing scientific literature lacks a wide-ranging identification and classification of the primary drivers that enable online control of AM processes. This article focuses on online control of one of the most industrially widespread AM processes: metal Laser Powder Bed Fusion (L-PBF), with particular emphasis on two subcategories, namely Selective Laser Sintering (SLS) and Selective Laser Melting (SLM). Through a systematic literature review, this article initially identified over 200 manuscripts. The search was conducted utilizing a defined research query within the Scopus database, double checked on Scholar. The results were refined through multiple phases of inclusion/exclusion criteria, culminating in the selection of 95 pertinent papers. This article aims to provide a systematic and comprehensive review of four identified drivers i) Online controllable input parameters, ii) Online observable output signatures, iii) Online sensing techniques, iv) Online feedback strategies, adopted from the general Deming control loop Plan-Do-Check-Act (PDCA). Ultimately, this article delves into the challenges and prospects inherent in the online control of metal L-PBF

A Survey of Prediction and Classification Techniques in Multicore Processor Systems

In multicore processor systems, being able to accurately predict the future provides new optimization opportunities, which otherwise could not be exploited. For example, an oracle able to predict a certain application\u27s behavior running on a smart phone could direct the power manager to switch to appropriate dynamic voltage and frequency scaling modes that would guarantee minimum levels of desired performance while saving energy consumption and thereby prolonging battery life. Using predictions enables systems to become proactive rather than continue to operate in a reactive manner. This prediction-based proactive approach has become increasingly popular in the design and optimization of integrated circuits and of multicore processor systems. Prediction transforms from simple forecasting to sophisticated machine learning based prediction and classification that learns from existing data, employs data mining, and predicts future behavior. This can be exploited by novel optimization techniques that can span across all layers of the computing stack. In this survey paper, we present a discussion of the most popular techniques on prediction and classification in the general context of computing systems with emphasis on multicore processors. The paper is far from comprehensive, but, it will help the reader interested in employing prediction in optimization of multicore processor systems

Identification of Partial Discharge Through Cable-Specific Adaption and Neural Network Ensemble

[EN] This paper proposes to administer a multi-step artificial intelligence approach with an ensemble of adaptive neural networks (NNs) trained on 50000 samples to identify partial discharge (PD) diagnostic measurements for in-service medium voltage (MV) power cables. To evaluate the performance of the algorithm, a case study was performed on cables deliberately selected to contain both uncomplicated measurements and disruptive irregularities representative of conditions during field testing. The experimental test results prove that the proposed cable-specific adaptation improves PD identification accuracy, with further increment through the NN ensembles. The main contribution of the approach is in both the cable-specific adaption and the NN ensemble being applied to MV cable field measurements.Yeo, J.; Jin, H.; Rodrigo Mor, A.; Yuen, C.; Tushar, W.; Saha, TK.; Seng Ng, C. (2021). Identification of Partial Discharge Through Cable-Specific Adaption and Neural Network Ensemble. IEEE Transactions on Power Delivery. 1-10. https://doi.org/10.1109/TPWRD.2021.3093670S11

FPGA-accelerated machine learning inference as a service for particle physics computing

New heterogeneous computing paradigms on dedicated hardware with increased parallelization, such as Field Programmable Gate Arrays (FPGAs), offer exciting solutions with large potential gains. The growing applications of machine learning algorithms in particle physics for simulation, reconstruction, and analysis are naturally deployed on such platforms. We demonstrate that the acceleration of machine learning inference as a web service represents a heterogeneous computing solution for particle physics experiments that potentially requires minimal modification to the current computing model. As examples, we retrain the ResNet-50 convolutional neural network to demonstrate state-of-the-art performance for top quark jet tagging at the LHC and apply a ResNet-50 model with transfer learning for neutrino event classification. Using Project Brainwave by Microsoft to accelerate the ResNet-50 image classification model, we achieve average inference times of 60 (10) milliseconds with our experimental physics software framework using Brainwave as a cloud (edge or on-premises) service, representing an improvement by a factor of approximately 30 (175) in model inference latency over traditional CPU inference in current experimental hardware. A single FPGA service accessed by many CPUs achieves a throughput of 600--700 inferences per second using an image batch of one, comparable to large batch-size GPU throughput and significantly better than small batch-size GPU throughput. Deployed as an edge or cloud service for the particle physics computing model, coprocessor accelerators can have a higher duty cycle and are potentially much more cost-effective.Comment: 16 pages, 14 figures, 2 table