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The piezoresistive response of specimens made by electrically conductive polylactic acid (PLA) under monotonic and cyclic flexural loading was experimentally investigated. A material extrusion process was utilized to 3D print the conductive PLA/carbon black nanocomposite samples using three infill patterns and three infill ratios. Then three-point bending tests, and electrical resistance measurements, were simultaneously conducted on beam-type specimens. The results of the electromechanical tests demonstrated that the 3D printed samples show outstanding piezoresistive sensing characteristics with excellent cyclic stability and reproducibility suitable for in-situ damage detection and strain monitoring through their electrical resistance change. Determination of gauge factors of specimens with different infill ratios and patterns exhibited a significant sensitivity even at low infill ratio. The potential of the piezoresistive property of conductive PLA/carbon black nanocomposite was examined by proposing 3D printed sensors for strain monitoring. The results demonstrated that conductive feedstock can be successfully processed by this material extrusion technique to develop advanced 3D printing sensing solutions. The applicability of the piezoresistive property of conductive cellular structures 3D printed with PLA/carbon black feedstock as a suitable technique for structural health monitoring was demonstrated.

ABSTRACTPollutant emissions into the atmosphere are recognized as a significant problem in fossil fuel combustion. The pollution emission measurement in industrial boilers is difficult and expensive but fundamental for monitoring and controlling. Frequently continuous emissions monitoring (CEM) system is out of service or useless due to obsolescence, high maintenance cost, and so on or simply is not installed. When a system for measuring pollutant emissions is not available, an alternative method must be employed to get the pollutant emission value. According to the black-box model approach, this article describes the nonlinear modeling of NOx emissions from a utility boiler. Bayesian-Gaussian (BG), multilayer perceptron (MLP), and Volterra polynomial basis functions (VPBF) neural networks are developed for model benchmarking. Experimental data from a utility boiler was acquired in order to model definition and evaluation. The models process three boiler variables oxygen excess, fuel mass flow and flue gas recirculation gates for NOx emission estimation. Models with BG show better performance than models with MLP and VPBF for NOx prediction.Implications: The technology to control NOx emissions generated by combustion operates under strict regulations. In order to reduce NOx emissions, theoretical models of NOx generation have been studied extensively, including nitrogen chemistry and the dynamic flow of gas particles which is very complex. The new technology trends would require the continuous measurement of high precision NOx emissions to achieve further reductions in NOx emissions. Currently, NOx emissions are measured by a Continuous Emission Monitoring system, which turns out to be extremely expensive and difficult to maintain, so alternative low-cost solutions are desirable. Our contribution shows how algorithms based on different artificial intelligence techniques are viable and quality alternatives for the measurement of continuous NOx emissions. The NOx emissions models based on IA algorithms are viable alternatives that have versatility and self-tuning capacity due to the fact that they are based on boiler operation parameters which have valuable information few explored nowadays.