A Coupled Thermoreflectance Thermography Experimental System and Ultra-Fast Adaptive Computational Engine for the Complete Thermal Characterization of Three-Dimensional Electronic Devices : Validation

This work builds on the previous introduction [1] of a coupled experimental-computational system devised to fully characterize the thermal behavior of complex 3D submicron electronic devices. The new system replaces the laser-based surface temperature scanning approach with a CCD camera-based approach. As before, the thermo-reflectance thermography system is used to non-invasively measure with submicron resolution the 2D surface temperature field of an activated device. The measured temperature field is then used as input for an ultra-fast inverse computational solution to fully characterize the thermal behavior of the complex three-dimensional device. For the purposes of this investigation, basic micro-heater devices were built, activated, and measured. In order to quantitatively validate the coupled experimental-computational system, the system was used to extract geometric features of a known device, thus assessing the system's ability to combine measured experimental results and computations to fully characterize complex 3D electronic devices

Strain state detection in composite structures: Review and new challenges

Developing an advanced monitoring system for strain measurements on structural components represents a significant task, both in relation to testing of in-service parameters and early identification of structural problems. This paper aims to provide a state-of-the-art review on strain detection techniques in composite structures. The review represented a good opportunity for direct comparison of different novel strain measurement techniques. Fibers Bragg grating (FBG) was discussed as well as non-contact techniques together with semiconductor strain gauges (SGs), specifically infrared (IR) thermography and the digital image correlation (DIC) applied in order to detect strain and failure growth during the tests. The challenges of the research community are finally discussed by opening the current scenario to new objectives and industrial applications

NASA SBIR abstracts of 1991 phase 1 projects

The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included

Machine Learning Based Defect Detection in Robotic Wire Arc Additive Manufacturing

In the last ten years, research interests in various aspects of the Wire Arc Additive Manufacturing (WAAM) processes have grown exponentially. More recently, efforts to integrate an automatic quality assurance system for the WAAM process are increasing. No reliable online monitoring system for the WAAM process is a key gap to be filled for the commercial application of the technology, as it will enable the components produced by the process to be qualified for the relevant standards and hence be fit for use in critical applications in the aerospace or naval sectors. However, most of the existing monitoring methods only detect or solve issues from a specific sensor, no monitoring system integrated with different sensors or data sources is developed in WAAM in the last three years. In addition, complex principles and calculations of conventional algorithms make it hard to be applied in the manufacturing of WAAM as the character of a long manufacturing cycle. Intelligent algorithms provide in-built advantages in processing and analysing data, especially for large datasets generated during the long manufacturing cycles. In this research, in order to establish an intelligent WAAM defect detection system, two intelligent WAAM defect detection modules are developed successfully. The first module takes welding arc current / voltage signals during the deposition process as inputs and uses algorithms such as support vector machine (SVM) and incremental SVM to identify disturbances and continuously learn new defects. The incremental learning module achieved more than a 90% f1-score on new defects. The second module takes CCD images as inputs and uses object detection algorithms to predict the unfused defect during the WAAM manufacturing process with above 72% mAP. This research paves the path for developing an intelligent WAAM online monitoring system in the future. Together with process modelling, simulation and feedback control, it reveals the future opportunity for a digital twin system