Technical Library | 2021-12-02 01:44:00.0
With the advent of Industry 4.0, production processes have been endowed with intelligent cyber-physical systems generating massive amounts of streaming sensor data. Internet of Things technologies have enabled capturing, managing, and processing production data at a large scale in order to utilize this data as an asset for the optimization of production processes. In this work, we focus on the automatic detection of physical defects in the production of surfacemount devices. We show how to build a classification model based on random forests that efficiently detects defect products with a high degree of precision. In fact, the results of our preliminary experimental analysis indicate that our approach is able to correctly determine defects in a simulated production environment of surface-mount devices with a MCC score of 0.96. We investigate the feasibility of utilizing this approach in realistic settings. We believe that our approach will help to advance the production of surface-mount devices.
Technical Library | 2022-10-11 20:29:31.0
Electronic assemblies deployed in harsh environments may be subjected to multiple thermal environments during the use-life of the equipment. Often the equipment may not have any macro-indicators of damage such as cracks or delamination. Quantiication of thermal environments during use-life is often not feasible because of the data-capture and storage requirements, and the overhead on core-system functionality. There is need for tools and techniques to quantify damage in deployed systems in absence of macro-indicators of damage without knowledge of prior stress history. The presented PHM framework is targeted towards high reliability applications such as avionic and space systems. In this paper, Sn3.0Ag0.5Cu alloy packages have been subjected to multiple thermal cycling environments including -55 to 125C and 0 to 100C. Assemblies investigated include area-array packages soldered on FR4 printed circuit cards. The methodology involves the use of condition monitoring devices, for gathering data on damage pre-cursors at periodic intervals. Damage-state interrogation technique has been developed based on the Levenberg-Marquardt Algorithm in conjunction with the microstructural damage evolution proxies. The presented technique is applicable to electronic assemblies which have been deployed on one thermal environment, then withdrawn from service and targeted for redeployment in a different thermal environment. Test cases have been presented to demonstrate the viability of the technique for assessment of prior damage, operational readiness and residual life for assemblies exposed to multiple thermo-mechanical environments. Prognosticated prior damage and the residual life show good correlation with experimental data, demonstrating the validity of the presented technique for multiple thermo-mechanical environments.
Technical Library | 2020-03-04 23:53:17.0
Critical to maintaining quality control in high-throughput screening is the need for constant monitoring of liquid-dispensing fidelity. Traditional methods involve operator intervention with gravimetric analysis to monitor the gross accuracy of full plate dispenses, visual verification of contents, or dedicated weigh stations on screening platforms that introduce potential bottlenecks and increase the plate-processing cycle time. We present a unique solution using open-source hardware, software, and 3D printing to automate dispenser accuracy determination by providing real-time dispense weight measurements via a network-connected precision balance. This system uses an Arduino microcontroller to connect a precision balance to a local network. By integrating the precision balance as an Internet of Things (IoT) device, it gains the ability to provide real-time gravimetric summaries of dispensing, generate timely alerts when problems are detected, and capture historical dispensing data for future analysis. All collected data can then be accessed via a web interface for reviewing alerts and dispensing information in real time or remotely for timely intervention of dispense errors. The development of this system also leveraged 3D printing to rapidly prototype sensor brackets, mounting solutions, and component enclosures.
Technical Library | 2020-07-22 19:24:33.0
Recent advancements in electronic packaging and image processing techniques have opened the possibility for optics-based portable eye tracking approaches, but technical and safety hurdles limit safe implementation toward wearable applications. Here, we introduce a fully wearable, wireless soft electronic system that offers a portable, highly sensitive tracking of eye movements (vergence) via the combination of skin-conformal sensors and a virtual reality system. Advancement of material processing and printing technologies based on aerosol jet printing enables reliable manufacturing of skin-like sensors, while the flexible hybrid circuit based on elastomer and chip integration allows comfortable integration with a user's head. Analytical and computational study of a data classification algorithm provides a highly accurate tool for real-time detection and classification of ocular motions. In vivo demonstration with 14 human subjects captures the potential of the wearable electronics as a portable therapy system, whose minimized form factor facilitates seamless interplay with traditional wearable hardware.
Technical Library | 2021-06-15 18:40:53.0
The jet printing of a dense mixed non-Newtonian suspension is based on the rapid displacement of fluid through a nozzle, the forming of a droplet and eventually the break-off of the filament. The ability to model this process would facilitate the development of future jetting devices. The purpose of this study is to propose a novel simulation framework and to show that it captures the main effects such as droplet shape, volume and speed. In the framework, the time dependent flow and the fluid-structure interaction between the suspension, the moving piston and the deflection of the jetting head is simulated. The system is modelled as a two phase system with the surrounding air being one phase and the dense suspension the other. Hence, the non-Newtonian suspension is modelled as a mixed single phase with properties determined from material testing. The simulations were performed with two coupled in-house solvers developed at Fraunhofer-Chalmers Centre; IBOFlow, a multiphase flow solver and LaStFEM, a large strain FEM solver. Jetting behaviour was shown to be affected not only by piston motion and fluid rheology, but also by the energy loss in the jetting head. The simulation results were compared to experimental data obtained from an industrial jetting head.
Fraunhofer-Chalmers Research Centre for Industustrial Mathematics
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