Technical Library: assembleon connect network (Page 1 of 1)

Rework of New High Speed Press Fit Connectors

Technical Library | 2019-06-06 00:19:02.0

More and more people and things are using electronic devices to communicate. Subsequently, many electronic products, in particular mobile base stations and core network nodes, need to handle enormous amounts of data per second. One important link in this communication chain is high speed pressfit connectors that are often used to connect mother boards and back planes in core network nodes. These new high speed pressfit connectors have several hundreds of thin, short and weak pins that are prone to damage. Small variations in via hole dimensions or hole plating thickness affect the connections; if the holes are too small, the pins may be bentor permanently deformed and if the holes are too large they will not form gas tight connections.The goal of this project was to understand how rework of these new high speed pressfit connectors affects connection strengths, hole wall deformations and plating cracks.

HDP User Group

Design and Integration of aWireless Stretchable Multimodal Sensor Network in a Composite Wing

Technical Library | 2020-10-08 00:55:22.0

This article presents the development of a stretchable sensor network with high signal-to-noise ratio and measurement accuracy for real-time distributed sensing and remote monitoring. The described sensor network was designed as an island-and-serpentine type network comprising a grid of sensor "islands" connected by interconnecting "serpentines." A novel high-yield manufacturing process was developed to fabricate networks on recyclable 4-inch wafers at a low cost. The resulting stretched sensor network has 17 distributed and functionalized sensing nodes with low tolerance and high resolution. The sensor network includes Piezoelectric (PZT), Strain Gauge(SG), and Resistive Temperature Detector (RTD) sensors. The design and development of a flexible frame with signal conditioning, data acquisition, and wireless data transmission electronics for the stretchable sensor network are also presented. The primary purpose of the frame subsystem is to convert sensor signals into meaningful data, which are displayed in real-time for an end-user to view and analyze. The challenges and demonstrated successes in developing this new system are demonstrated, including (a) developing separate signal conditioning circuitry and components for all three sensor types (b) enabling simultaneous sampling for PZT sensors for impact detection and (c)configuration of firmware/software for correct system operation. The network was expanded with an in-house developed automated stretch machine to expand it to cover the desired area. The released and stretched network was laminated into an aerospace composite wing with edge-mount electronics for signal conditioning, processing, power, and wireless communication.

Stanford University

Orbotech Trion 2340 Manual

Technical Library | 2020-10-30 10:27:39.0

Preview of the Complete Aneryn's Orbotech Trion 2340 Manual. It explains how to programme the AOI machine, how to train new components (shapes), how to connect it to your network, among other processes. Following the Guide, you will minimize false alarms and escapes rates easily. Full Manual available at https://aneryn.com/creation/trion-2340-manual/ Digital development by Sinfonía Digital

Aneryn

IoT for Real-Time Measurement of High-Throughput Liquid Dispensing in Laboratory 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.

SLAS Technology

All-in-One, Wireless, Stretchable Hybrid Electronics for Smart, Connected, and Ambulatory Physiological Monitoring

Technical Library | 2020-08-19 19:13:00.0

Commercially available health monitors rely on rigid electronic housing coupled with aggressive adhesives and conductive gels, causing discomfort and inducing skin damage. Also, research-level skin-wearable devices, while excelling in some aspects, fall short as concept-only presentations due to the fundamental challenges of active wireless communication and integration as a single device platform. Here, an all-in-one, wireless, stretchable hybrid electronics with key capabilities for real-time physiological monitoring, automatic detection of signal abnormality via deep-learning, and a long-range wireless connectivity (up to 15 m) is introduced. The strategic integration of thin-film electronic layers with hyperelastic elastomers allows the overall device to adhere and deform naturally with the human body while maintaining the functionalities of the on-board electronics. The stretchable electrodes with optimized structures for intimate skin contact are capable of generating clinical-grade electrocardiograms and accurate analysis of heart and respiratory rates while the motion sensor assesses physical activities. Implementation of convolutional neural networks for real-time physiological classifications demonstrates the feasibility of multifaceted analysis with a high clinical relevance. Finally, in vivo demonstrations with animals and human subjects in various scenarios reveal the versatility of the device as both a health monitor and a viable research tool.

Georgia Institute of Technology

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