Technical Library | 2008-10-29 18:45:53.0
Growing demand for compact, multi-function electronics products has accelerated component miniaturization and high-density placement, creating new challenges for the electronics manufacturing industry. It is no longer adequate to simply place parts accurately per a pre-defined CAD assembly program because solder paste alignment errors are increasing for numerous reasons. The solution to this problem is a system in which the placement machine can automatically detect and compensate for misalignment of the solder paste to produce high-quality boards regardless of the process errors beforehand.
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 | 2023-11-20 18:10:20.0
The electronics production is prone to a multitude of possible failures along the production process. Therefore, the manufacturing process of surface-mounted electronics devices (SMD) includes visual quality inspection processes for defect detection. The detection of certain error patterns like solder voids and head in pillow defects require radioscopic inspection. These high-end inspection machines, like the X-ray inspection, rely on static checking routines, programmed manually by the expert user of the machine, to verify the quality. The utilization of the implicit knowledge of domain expert(s), based on soldering guidelines, allows the evaluation of the quality. The distinctive dependence on the individual qualification significantly influences false call rates of the inbuilt computer vision routines. In this contribution, we present a novel framework for the automatic solder joint classification based on Convolutional Neural Networks (CNN), flexibly reclassifying insufficient X-ray inspection results. We utilize existing deep learning network architectures for a region of interest detection on 2D grayscale images. The comparison with product-related meta-data ensures the presence of relevant areas and results in a subsequent classification based on a CNN. Subsequent data augmentation ensures sufficient input features. The results indicate a significant reduction of the false call rate compared to commercial X-ray machines, combined with reduced product-related optimization iterations.
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