Technical Library | 2022-10-31 09:01:25.0
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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 | 2008-05-28 18:41:53.0
This paper describes correlation between a true 2D area measurement (e.g. printer) and a height map generated area from a SPI system. In addition, this paper will explore the correlation between area/volume measurements and bridge detection between 2D/3D techniques. The ultimate goal is to arm the process engineers with information that can be used to make decision that will impact defects, cost, throughput and Return On Investment.
Technical Library | 2015-06-30 22:02:41.0
This paper describes the losses from defects at the placement process in the SMT line. Two case studies of European and Taiwanese SMT manufacturers illustrate the actual losses from their defects. An evaluation method to select a pre-reflow AOI system maximizing the return on investment (ROI) is introduced. In the end, ROIs of three commercial pre-reflow AOI systems are compared to demonstrate the importance of selecting an appropriate AOI system. This paper will increase the probability that anyone installing an AOI system during the pre-reflow process will obtain a successful gain with short payback period.
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