Technical Library | 2016-08-17 01:24:36.0
To stake a claim in upcoming new technologies and increasing improved customer experience, it is now becoming a central point of consideration to bring out the new classy vehicle design, car manufacturing techniques, testing system in the global market. The current vehicle manufacturer’s also aim to maintain equilibrium between deep capital investment and long product cycle to make the car model a success story. With this, the type of printed circuit board to be used in the vehicle is decided with focusing more on the type of material used in the vehicle and the level of electronic manufacturing and design solution needed in the vehicle production. To go into the roots of the automotive industry, it is equally important to get insights into the PCB used in vehicles and the new innovations brought forward by researchers to create a dream vehicle of the series. The below paragraph drives you to the types of PCB used in the automotive sector.
Technical Library | 2023-06-12 18:33:29.0
This paper presents a real-time predictive approach to improve solder paste stencil printing cycle decision making process in surface mount assembly lines. Stencil cleaning is a critical process that influences the quality and efficiency of printing circuit board. Stencil cleaning operation depends on various process variables, such as printing speed, printing pressure, and aperture shape. The objective of this research is to help efficiently decide stencil printing cleaning cycle by applying data-driven predictive methods. To predict the printed circuit board quality level, a recurrent neural network (RNN) is applied to obtain the printing performance for the different cleaning aging. In the prediction model, not only the previous printing performance statuses are included, but also the printing settings are used to enhance the RNN learning. The model is tested using data collected from an actual solder paste stencil printing line. Based on the predicted printing performance level, the model can help automatically identify the possible cleaning cycle in practice. The results indicate that the proposed model architecture can predictively provide accurate solder paste printing process information to decision makers and increase the quality of the stencil printing process.
Technical Library | 2021-06-07 19:06:32.0
The technological growth of the last decades has brought many improvements in daily life, but also concerns on how to deal with electronic waste. Electrical and electronic equipment waste is the fastest-growing rate in the industrialized world. One of the elements of electronic equipment is the printed circuit board (PCB) and almost every electronic equipment has a PCB inside it. While waste PCB (WPCB) recycling may result in the recovery of potentially precious materials and the reuse of some components, it is a challenging task because its composition diversity requires a cautious pre-processing stage to achieve optimal recycling outcomes. Our research focused on proposing a method to evaluate the economic feasibility of recycling integrated circuits (ICs) from WPCB. The proposed method can help decide whether to dismantle a separate WPCB before the physical or mechanical recycling process and consists of estimating the IC area from a WPCB, calculating the IC's weight using surface density, and estimating how much metal can be recovered by recycling those ICs. To estimate the IC area in a WPCB, we used a state-of-the-art object detection deep learning model (YOLO) and the PCB DSLR image dataset to detect the WPCB's ICs. Regarding IC detection, the best result was obtained with the partitioned analysis of each image through a sliding window, thus creating new images of smaller dimensions, reaching 86.77% mAP. As a final result, we estimate that the Deep PCB Dataset has a total of 1079.18 g of ICs, from which it would be possible to recover at least 909.94 g of metals and silicon elements from all WPCBs' ICs. Since there is a high variability in the compositions of WPCBs, it is possible to calculate the gross income for each WPCB and use it as a decision criterion for the type of pre-processing.
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