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This paper addresses a critical challenge in Printed Circuit Board (PCB) manufacturing by proposing an AI-driven, fully automated drilling machine that employs sophisticated path-planning techniques. Current methodologies often fail to adequately assess designs with varying hole sizes, diverse component placements, and complex geometries, leading to compromised precision and increased manufacturing times. Our innovative approach leverages advanced algorithms to intelligently analyze PCB designs and optimize drilling paths, significantly reducing production time and minimizing errors. By automating the drilling process, we enhance overall productivity while ensuring precise hole placement, essential for maintaining high-quality circuit boards. Utilizing KiCAD EDA software, we automate the generation of Gerber files and G-Codes, demonstrating a remarkable 13-fold improvement in routing efficiency compared to manual methods. Our case studies validate the effectiveness of this system, showcasing successful continuity tests and superior quality finishes in PCB production. This research contributes to the advancement of PCB manufacturing. It lays the groundwork for future explorations into machine learning algorithms and optimization techniques, further enhancing the efficiency and applicability of AI in electronics design.

The development of electric vehicles (EVs) has been incremental because EVs satisfy a significant demand for energy sources. Electronic control unit (ECU) is an important component that processes the electric signals received from various sensors for generating the control signals for the actuators. Automotive control systems were initially operated manually throughout the automotive revolution based on the responses of input signals received from ECUs and drivers. Most of the functions in EV are controlled by the ECU and every ECU consumes power at all times even if it is not in use. The larger power consumption of passive ECUs like adaptive lighting systems (ALS), automatic wiper systems (AWS) brake light systems (BLS), etc., affect the life of ECUs and the range of EVs. This article is primarily concerned with limiting power consumption by switching the power supply to the passive ECUs based on their requirements. Hence, to achieve the objective, the intelligent zone (i-zone) based master ECU is triggered to activate the slave ECUs. Designing suites including Proteus and KiCAD were used for designing the circuits including master as well as slave ECU. This prototype is built using three secondary ECUs such as ALS & AWS and BLS which are controlled using i-zone-based master ECU. The performance of this implemented design is evaluated, and it is discovered that almost 40% of the battery consumption is reduced. This i-zone-based master ECU and all its slave ECUs manage power while ensuring the safety and reliability of EVs.

Traveling wave ion mobility experiments using planar electrode structures (e.g., structures for lossless ion manipulation, TW-SLIM) leverage the mature manufacturing capabilities of printed circuit boards (PCBs). With routine levels of mechanical precision below 150 μm, the conceptual flexibility afforded by PCBs for use as planar ion guides is expansive. To date, the design and construction of TW-SLIM platforms require considerable legacy expertise, especially with respect to simulation and circuit layout strategies. To lower the barrier of TW-SLIM implementation, we introduce Python-based interactive tools that assist in graphical layout of the core electrode footprints for planar ion guides with minimal user inputs. These scripts also export the exact component locations and assignments for direct integration into KiCad and SIMION for PCB finalization and ion flight simulations. The design concepts embodied in the set of scripts comprising SLIM Pickins (PCB CAD generation) and pigsim (SIMION workspace generation) build upon the lessons learned in the independent development of the research-grade TW-SLIM platforms in operation at WSU. Due to the inherent flexibility of the PCB manufacturing process and the time devoted to board layouts prior to manufacturing, both scripts serve to enable rapid, iterative design considerations. Because only a few predefined parameters are necessary (i.e., the TW-SLIM monomer width, x position following a TW Turn, and y position following a TW Turn) it is possible to design the exact component layouts and accompanying simulation space in a manner of minutes. There is no known limitation to the board layout capacities of the scripts, and the size of a designed layout is ultimately constrained by the abilities of the final PCB design and simulation tools, KiCad and SIMION, to accommodate the thousands of electrodes comprising the final design (i.e., RAM and software overhead). Toward removing the barriers to exploring new SLIM tracks and the likelihood of layout errors that require considerable revision and engineering time, the SLIM Pickins and pigsim tools (included as Supporting Information) allow the user to quickly design a length of planar ion guide, simulate its abilities to confine and transmit ions, compare hypothetical board outlines to given vacuum chamber dimensions, and generate a near-production ready PCB CAD file. In addition to these tools, this report outlines a series of cost-saving strategies with respect to vacuum feedthroughs and vacuum chamber design for TW ion mobility experiments using planar ion guides.

Fast and effective algorithms for deep learning on 3D shapes are keys to innovate mechanical and electronic engineering design workflow. In this paper, an efficient 3D shape to 2D images projection algorithm and a shallow 2.5D convolutional neural network architecture is proposed. A smaller convolutional neural network (CNN) model is achieved by information enrichment at the preprocessing stage, i.e. 3D geometry is compressed into 2D "thickness view" and "depth view". Fusing the depth view and thickness view (DTV) from the same projection view into a dual-channel grayscale image, can improve information locality for geometry and topology feature extraction. This approach bridges the gap between mature image deep learning technologies to the applications of 3D shape. Enhanced by several essential scalar geometry properties and only 3 projection views, a mixed CNN and multiple linear parameter (MLP) neural network model achives a validation accuracy of 92 % for ModelNet10 mesh-based dataset, while the training time is one order of magnitude less than the original multi-view CNN approach. This study also creates new 3D shape datasets from 2 open source CAD projects. Higher validation accuracy is obtained for realistic CAD datasets, i.e. 97 % for FreeCAD's mechanical part library and 95 % for KiCAD electronic part library. The training cost reduces to tens of minutes on a laptop CPU, given the smaller input data size and shallow neural network design. It is expected that this approach can be adapted for other machine learning scenarios involved in CAD geometry.

Radiation dosimeters play a crucial role in radiation oncology by accurately measuring radiation dose, ensuring precise and safe radiation therapy. This study presents the design and development of a low-cost printed circuit board (PCB) dosimeter and an integrated electrometer with sensitivity optimized for dose rates intended for use in megavoltage radiation therapy. The PCB dosimeter was designed in KiCad, and it uses a low-cost S5MC-13F general-purpose 1 kV 5A power diode as a radiation detector. The dosimeter is calibrated against a known dose derived from an ionization chamber and tested for dose linearity, dose rate dependence, field size dependence, and detector orientation dependence. The observed average dose differences between the delivered and measured doses for most measurements were found to be < 1.1%; the dose rate linearity between 100 MU/min and 1400 MU/min was found to be within 1.3%. This low-cost architecture could successfully be adapted further for a scalable, cost-effective dosimetry solution through firmware or circuit design.

Rodent behavioral models with an electrophysiological component may require the joint operation of hardware from Med Associates, Inc. (St. Albans, VT) and Tucker-Davis Technologies (TDT; Alachua, FL). Although these manufacturers do produce supplemental hardware for interfacing with each other, investing in such hardware may be untenable for research groups with limited funds who wish to use equipment already in their possession. We designed a printed circuit board (PCB) in KiCad and had it fabricated by Advanced Circuits (Aurora, CO), with components sourced from Digi-Key (Thief River Falls, MN). The PCB provided 8 channels of bidirectional communication for the transmission of signals between Med Associates' SG-716B SmartCtrl connection panel and TDT's RZ5D base station. This setup enabled the coordinated operation of programs running separately on each set of hardware. The custom-built PCB facilitated the joint operation of Med Associates and TDT hardware in a go/no-go detection task involving rats with electrical implants in their sciatic nerves. Conventional methods for interfacing Med Associates and Tucker-Davis Technologies rely on the purchase of pre-built hardware whose costs can add up to thousands of dollars. The present method offers a viable alternative that is easily implemented and considerably less expensive (below $200). The present approach provides an inexpensive yet effective alternative to far more costly interfacing solutions offered by Med Associates and Tucker-Davis Technologies.

The proliferative index in breast carcinoma is usually calculated by the percentage of the Ki-67 positive cells out of the total number of malignant cells. In order to reduce the inter-observer variability of the calculated proliferative index a cocktail of antibodies against E-Cadherin and Ki-67 (Ki/Cad Cocktail) is presented. The cocktail was applied on 59 cases of infiltrating duct carcinoma of breast and compared to the consecutive slides stained for Ki-67 alone. The Ki/Cad cocktail has the advantage that by adding the anti E-Cadherin antibody, all the malignant epithelial cells are highlighted and can be differentiated from other proliferating cells. Statistical analysis proved that the cocktail increases the inter-observer agreement from 89 % to 97 % as compared to the Ki-67 alone and also reduces the overlap between the cancer grades.

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