scholarly journals Automating CAD for creating assembly structure from Bill of Materials

2021 ◽  
Vol 1034 (1) ◽  
pp. 012092
Author(s):  
Nanang Ali Sutisna ◽  
Nasir Widha Setyanto
2014 ◽  
Vol 602-605 ◽  
pp. 45-48
Author(s):  
Jun Cai Zhang ◽  
Qing Guo Chen

In UG(Unigraphics) environment, computer aided design on Bill of Materials (BOM) for chemical towel is developed using the development tools of VB. By depth traverse to the assembly structure tree,part properties are extracted from the parts information library in the tower assembly. These information are saved to the BOM management library, to generate the Engineering BOM(EBOM) view. The application module is the base for the further process planning and manufacturing.


Author(s):  
Mukhil Azhagan M. S ◽  
Dhwani Mehta ◽  
Hangwei Lu ◽  
Sudarshan Agrawal ◽  
Mark Tehranipoor ◽  
...  

Abstract Globalization and complexity of the PCB supply chain has made hardware assurance a challenging task. An automated system to extract the Bill of Materials (BoM) can save time and resources during the authentication process, however, there are numerous imaging modalities and image analysis techniques that can be used to create such a system. In this paper we review different imaging modalities and their pros and cons for automatic PCB inspection. In addition, image analysis techniques commonly used for such images are reviewed in a systematic way to provide a direction for future research in this area. Index Terms—Component Detection, PCB, Authentication, Image Analysis, Machine Learning


2021 ◽  
Vol 628 ◽  
pp. 119230
Author(s):  
Shuhao Wang ◽  
Shaosuo Bing ◽  
Yunhao Li ◽  
Yong Zhou ◽  
Lin Zhang ◽  
...  

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Seth Carmody ◽  
Andrea Coravos ◽  
Ginny Fahs ◽  
Audra Hatch ◽  
Janine Medina ◽  
...  

AbstractAn exploited vulnerability in a single software component of healthcare technology can affect patient care. The risk of including third-party software components in healthcare technologies can be managed, in part, by leveraging a software bill of materials (SBOM). Analogous to an ingredients list on food packaging, an SBOM is a list of all included software components. SBOMs provide a transparency mechanism for securing software product supply chains by enabling faster identification and remediation of vulnerabilities, towards the goal of reducing the feasibility of attacks. SBOMs have the potential to benefit all supply chain stakeholders of medical technologies without significantly increasing software production costs. Increasing transparency unlocks and enables trustworthy, resilient, and safer healthcare technologies for all.


Author(s):  
John C. Steuben ◽  
Athanasios P. Iliopoulos ◽  
John G. Michopoulos

A wide variety of scientific and engineering activities require the use of testing machines in order to acquire data regarding the response of materials subjected to mechanical loads. This is particularly applicable to the domain of Additive Manufacturing (AM), where mechanical qualification is essential. Such machinery should be capable of applying loads at required levels and exhibit high mechanical stiffness. Accurate force, displacement, and strain measurements are also required. As a consequence, such testing machines are typically very costly. In the present paper we introduce the Open Uniaxial Test Machine (OpenUTM) project, aimed at providing a low-cost (less than $2500.00) material testing hardware/software framework. This paper will focus on the engineering design and hardware aspects of the OpenUTM project, with particular attention paid to the use of an electrohydraulic actuator (EHA) to provide test loads. A full bill of materials and drawings package is provided, in order to enable the use of the OpenUTM framework by research groups with minimal machine tooling. We introduce several case studies demonstrating the successful use of the OpenUTM frame in AM research efforts, including the testing and characterization of AM polymers and ceramics. We conclude with discussion of the software aspects of the OpenUTM framework, which will be elaborated upon in a follow-up paper (part two). We also present a series of potential avenues towards the improvement of the OpenUTM frame in future hardware iterations.


2020 ◽  
Vol 2 (2) ◽  
pp. 280-293
Author(s):  
Mathew G. Pelletier ◽  
Greg A. Holt ◽  
John D. Wanjura

The removal of plastic contamination in cotton lint is an issue of top priority to the U.S. cotton industry. One of the main sources of plastic contamination showing up in marketable cotton bales, at the U.S. Department of Agriculture’s classing office, is plastic from the module wrap used to wrap cotton modules produced by the new John Deere round module harvesters. Despite diligent efforts by cotton ginning personnel to remove all plastic encountered during unwrapping of the seed cotton modules, plastic still finds a way into the cotton gin’s processing system. To help mitigate plastic contamination at the gin; an inspection system was developed that utilized low-cost color cameras to see plastic on the module feeder’s dispersing cylinders, that are normally hidden from view by the incoming feed of cotton modules. This technical note presents the design of an automated intelligent machine-vision guided cotton module-feeder inspection system. The system includes a machine-learning program that automatically detects plastic contamination in order to alert the cotton gin personnel as to the presence of plastic contamination on the module feeder’s dispersing cylinders. The system was tested throughout the entire 2019 cotton ginning season at two commercial cotton gins and at one gin in the 2018 ginning season. This note describes the over-all system and mechanical design and provides an over-view and coverage of key relevant issues. Included as an attachment to this technical note are all the mechanical engineering design files as well as the bill-of-materials part source list. A discussion of the observational impact the system had on reduction of plastic contamination is also addressed.


ACS Nano ◽  
2007 ◽  
Vol 1 (5) ◽  
pp. 476-486 ◽  
Author(s):  
Ling Zhang ◽  
Wanhua Zhao ◽  
Jai S. Rudra ◽  
Donald T. Haynie

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