Technical Library | 2024-08-29 18:30:46.0
The mechanical experience of consumption (i.e., feel, softness, and texture) of many foods is intrinsic to their enjoyable consumption, one example being the habit of twisting a sandwich cookie to reveal the cream. Scientifically, sandwich cookies present a paradigmatic model of parallel plate rheometry in which a fluid sample, the cream, is held between two parallel plates, the wafers. When the wafers are counterrotated, the cream deforms, flows, and ultimately fractures, leading to separation of the cookie into two pieces. We introduce Oreology (/Oriːˈɒl@dʒi/), from the Nabisco Oreo for "cookie" and the Greek rheo logia for "flow study," as the study of the flow and fracture of sandwich cookies. Using a laboratory rheometer, we measure failure mechanics of the eponymous Oreo's "creme" and probe the influence of rotation rate, amount of creme, and flavor on the stress–strain curve and postmortem creme distribution. The results typically show adhesive failure, in which nearly all (95%) creme remains on one wafer after failure, and we ascribe this to the production process, as we confirm that the creme-heavy side is uniformly oriented within most of the boxes of Oreos. However, cookies in boxes stored under potentially adverse conditions (higher temperature and humidity) show cohesive failure resulting in the creme dividing between wafer halves after failure. Failure mechanics further classify the creme texture as "mushy." Finally, we introduce and validate the design of an open-source, three-dimensionally printed Oreometer powered by rubber bands and coins for encouraging higher precision home studies to contribute new discoveries to this incipient field of study
Technical Library | 2024-04-22 20:16:01.0
The solid-state electronics industry faces relentless pressure to improve performance, increase functionality, decrease costs, and reduce design and development time. As a result, device feature sizes are now in the nanometer scale range and design life cycles have decreased to fewer than five years. Until recently, semiconductor device lifetimes could be measured in decades, which was essentially infinite with respect to their required service lives. It was, therefore, not critical to quantify the device lifetimes exactly, or even to understand them completely. For avionics, medical, military, and even telecommunications applications, it was reasonable to assume that all devices would have constant and relatively low failure rates throughout the life of the system; this assumption was built into the design, as well as reliability and safety analysis processes.
Technical Library | 2016-04-08 01:19:52.0
PCB assembly designs become more complex year-on-year, yet early-stage form/fit compliance verification of all designed-in components to the intended manufacturing processes remains a challenge. So long as librarians at the design and manufacturing levels continue to maintain their own local standards for component representation, there is no common representation in the design-to-manufacturing phase of the product lifecycle that can provide the basis for transfer of manufacturing process rules to the design level. A comprehensive methodology must be implemented for all component types, not just the minority which happen to conform to formal packaging standards, to successfully left-shift assembly and test DFM analysis to the design level and thus compress NPI cycle times.(...)This paper will demonstrate the technological components of the working solution: the logic for deriving repeatable and standardized package and pin classifications from a common source of component physical-model content, the method for associating DFA and DFT rules to those classifications, and the transfer of those rules to separate DFM and NPI analysis tools elsewhere in the design-through-manufacturing chain resulting in a consistent DFM process across multiple design and manufacturing organizations.
Technical Library | 2023-06-14 01:09:26.0
In the electronic packaging industry, it is important to be able to make accurate predictions of board level solder joint reliability during thermal cycling exposures. The Anand viscoelastic constitutive model is often used to represent the material behavior of the solder in finite element simulations. This model is defined using nine material parameters, and the reliability prediction results are often highly sensitive to the Anand parameters. In this work, an investigation on the Anand constitutive model and its application to SAC solders of various Ag contents (i.e. SACN05, with N = 1, 2, 3, 4) has been performed. For each alloy, both water quenched (WQ) and reflowed (RF) solidification profiles were utilized to establish two unique specimen microstructures, and the same reflow profile was used for all four of the SAC alloys so that the results could be compared and the effects of Ag content could be studied systematically.
Technical Library | 2021-11-22 20:39:44.0
Quality control is a key activity performed by manufacturing companies to verify product conformance to the requirements and specifications. Standardized quality control ensures that all the products are evaluated under the same criteria. The decreased cost of sensors and connectivity enabled an increasing digitalization of manufacturing and provided greater data availability. Such data availability has spurred the development of artificial intelligence models, which allow higher degrees of automation and reduced bias when inspecting the products. Furthermore, the increased speed of inspection reduces overall costs and time required for defect inspection. In this research, we compare five streaming machine learning algorithms applied to visual defect inspection with real world data provided by Philips Consumer Lifestyle BV. Furthermore, we compare them in a streaming active learning context, which reduces the data labeling effort in a real-world context. Our results show that active learning reduces the data labeling effort by almost 15% on average for the worst case, while keeping an acceptable classification performance. The use of machine learning models for automated visual inspection are expected to speed up the quality inspection up to 40%.
Technical Library | 2016-03-24 17:37:09.0
Today's Electronic Industry is changing at a high pace. The root causes are manifold. So world population is growing up to eight billions and gives new challenges in terms of urbanization, mobility and connectivity. Consequently, there will raise up a lot of new business models for the electronic industry. Connectivity will take a large influence on our lives. Concepts like Industry 4.0, internet of things, M2M communication, smart homes or communication in or to cars are growing up. All these applications are based on the same demanding requirement – a high amount of data and increased data transfer rate. These arguments bring up large challenges to the Printed Circuit Board (PCB) design and manufacturing.This paper investigates the impact of different PCB manufacturing technologies and their relation to their high frequency behavior. In the course of the paper a brief overview of PCB manufacturing capabilities is be presented. Moreover, signal losses in terms of frequency, design, manufacturing processes, and substrate materials are investigated. The aim of this paper is, to develop a concept to use materials in combination with optimized PCB manufacturing processes, which allows a significant reduction of losses and increased signal quality.
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