Technical Library | 2017-08-02 20:18:21.0
In this rapidly moving electronics market, fast to market with new products is what separates top performing companies from average companies. A survey conducted by Arthur D. Little revealed that "New-Product Development (NPD) productivity in atop performing company is five times what it is in the average company. The top performer gets five times as much new product output for the same investment." What do they know that the rest of us do not? One winning factor is the use of the Robert Cooper process. (...)This paper will present a Lean Six Sigma approach to "right sizing" the Stage Gate process to be efficient, practical, and easy to manage. Various tools of Stage Gate, along with proven best practice, will be covered. In addition, a reduced Stage Gate model will be discussed for simple, low risk projects.
Technical Library | 2021-08-04 18:41:30.0
Industry 4.0 is one of the most exciting developments in the manufacturing industry in decades. It promises vast improvements for both manufacturers and their customers. For some companies, however, it can be overwhelming, and it can be difficult with the current available information to understand exactly what the benefits will be in the average factory, and to calculate the return on the investment. Therefore, it may be helpful to bring the discussion down to a tangible level and to isolate one little part of the whole smart electronic assembly factory, namely reflow.
Technical Library | 2017-03-09 17:37:05.0
This article focuses on the fabrication and characterization of stretchable interconnects for wearable electronics applications. Interconnects were screen-printed with a stretchable silver-polymer composite ink on 50-μm thick thermoplastic polyurethane. The initial sheet resistances of the manufactured interconnects were an average of 36.2 mΩ/◽, and half the manufactured samples withstood single strains of up to 74%. The strain proportionality of resistance is discussed, and a regression model is introduced. Cycling strain increased resistance. However, the resistances here were almost fully reversible, and this recovery was time-dependent. Normalized resistances to 10%, 15%, and 20% cyclic strains stabilized at 1.3, 1.4, and 1.7. We also tested the validity of our model for radio-frequency applications through characterization of a stretchable radio-frequency identification tag.
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 | 2023-03-27 19:18:38.0
Electronic waste (e-waste) is currently the fastest growing hazardous waste stream that continues to be a challenging concern for the global environment and public health. The average useful life of electronic products has continued to decline, and obsolete products are being stored or discarded with increasing frequency. E-waste is hazardous, complex and expensive to treat in an environmentally sound manner. As a result, new challenges related to the management of e-waste have become apparent. Most electronic products contain a combination of hazardous materials, toxic materials, and valuable elements such as precious metals and rare earth elements. There are risks to human health associated with the disposal of E-waste in landfills, or treatment by incineration. Americans discard 400+ million electronic items per year recycling less than 20 percent in safe and sustainable manner. E-waste is exported from developed countries and processed informally using unsafe conditions in many regions of developing countries. A mixture of pollutants is released from these informal rudimentary operations. Exposure to e-waste recycling includes the dismantling of used electronics and the use of hydrometallurgical and pyrometallurgical processes, which emit toxic chemicals, to retrieve valuable components. Thermal analysis integrated with chromatographic and spectroscopic techniques are used to determine dangerous chemicals emitted during the burning of e-waste. The information is used to assess the risk of exposure of workers at these semi-formal recycling centers.
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