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The Internet-of-Things: Reflections on the past, present and future from a user-centered and smart environment perspective

This paper introduces the Internet-of-Things (IoT) and describes its evolution from a concept proposed by Kevin Ashton in 1999 through its public emergence in 2005 in a United Nations ITU report entitled “The Internet of Things”, to the present day where IoT devices are available as off-the-shelf products from major manufacturers. Using a systematic study of public literature, the paper presents a five-phase categorisation of the development of the Internet-of-Things from its beginnings to the present day. Four mini case studies are included to illustrate some of the issues involved. Finally, the paper discusses some of the big issues facing future developers and marketers of Internet-of-Things based products ranging from artificial intelligence (AI) through to customer privacy and acceptance finishing with an optimistic assessment of the future of the Internet-of-Things.

From Square Meters to Pixels. Semiconductors and the future of B-to-B events

Agile Multi-Parallel Micro Manufacturing Using a Grid of Equiplets

Abstract: Unlike manufacturing technology for semiconductors and printed circuit boards, the market for traditional micro assembly lacks a clear public roadmap. More agile manufacturing strategies are needed in an environment in which dealing with change becomes a rule instead of an exception. In this paper, an attempt is made to bring production with universal micro assembly cells to the next level. This is realised by placing a larger number of cells, called Equiplets, in a “Grid”. Equiplets are compact and low-cost manufacturing platforms that can be reconfigured to a broad number of applications. Benchmarking Equiplet production has shown reduced time to market and a smooth transition from R&D to Manufacturing. When higher production volumes are needed, more systems can be placed in parallel to meet the manufacturing demand. Costs of product design changes in the later stage of industrialisation have been reduced due to the modular production in grids, which allows the final design freeze to be postponed as late as possible. The need for invested capital is also pushed backwards accordingly. doi 10.1007/978-3-642-11598-1_32

Ethical considerations of Augmented Reality in High-Tech manufacturing

The use of Augmented Reality (AR) in industry is growing rapidly, driven by benefits such as efficiency gains and ability to overcome physical boundaries. Existing studies stress the need to take stakeholder values into account in the design process. In this study the impact of AR on stakeholders' values is investigated by conducting focus groups and interviews, using value sensitive design as a framework. Significant impacts were found on the values of safety, accuracy, privacy, helpfulness and autonomy. Twenty practical design choices to mitigate potential negative impact emerged from the study.

An ultra-compact particle size analyser using a CMOS image sensor and machine learning.

Light scattering is a fundamental property that can be exploited to create essential devices such as particle analysers. The most common particle size analyser relies on measuring the angle-dependent diffracted light from a sample illuminated by a laser beam. Compared to other non-light-based counterparts, such a laser diffraction scheme offers precision, but it does so at the expense of size, complexity and cost. In this paper, we introduce the concept of a new particle size analyser in a collimated beam configuration using a consumer electronic camera and machine learning. The key novelty is a small form factor angular spatial filter that allows for the collection of light scattered by the particles up to predefined discrete angles. The filter is combined with a light-emitting diode and a complementary metal-oxide-semiconductor image sensor array to acquire angularly resolved scattering images. From these images, a machine learning model predicts the volume median diameter of the particles. To validate the proposed device, glass beads with diameters ranging from 13 to 125 µm were measured in suspension at several concentrations. We were able to correct for multiple scattering effects and predict the particle size with mean absolute percentage errors of 5.09% and 2.5% for the cases without and with concentration as an input parameter, respectively. When only spherical particles were analysed, the former error was significantly reduced (0.72%). Given that it is compact (on the order of ten cm) and built with low-cost consumer electronics, the newly designed particle size analyser has significant potential for use outside a standard laboratory, for example, in online and in-line industrial process monitoring.

Demonstration of the Feasibility of Real Time Application of Machine Learning to Production Scheduling

Industry 4.0 has placed an emphasis on real-time decision making in the execution of systems, such as semiconductor manufacturing. This article will evaluate a scheduling methodology called Evolutionary Learning Based Simulation Optimization (ELBSO) using data generated by a Manufacturing Execution System (MES) for scheduling a Stochastic Job Shop Scheduling Problem (SJSSP). ELBSO is embedded within Ordinal Optimization (OO), where in the first phase it uses a meta model, which previously was trained by a Discrete Event Simulation model of a SJSSP. The meta model used within ELBSO uses Genetic Programming (GP)-based Machine Learning (ML). Therefore, instead of using the DES model to train and test the meta model, this article uses historical data from a front-end fab to train and test. The results were statistically evaluated for the quality of the fit generated by the meta-model.