Flexible manufacturing control with autonomous product memories

Interacting with Digital Product Memories (DPMs) along the supply chain occurs in a variety of scenarios with different users in different locations with different tasks. This chapter discusses solutions for the modality layer, which establishes the end point of a communication channel between an actor and the DPM, connecting the user to the dialog logic of an application based on DPMs.KeywordsMobile DeviceMultimodal User InterfaceSpeech InputMultimodal InteractionTangible InterfaceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Industry 4.0 (I4.0) refers to the recent changes in manufacturing technologies. In this context, intelligent equipment networks provide a high level of automation and information exchange between the elements in a manufacturing environment. On the other hand, logistics refers to the management of the flow of things (physical or digital) between a point of origin and a point of consumption, among which it includes the flow of information. Given the relevance of the information sharing on I4.0 and logistics, this work has as main goal the development of an architecture that allows the sharing of information related to the product life cycle as it moves throughout the supply chain (SC). The architecture developed is based on the Reference Architecture Model Industry 4.0 (RAMI4.0) and allows sharing the Digital Product Memory (DPM) between the SC members through Web Services. Finally, the definition of an architecture as discussed in this work addresses the consistency and interop-erability on the way that the members of a SC interact with each other.

Maintenance is essential to guarantee the availability of any technical equipment, but is the dominant cost factor during the equipment’s operating phase. In this chapter it is shown how Digital Product Memories (DPMs) can be used to optimize different maintenance tasks. Therefore, the analysis is focused on the requirements of two domains: industrial manufacturing and automobiles.KeywordsSpare PartRegistration ProcessCounterfeit ProductCore BlockAutomotive DomainThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Smart package solutions may support a patient in the intake of medication—for instance, through the package’s form factor or by linking of the package with digital data and services built upon these data. This chapter discusses the application potential of a so-called digital product memory in this domain. The starting point is a visionary scenario in which the interplay of intelligent environments and medicine packages equipped with digital product memories allows for unobtrusive support of a patient in dealing with situations that might affect his or her therapy compliance. In this chapter, components of a technical infrastructure of particular relevance to the envisioned kind of support are described. This discussion is complemented with a review of prototype implementations of interaction mechanisms taken from the scenario.KeywordsTechnical InfrastructureApplication KnowledgeInteraction ConceptDigital MemoryVisualization FrameworkThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In this chapter, the utilization of Digital Product Memories (DPMs) in the retail domain is discussed. We demonstrate a complete retail-related usage scenario from a consumer perspective that begins with the preparation of a shopping list before the physical in-store shopping activity. Throughout the entire shopping process in the retail store, the DPM infrastructure effectively supports the consumer with appropriate product recommendations. It provides explicit and implicit access to the consumer’s user profile with different interaction modalities, e.g., via a display on the shopping cart or the consumer’s cell phone, and supports the payment and checkout process based on digital user representations stored on the consumer’s physical items such as his car key. In order to fully close the circle, we then conclude the discussion of DPM-based consumer support by analyzing the way home by car, as a DPM infrastructure in a car provides ample opportunities for driver assistance, including but not limited to monitoring the temperature in the car or finding an optimal route home that avoids roadworks in order to address the extra need for a shock-free ride for bought items such as raw eggs or bottles of champagne.

Collaborations among businesses can be described from different viewpoints. Two of these viewpoints are the value viewpoint, representing estimated values exchanged in a collaboration, and the coordination viewpoint, representing messages exchanged between the actors to coordinate the execution of a collaboration. To observe and maintain the value viewpoint during the complete life cycle, the estimated values have to be validated during the execution of the collaboration. However, since the value model is not implemented, the necessary information for monitoring the value viewpoint needs to be derived from the coordination viewpoint. Relating coordination and value viewpoint is a difficult process because the coordination viewpoint lacks information present in the value viewpoint. In this paper we define the relation between both viewpoints for the complete collaboration life cycle. Furthermore, we provide a mechanism to monitor the collaboration from a value viewpoint.

Recent studies unravelled the effect of climate changes on populations through their impact on functional traits and demographic rates in terrestrial and freshwater ecosystems, but such understanding in marine ecosystems remains incomplete. Here, we evaluate the impact of the combined effects of climate and functional traits on population dynamics of a long-lived migratory seabird breeding in the southern ocean: the black-browed albatross (Thalassarche melanophris, BBA). We address the following prospective question: "Of all the changes in the climate and functional traits, which would produce the biggest impact on the BBA population growth rate?" We develop a structured matrix population model that includes the effect of climate and functional traits on the complete BBA life cycle. A detailed sensitivity analysis is conducted to understand the main pathway by which climate and functional trait changes affect the population growth rate. The population growth rate of BBA is driven by the combined effects of climate over various seasons and multiple functional traits with carry-over effects across seasons on demographic processes. Changes in sea surface temperature (SST) during late winter cause the biggest changes in the population growth rate, through their effect on juvenile survival. Adults appeared to respond to changes in winter climate conditions by adapting their migratory schedule rather than by modifying their at-sea foraging activity. However, the sensitivity of the population growth rate to SST affecting BBA migratory schedule is small. BBA foraging activity during the pre-breeding period has the biggest impact on population growth rate among functional traits. Finally, changes in SST during the breeding season have little effect on the population growth rate. These results highlight the importance of early life histories and carry-over effects of climate and functional traits on demographic rates across multiple seasons in population response to climate change. Robust conclusions about the roles of various phases of the life cycle and functional traits in population response to climate change rely on an understanding of the relationships of traits to demographic rates across the complete life cycle.

The fully π-conjugated donor-acceptor hybrid polymers Fl-TPA, Fl-TPA-TCNE, and Fl-TPA-TCNQ, which are composed of fluorene (Fl), triphenylamine (TPA), dimethylphenylamine, alkyne, alkyne-tetracyanoethylene (TCNE) adduct, and alkyne-7,7,8,8-tetracyanoquinodimethane (TCNQ) adduct, were synthesized. These polymers are completely amorphous in the solid film state and thermally stable up to 291-409 °C. Their molecular orbital levels and band gaps vary with their compositions. The TCNE and TCNQ units, despite their electron-acceptor characteristics, were found to enhance the π-conjugation lengths of Fl-TPA-TCNE and Fl-TPA-TCNQ (i.e., to produce red shifts in their absorption spectra and significant reductions in their band gaps). These changes are reflected in the electrical digital memory behavior of the polymers. Moreover, the TCNE and TCNQ units were found to diversify the digital memory modes and to widen the active polymer layer thickness window. In devices with aluminum top and bottom electrodes, the Fl-TPA polymer exhibits stable unipolar permanent memory behavior with high reliability. The Fl-TPA-TCNE and Fl-TPA-TCNQ devices exhibit stable unipolar permanent memory behavior as well as dynamic random access memory behavior with excellent reliability. These polymer devices were found to operate by either hole injection or hole injection along with electron injection, depending on the polymer composition. Overall, this study demonstrated that the incorporation of π-conjugated cyano moieties, which control both the π-conjugation length and electron-accepting power, is a sound approach for the design and synthesis of high-performance digital memory polymers. The TCNE and TCNQ polymers synthesized in this study are highly suitable active materials for the low-cost mass production of high-performance, polarity-free, programmable, volatile, and permanent memory devices that can be operated with very low power consumption, high ON/OFF current ratios, and high reliability.

While Industry 4.0 targets are theoretically set from various aspects, the approaches for covering some of its key areas are heterogeneous. The main pillars for Productive4.0 – a European research initiative with the industrial drive – are Digital Production, Product Lifecycle Management and Supply Chain Management. These are also key areas for Industry 4.0, and so far handled with heterogeneous approaches, toolsets – and success, indeed.This paper has two aims. First, to provide an overview of the state-of-the-art concepts regarding these three areas, filtered through the actual industrial requirements. Second, to survey the Arrowhead Framework concerning these areas, and suggest a homogeneous approach that can support not only the main vision of these three domains but the actual design-time and operational-time tasks needed to make the vision of Industry 4.0 reality.

Augmented Reality Technology is one of the key technologies of digital transformation in industrial and non-industrial areas. Due to the rapid development of display hardware and tracking systems, virtual and augmented reality applications are being developed today that would only have been possible in large research laboratories with huge efforts a few years ago. This paper presents the application of the augmented reality technology (AR) in the industrial field. Based on industrial case studies the implementation of two AR applications and their potential to digitize the product lifecycle processes will be discussed. The use cases were developed within cooperation projects between the University of Applied Sciences Karlsruhe and a fire truck manufacturer. The case studies focus on the following topics: digital product configuration management and production planning and assistance. The objective of the case studies is the investigation of the AR application in the industrial environment and its capability as digital transformation technology along the product lifecycle. The presented case studies deliver experiences and suggestions in terms of praxis-oriented development and deployment of the AR technology in the engineering field, which can be used for future AR research works.

Product sustainability is a pressing global issue that requires urgent improvement, and low-carbon design is a crucial approach toward achieving sustainable product development. Digital twin technology, which connects the physical and virtual worlds, has emerged as an effective tool for supporting product design and development. However, obtaining accurate product parameters remains a challenge, and traditional low-carbon product design primarily focuses on design parameters. To address these issues, this paper proposes a method for data collection throughout the product lifecycle, leveraging the Internet of Things. The paper envisions the automatic collection of product lifecycle data to enhance the accuracy of product design. Moreover, traditional low-carbon design often has a limited scope that primarily considers product structure and lifecycle stage for optimization. In contrast, combining digital twin technology with low-carbon design can effectively improve product sustainability. Therefore, this paper proposes a three-layer architecture model of product sustainability digital twin, comprising data layer, mapping layer, and application layer. This model sets the carbon footprint as the iterative optimization goal and facilitates the closed-loop sustainable design of the product. The paper envisions sustainable product design based on digital twins that can address cascading problems and achieve closed-loop sustainable design.

For last three decades, semiconductor memory business has greatly grown due to the tremendous progress of electronic data processing (EDP) mainly led by outstanding evolution of PC technology. Recently, various mobile appliances such as hand-held phone, DCS, and MP3 drive new growth of semiconductor memory, which results in unprecedented demand of non-volatile memories, especially mass storage NAND flash. The mobile appliances which aim for smaller and lighter products while improving the performance under ever-increased demand of longer battery lifetime impose different requirements on the semiconductor memories. The newly defined requirements along with short life cycle and customer orientation of mobile products transform the memory semiconductor from standard data storage memory to diverse solution memory. It is predicted that this trend is magnified in the future memory market. In this paper, discussion about the technology trend of memory semiconductor in the mobile era and also the memory technology required satisfying the mobile appliances

Products that exist in digital form can be bought, sold, and, in some cases, delivered, online. The pricing issues that arise in the sale of these products are different from those that sellers face when pricing physical goods and can lead to interesting opportunities for devising revenue models. The success of revenue models for companies that sell digital products depend on the nature of the product, the characteristics of the buyers, and the traditional practices in the industry. This chapter examines the nature of digital products, their pricing issues, and the efficacy of various revenue models that have been implemented by companies that deal in digital products.

The introduction of elements of the concept of digital production is especially important in enterprises manufacturing electronic products that are in demand in all spheres of human activity. To create new objects representing the digital production concept, it is necessary to introduce technological innovations in the production of electronics. This is achieved by solving actual analyzing problems system properties means of production and ensuring product quality. Therefore, the article purpose is to ensure the quality of electronic products based on models and methods for analyzing the means and processes of electronic production. To achieve the goal, the digital production development in a structural framework, functional and informational description are considered. The results of the simulation stages of the production life cycle allowed us to estimate the achieved product quality level while improving the subsystems of automatic installation of printed circuit boards.

This paper investigates possible implementations of the autonomous agents concept in flexible manufacturing control. The implementation issues and the effectiveness of different control architectures and algorithms are analyzed by means of a simulation model of a flexible job shop. Extensive experimental results are reported, allowing the evaluation of the trade-off between the degree of autonomy and system performance.