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Learning factories present a promising environment for education, training and research, especially in manufacturing related areas which are a main driver for wealth creation in any nation. While numerous learning factories have been built in industry and academia in the last decades, a comprehensive scientific overview of the topic is still missing. This paper intends to close this gap establishing the state of the art of learning factories. The motivations, historic background, and the didactic foundations of learning factories are outlined. Definitions of the term learning factory and the corresponding morphological model are provided. An overview of existing learning factory approaches in industry and academia is provided, showing the broad range of different applications and varying contents. The state of the art of learning factories curricula design and their use to enhance learning and research as well as potentials and limitations are presented. Conclusions and an outlook on further research priorities are offered.
Technologies for mapping the “digital twin“ have been under development for approximately 20 years. Nowadays increasingly intelligent, individualized products encourages companies to respond innovatively to customer requirements and to handle the rising product variations quickly.
An integrated engineering network, spanning across the entire value chain, is operated to intelligently connect various company divisions, and to generate a business ecosystem for products, services and communities. The conditions for the digital twin are thereby determined in which the digital world can be fed into the real, and the real world back into the digital to deal such intelligent products with rising variations.
The term digital twin can be described as a digital copy of a real factory, machine, worker etc., that is created and can be independently expanded, automatically updated as well as being globally available in real time. Every real product and production site is permanently accompanied by a digital twin. First prototypes of such digital twins already exist in the ESB Logistics Learning Factory on a cloud- and app based software that builds on a dynamic, multidimensional data and information model. A standardized language of the robot control systems via software agents and positioning systems has to be integrated. The aspect of the continuity of the real factory in the digital factory as an economical means of ensuring continuous actuality of digital models looks as the basis of changeability.
For the indoor localization sensor combinations that in addition to the hardware already contain the software required for the sensor data fusion should be used. Processing systems, scenario-live-simulations and digital shop floor management results in a mandatory procedural combination. Essential to the digital twin is the ability to consistently provide all subsystems with the latest state of all required information, methods and algorithms.
Context: Development of software intensive products and services increasingly occurs by continuously deploying product or service increments, such as new features and enhancements, to customers. Product and service developers must continuously find out what customers want by direct customer feedback and usage behaviour observation. Objective: This paper examines the preconditions for setting up an experimentation system for continuous customer experiments. It describes the RIGHT model for Continuous Experimentation (Rapid Iterative value creation Gained through High-frequency Testing), illustrating the building blocks required for such a system. Method: An initial model for continuous experimentation is analytically derived from prior work. The model is matched against empirical case study findings from two startup companies and further developed. Results: Building blocks for a continuous experimentation system and infrastructure are presented. Conclusions: A suitable experimentation system requires at least the ability to release minimum viable products or features with suitable instrumentation, design and manage experiment plans, link experiment results with a product roadmap, and manage a flexible business strategy. The main challenges are proper, rapid design of experiments, advanced instrumentation of software to collect, analyse, and store relevant data, and the integration of experiment results in both the product development cycle and the software development process.
Comments on “Solubility parameter of chitin and chitosan”, Carbohydrate Polymers 36 (1998) 121–127
(2017)
Results on the solubility parameters of chitin and chitosan presented in the paper DOI: 10.1016/S0144-8617(98)00020-4 were recalculated and data evaluation was redone. A number of misprints, erroneous calculations and data evaluations were found with respect to Hansen as well as total solubility parameters as derived according to group contribution methods by Hoftyzer-Van Krevelen and Hoy’s system. Revised numerical data are presented.
Digitisation forms a part of Industrie 4.0 and is both threatening, but also providing an opportunity to transform business as we know it; and can make entire business models redundant. Although companies might realise the need to digitise, many are unsure of how to start this digital transformation. This paper addresses the problems and challenges faced in digitisation, and develops a model for initialising digital transformation in enterprises. The model is based on a continuous improvement cycle, and also includes triggers for innovative and digital thinking within the enterprise. The model was successfully validated in the German service sector.
Induced by a societal decision to phase out conventional energy production - the so-called Energiewende (energy transition) - the rise of distributed generation acts as a game changer within the German energy market. The share of electricity produced from renewable resources increased to 31,6% in 2015 (UBA, 2016) with a targeted share of renewable resources in the electricity mix of 55%-60% in 2035 (RAP, 2015), opening perspectives for new products and services. Moreover, the rapidly increasing degree of digitization enables innovative and disruptive business models in niches at the grid's edge that might be the winners of the future. It also stimulates the market entry of newcomers and competitors from other sectors, such as IT or telecommunication, challenging the incumbent utilities. For example, virtual and decentral market places for energy are emerging; a trend that is likely to speed up considerably by blockchain technology, if the regulatory environment is adjusted accordingly. Consequently, the energy business is turned upside down, with customers now being at the wheel. For instance, more than one-third of the renewable production capacities are owned by private persons (Trendsearch, 2013). Therefore, the objective of this chapter is to examine private energy consumer and prosumer segments and their needs to derive business models for the various decentralized energy technologies and services. Subsequently, success factors for dealing with the changing market environment and consequences of the potentially disruptive developments for the market structure are evaluated.
Propofol is a commonly used intravenous general anesthetic. Multi-capillary column (MCC) coupled ion-mobility spectrometry (IMS) can be used to quantify exhaled propofol, and thus estimate plasma drug concentration. Here, we present results of the calibration and analytical validation of a MCC/IMS pre-market prototype for propofol quantification in exhaled air.
Close and safe interaction of humans and robots in joint production environments is technically feasible, however should not be implemented as an end in itself but to deliver improvement in any of a production system’s target dimensions. Firstly, this paper shows that an essential challenge for system integrators during the design of HRC applications is to identify a suitable distribution of available tasks between a robotic and a human resource. Secondly, it proposes an approach to determine task allocation by considering the actual capabilities of both human and robot in order to improve work quality. It matches those capabilities with given requirements of a certain task in order to identify the maximum congruence as the basis for the allocation decision. The approach is based on a study and subsequent generic description of human and robotic capabilities as well as a heuristic procedure that facilities the decision making process.
Pokémon Go was the first mobile augmented reality (AR) game to reach the top of the download charts of mobile applications. However, little is known about this new generation of mobile online AR games. Existing theories provide limited applicability for user understanding. Against this background, this research provides a comprehensive framework based on uses and gratification theory, technology risk research, and flow theory. The proposed framework aims to explain the drivers of attitudinal and intentional reactions, such as continuance in gaming or willingness to invest money in in-app purchases. A survey among 642 Pokémon Go players provides insights into the psychological drivers of mobile AR games. The results show that hedonic, emotional, and social benefits and social norms drive consumer reactions while physical risks (but not data privacy risks) hinder consumer reactions. However, the importance of these drivers differs depending on the form of user behavior.
LDMOS transistors in integrated power technologies are often subject to thermo-mechanical stress, which degrades the on-chip metallization and eventually leads to a short. This paper investigates small sense lines embedded in the LDMOS metallization. It will be shown that their resistance depends strongly on the stress cycle number. Thus, they can be used as aging sensors and predict impending failures. Different test structures have been investigated to identify promising layout configurations. Such sensors are key components for resilient systems that adaptively reduce stress to allow aggressive LDMOS scaling without increasing the risk of failure.