The aim of this research is to establish clear concepts for working with standards (e.g. ISO) in the context of connected factory. When machines or other output devices need to communicate with each other, e.g., to autonomously perform processes in manufacturing processes, all the necessary information must be accessible to them. Today, however, the required information is documented in standards that are only available in PDF or even paper form. Thus, a prerequisite for using information from standards is that these are automatically provided in output devices. A deeper look leads to three levels of maturity for using standard information in output devices: machine-readable, machine-actionable and machine-interpretable. Starting from a clear definition of these terms an approach is developed to implement machine-actionable standards. It is assumed that not all standards are suitable for machine-actionability and therefore the first step is to classify the standards in order to identify the appropriate standards. The second step describes how information from standards can be modelled to be available in a machine-actionable form. The last step clarifies how the machine-actionable content must be provided afterwards so that they can be used in all output devices with less effort. This research work closes with the validation of the developed approach.
To utilize the residual value, which remains an idle asset and disposed product based on the circular economy package, the definition or clarification of such a value and the development of a scheme for such a reutilization is essential. In addition, to facilitate an efficient utilization of the residual value, a product or its components needs to be designed considering its future reuse, remanufacturing, and upgrading. When companies monitor product usage using the Internet of things, they can make proposals to users of appropriate lifecycle options such as reuse, remanufacturing, and upgrading and such proposals are conductive to customer retention for these companies. Therefore, an ideal situation is one in which the product is appropriately modularly designed and its components will be reused in an upward product or another product family or be recycled at the end of life. A designer needs to consider resource efficiency not only in the reuse stage but also in the production and procurement stages. In addition, a company that designs and manufactures products needs to strive for the simultaneous attainment of cooperate social responsibility, higher profits, and higher user satisfaction. Therefore, proper supplier selection is necessary because products currently consist of many components and modules manufactured by various companies. This study proposes a modular design and a strategic evaluation method based on the viewpoint of supply chain management considering sustainability and supplier selection simultaneously. In particular, the proposed method evaluates the designed modular strategy from the perspectives of cost, environmental load in production and transportation, quality, and procurement lead time. As an evaluation, the proposed indicator evaluates the efficiencies of the candidate suppliers. This study applied the proposed method to the design problem of a laptop module and determined the more appropriate suppliers with respect to each destination.
In the Internet of Things (IoT) era, manufacturers collect and share data to provide products that are more functional and less expensive. This embedded manufacturers to construct the global supply chain comprising suppliers, factories and recyclers to assemble products at a lower cost. On the other hand, global warming has become a serious environmental issue, and CO2 emissions on the global supply chain should be visualized and reduced by life cycle assessment. However, CO2 emissions vary for each country because of disparities in the energy mix. Therefore, manufacturers need to select appropriate suppliers for specific components, especially to ensure a lower procurement cost of parts and material-based GHG (GreenHouse Gas) emissions. Additionally, the economic model in the world shifts to circular economy which includes recycling the products economically because of the regenerative use for materials. If the parts inside the end-of-life (EOL) products are recycled, CO2 emissions in the procurement stage can be recovered with recycling cost. Therefore, recyclers need a disassembly part selection that selects recycling or disposal for each part in order to recover CO2 emission and reduce recycling cost in the EOL stage. Thus, certain product data, such as GreenHouse Gas (GHG) emissions and costs, needs to be shared with not only suppliers/factories but also recyclers by IoT technology on the global supply chain for connecting supplier and disassembly part selections. This study proposes a decision support model for economical carbon recovery by connecting supplier and disassembly part selections on procurement and EOL stages. First, a bill of materials (BOM) is prepared using an Asian supplier selection with the 3D-CAD model and Life Cycle Inventory (LCI) database. Second, disassembled parts of the EOL assembly products from the BOM data are selected for either recycling or disposal using 0-1 integer programming with ε constraint method. Finally, the results of the disassembly part selection, in terms of CO2 emission reduction and costs are discussed.
In human face-to-face conversation, embodied rhythms between speech and body motions such as nodding are mutually synchronized not only between talkers but also in a talker. This synchrony called entrainment in communication generates the sharing of embodiment in human interaction, which plays an important role in human interaction and communication. Focusing on the embodied entrainment, a human-entrained embodied interaction and communication technology has been developed by applying the entrainment mechanism of embodied rhythms of nodding and body movements to physical robots and CG characters in verbal communication. In particular, the technology for automatically generating communicative motions and actions from voice is put to practical use in communication robots and toys, media contents, e-learning and game software for a wide range of applications such as education, nursing and entertainment. The sense of unity and sharing of this technology supports the happy feelings and security, and it is the key to human interface that humans connect. From the viewpoint of human interface in the advanced media society and super aged society, the human-entrained embodied interaction and communication technology and the design theory of involvement are introduced as the basis of human connected Internet of Things (IoT) smart technology.
In times of smart products and systems, the Internet of Things (IoT) plays an increasingly important role. IoT combines the digital world (internet) with the physical world (sensors, actuators, robots, smartphones, connected cars etc.). The autonomous operation and remote control of smart systems (e.g. smart home, production hall or line) requires efficient and specially reliable actuators and control mechanisms. Shape memory actuators are particularly suitable for this application due to their properties as they are lightweight, small, energy-efficient and can also be used as sensors at the same time. Many shape memory actuators have been developed for various applications over the past years. Despite great interest, there are no standardized test programs available. The complexity of the shape memory technology is a major challenge in testing fatigue and degradation behavior of components to determine reliability. This article presents fatigue test results of a laboratory test rig of a case study for a shape memory actuator, including all boundary conditions and test requirements. The measurement data consists of different parameters e.g. the stroke of the actuator, the electrical voltage and current (to activate the actuator) as well as the ambient temperature. Since the study comprises only a few prototypes, parametric methods are not suitable for a comprehensive evaluation, therefore parameter-free methods are used as well. The analysis regarding the description of dependencies between the recorded signals and the detection of degradation of the shape memory actuators is discussed in detail. The main objective is the development of a prognosis algorithm in order to be able to predict the failure behaviour of the actuators at an early stage. The methodical approach includes various methods and procedures, which are applied in a logical order. The statistical analytics used in this study are focusing on nonparametric significance tests, such as the Levene’s test and the u-test by Wilcoxon and Mann-Whitney. Further methods are the correlation analysis and the regression analysis as well as multivariate 3D-plots. The fundamentals of shape memory alloys, as well as the used statistical nonparametric methods, are described briefly. Finally, the realization (application of the analysis methods) based on the real test rig data of a case study consisting of 18 actuators is shown and discussed in detail.
Today, Industry 4.0 concerns a rapid advancement in manufacturing technologies which help industries increase their productivity. To adopt Industry 4.0 concept is still visionary by certain lean manufacturers when the communication technologies interfaces are not fully equipped at the production system. Most of the facilities towards digitalization are also expensive and require many specialists in different fields to manage the technologies. Therefore, most data analytics (DA) engineering is cannot be employed broadly for process enhancement by Industry 4.0 environment. However, starting with Internet of Things (IOT) concepts, Andon system with simulation was enhanced to support decision making in lean manufacturing. The aims of this research paper is to develop a decision support system (DSS) framework which intersects between Andon and simulation through IOT concept. A better decision-making information flow are demonstrated in detail. To illustrate the applicability of the DSS, it has been implemented in lean manufacturing for automotive part assembly. The results indicate that the DSS can easily be adopted in digital factories to support in planned and operational activities.