Knowledge-Based Virtual Modeling and Simulation of Manufacturing Processes for Industry 4.0

Abstract

Industry 4.0 aims at providing a digital representation of a production landscape, but the challenges in building, maintaining, optimizing, and evolving digital models in inter-organizational production chains have not been identified yet in a systematic manner. In this paper, various Industry 4.0 research and technical challenges are addressed, and their present scenario is discussed. Moreover, in this article, the novel concept of developing experience-based virtual models of engineering entities, process, and the factory is presented. These models of production units, processes, and procedures are accomplished by virtual engineering object (VEO), virtual engineering process (VEP), and virtual engineering factory (VEF), using the knowledge representation technique of Decisional DNA. This blend of the virtual and physical domains permits monitoring of systems and analysis of data to foresee problems before they occur, develop new opportunities, prevent downtime, and even plan for the future by using simulations. Furthermore, the proposed virtual model concept not only has the capability of Query Processing and Data Integration for Industrial Data but also real-time visualization of data stream processing.     

Public and private attitudes towards “green” electricity: the case of Swedish wind power

There exists a political goal in Sweden and elsewhere to increase the use of renewable energy and wind power seems to be a favourable choice from an environmental perspective. However, although the public generally expresses a positive attitude towards wind power, the experience often shows that specific wind power projects face resistance from the local population. This paper analyses the attitudes towards wind power among the electricity consumers as well as the foundations of these attitudes. Results are based on a postal survey that was sent out to 1000 Swedish house owners. According to the results, the public is generally positive towards wind power. The probability of finding an average individual in support of wind power decreases with age and income. People with an interest in environmental issues are, however, more likely to be positive towards wind power than the average respondent and the results do not support the NIMBY-hypothesis. In addition, people that are more inclined to express public preferences are also more likely to be positive towards wind electricity than people who are less inclined to do so. These results imply, for instance, that the potential of markets for “green” electricity may be limited, other support schemes is thus required if the politically stated goal to increase wind power capacity is to be fulfilled.     

The Food Choice Questionnaire: Factorial invariant over western urban populations?

To assess the degree of measurement invariance of the Food Choice Questionnaire (FCQ) across western urban populations, it was filled out by demographically comparable samples in Canada (163 English speaking students, original version), Belgium (Flanders, N = 176, Dutch translation), and Italy (N = 163, Italian translation). Reliability of the FCQ-scales was moderate to good, but sometimes differed from the normative values. Item analysis pinpointed items with skewed distributions and low item-total correlations. Subsequent confirmatory and exploratory factor analyses revealed a suboptimal fit for the FCQ-model in all samples, with small to considerable divergences from the original configuration. The findings do not support the generalizability of the FCQ’s factor structure, but suggest that its items and underlying constructs may have different connotations across western urban populations. Explanations for the lack of convergence in factor structure and implications for research are discussed.     

Analysis of a Novel Flux-Weakening Structure of High-Speed Permanent-Magnet Motors Based on Soft-Magnetic Ferrite

Soft-magnetic ferrite has advantages of high initial magnetic permeability, high electric resistivity, low loss and low cost in the high-frequency alternating field. Considering the characters of soft-magnetic ferrite, this paper uses a soft-magnetic ferrite ring as stator core and presents a novel field-weakening structure of high speed PM motors. Based on this structure, a novel field-weakening method of PM motors can be presented. The equivalent reluctance of permanent magnetic field circuit can be changed by altering saturation degree of soft-magnetic ferrite core in this method. For the convenience of analysis and calculation, considering the magnetic field distribution in the motors, this paper deduces an equivalent two-dimensional finite element model of three-dimensional magnetic field, by which the field-weakening parameters of high speed PM motor can be conveniently analyzed. The simulation results of finite element model prove: the field-weakening structure of high speed PM motor based on soft-magnetic ferrite, presented in this paper, is valid and feasible, offers a valid and applicative method for field-weakening control of high speed PM motor.     

Flexible and robust CAD models for design automation

This paper explores novel methodologies for enabling Multidisciplinary Design Optimization (MDO) of complex engineering products. To realize MDO, Knowledge Based Engineering (KBE) is adopted with the aim of achieving design reuse and automation. The aim of the ongoing research at Linköping University is to shift from manual modeling of disposable geometries to Computer Aided Design (CAD) automation by introducing generic high level geometry templates. Instead of repeatedly modeling similar instances of objects, engineers should be able to create more general models that can represent entire classes of objects. The proposed methodology enables utilization of commercial design tools, hence taking industrial feasibility into consideration. The concept of High Level CAD templates (HLCt) will be proposed and discussed as the building blocks of flexible and robust CAD models, which in turn enables high fidelity geometry in the MDO loop. Furthermore, quantification of the terms flexibility and robustness are presented, providing a means to measure the quality of the geometry models. Finally, application examples are presented in which the outlined framework is evaluated. The applications have been chosen from three ongoing research projects aimed at automating the design of transport aircraft, industrial robots, and micro air vehicles.     

Environmental-aware virtual data center network

Cloud computing services have recently become a ubiquitous service delivery model, covering a wide range of applications from personal file sharing to being an enterprise data warehouse. Building green data center networks providing cloud computing services is an emerging trend in the Information and Communication Technology (ICT) industry, because of Global Warming and the potential GHG emissions resulting from cloud services. As one of the first worldwide initiatives provisioning ICT services entirely based on renewable energy such as solar, wind and hydroelectricity across Canada and around the world, the GreenStar Network (GSN) was developed to dynamically transport user services to be processed in data centers built in proximity to green energy sources, reducing Greenhouse Gas (GHG) emissions of ICT equipments. Regarding the current approach, which focuses mainly in reducing energy consumption at the micro-level through energy efficiency improvements, the overall energy consumption will eventually increase due to the growing demand from new services and users, resulting in an increase in GHG emissions. Based on the cooperation between Mantychore FP7 and the GSN, our approach is, therefore, much broader and more appropriate because it focuses on GHG emission reductions at the macro-level. This article presents some outcomes of our implementation of such a network model, which spans multiple green nodes in Canada, Europe and the USA. The network provides cloud computing services based on dynamic provision of network slices through relocation of virtual data centers.     

Quantifying DNS namespace influence

Name resolution using the Domain Name System (DNS) is integral to today’s Internet. The resolution of a domain name is often dependent on namespace outside the control of the domain’s owner. In this article we review the DNS protocol and several DNS server implementations. Based on our examination, we propose a formal model for analyzing the name dependencies inherent in DNS. Using our name dependency model we derive metrics to quantify the extent to which domain names affect other domain names. It is found that under certain conditions, more than half of the queries for a domain name are influenced by namespaces not expressly configured by administrators. This result serves to quantify the degree of vulnerability of DNS due to dependencies that administrators are unaware of. When we apply metrics from our model to production DNS data, we show that the set of domains whose resolution affects a given domain name is much smaller than previously thought. However, behaviors such as using cached addresses for querying authoritative servers and chaining domain name aliases increase the number and diversity of influential domains, thereby making the DNS infrastructure more vulnerable.     

Supply chain trust diagnosis (SCTD) using inductive case-based reasoning ensemble (ICBRE): The case of general competence trust diagnosis

General competence trust among supply chain partners, referring to the trust that a partner holds the general ability of fulfilling contracts, is a critical factor to ensure effective cooperation in a supply chain, especially in the current financial crisis. The method of supply chain trust diagnosis (SCTD) is to evaluate whether or not a partner holds such competence. This research devotes to an early investigation on diagnosing competence trust of supply chain with the method of inductive case-based reasoning ensemble (ICBRE). The so-called supply chain trust diagnosis with inductive case-based reasoning ensemble consists of five levels, that is, information level, the level of ratios of general competence states, the level of inductive case-based reasoning, ensemble level, and diagnosis result level. Knowledge for diagnosing competence trust, which composes of a case base, is hidden in data represented by ratios of general competence states. Inductive approach is combined with randomness to construct diverse and good member methods of inductive case-based reasoning. Finally, simple voting is used to integrate outputs of member inductive case-based reasoning methods in order to produce the final diagnosis on whether or not a partner holds the general ability of fulfilling contracts. We statistically validated results of the method of supply chain trust diagnosis with inductive case-based reasoning ensemble by comparing them with those of multivariate discriminant analysis, logistic regression, single Euclidean case-based reasoning, and single inductive case-based reasoning. The results indicate that the method of supply chain trust diagnosis with inductive case-based reasoning ensemble significantly improves predictive capability of case-based reasoning in this problem and outperforms all the comparative models by group decision of several decision-making agents and non-strict assumptions like statistical methods.     

Developing a talent training model related to chemical process safety based on interdisciplinary education in China

While chemical industrial development in China is growing rapidly, the corresponding safety training resources remain inadequate, which may often lead to increased risk of chemical accidents. These accidents are often associated with the negligence of safety management, poor safety hazard awareness, and lack of safety practice. In order to alleviate these prominent risk factors in chemical industries in China, our study develops a talent training model related to chemical process safety. First, we propose an approach for establishing the “talent training model” related to chemical process safety, consisting of three steps: analyzing the current status and existing problems of talent training related to chemical process safety, determining the theoretical basis and training objectives for developing interdisciplinary talents, and designing a new talent training model. Second, we establish a talent training model using the proposed method, which includes a comprehensive curriculum system, a diversified teaching pattern, and a quintuple evaluation method. Furtherly, we determine the expected outcomes of the talent training model. The research results provide an innovative chemical process safety training method that is applicable nationwide, also it works as a reference for other rapidly developing countries in the chemical process industry to improve safety within the chemical industry.