Product information management for complex modular security systems

Um sistema PIM gere toda a informação que possibilita a comercialização dos produtos através de diferentes canais. A sua importância durante o ciclo de vida de um produto aumentou devido à sofisticação técnica dos produtos, a gerir internamente e a publicar externamente. Sistemas, tais como o ERP e o CCMS, deverão integrar-se com um sistema PIM, o qual deve funcionar como a “espinha dorsal” da informação de produto. O presente projeto tem como objetivo principal a criação de uma solução para gerir a informação de produto para sistemas modulares complexos. A proposta de solução inclui a criação de uma ontologia para parte dos inúmeros sistemas disponíveis no catálogo de produtos de uma das maiores organizações multinacionais do setor de engenharia e tecnologia a nível mundial. O processo de criação da solução proposta baseou-se na metodologia de investigação pesquisa-ação e foi dividido em cinco fases. Na fase de diagnóstico descreveu-se e analisou-se a atual situação dos sistemas ERP e CCMS que gerem o catálogo online dos sistemas de produtos comercializados. Levantaram-se ainda as taxonomias de produto atuais e elaborou-se a proposta. Na fase de planeamento da ação descreveram-se a equipa de trabalho, a abordagem inspirada na metodologia Agile usada para desenvolver a solução, as reuniões de planeamento, os parceiros de trabalho, as ferramentas a usar e a sua justificação. Na fase de tomada de ação foi descrito o processo de criação da solução ontológica e o resultado final, incluindo a construção das novas taxonomias e a sua validação pelos especialistas. Propuseram-se exemplos e representações gráficas usando a ferramenta Protégé. Na fase de avaliação, a solução ontológica foi testada, tendo-se validado que os requisitos necessários foram satisfeitos pela estrutura. Na fase de especificação de aprendizagem propuseram-se os próximos passos para a implementação e gestão futura do modelo ontológico. Com esta solução, a organização poderá gerir mais eficientemente a informação de produto e a estrutura de dados. Ela possui versatilidade para gerir produtos individuais ou sistemas modulares complexos e melhorar a sua comunicação com o cliente. Além disso, a ontologia tem ainda um enorme potencial se combinada com técnicas de IA. Algumas limitações do projeto e propostas de trabalhos futuros foram ainda apresentadas

Synchro-push: A new production control paradigm

The paper aims at proposing a new production control paradigm, the Synchro-push, that offers a step forward with respect to the traditional push and pull production paradigms as for plant re-configurability power and quick reaction to demand changes: in fact, theoretically, it offers the advantages of the two traditional approaches without suffering their drawbacks. This could be of advantage for any manufacturing company and especially for SMEs (Small-Medium Enterprises), acting as a support against worldwide competition. The paper presents a brief history of the evolution of the push and pull approaches, the comparison between them and among the different alternatives that have been proposed in literature for their implementation. It presents the new approach, its theory and the subsequent industrial implications. The new approach is now made possible by the development of innovative smart technologies that allow the close-to-real-time decision making in scheduling and a higher level of modularity in the plant

Developing sensor signal-based digital twins for intelligent machine tools

Abstract Digital twins can assist machine tools in performing their monitoring and troubleshooting tasks autonomously from the context of smart manufacturing. For this, a special type of twin denoted as sensor signal-based twin must be constructed and adapted into the cyber-physical systems. The twin must (1) machine-learn the required knowledge from the historical sensor signal datasets, (2) seamlessly interact with the real-time sensor signals, (3) handle the semantically annotated datasets stored in clouds, and (4) accommodate the data transmission delay. The development of such twins has not yet been studied in detail. This study fills this gap by addressing sensor signal-based digital twin development for intelligent machine tools. Two computerized systems denoted as Digital Twin Construction System (DTCS) and Digital Twin Adaptation System (DTAS) are proposed to construct and adapt the twin, respectively. The modular architectures of the proposed DTCS and DTAS are presented in detail. The real-time responses and delay-related computational arrangements are also elucidated for both systems. The systems are also developed using a Java™-based platform. Milling torque signals are used as an example to demonstrate the efficacy of DTCS and DTAS. This study thus contributes toward the advancement of intelligent machine tools from the context of smart manufacturing

Can Cybersecurity Be Proactive? A Big Data Approach and Challenges

The cybersecurity community typically reacts to attacks after they occur. Being reactive is costly and can be fatal where attacks threaten lives, important data, or mission success. But can cybersecurity be done proactively? Our research capitalizes on the Germination Period—the time lag between hacker communities discussing software flaw types and flaws actually being exploited—where proactive measures can be taken. We argue for a novel proactive approach, utilizing big data, for (I) identifying potential attacks before they come to fruition; and based on this identification, (II) developing preventive counter-measures. The big data approach resulted in our vision of the Proactive Cybersecurity System (PCS), a layered, modular service platform that applies big data collection and processing tools to a wide variety of unstructured data sources to predict vulnerabilities and develop countermeasures. Our exploratory study is the first to show the promise of this novel proactive approach and illuminates challenges that need to be addressed

GO faster ChEBI with Reasonable Biochemistry

Chemical Entities of Biological Interest (ChEBI) is a database and ontology that represents biochemical knowledge about small molecules. Recent changes to the ontology have created new opportunities for automated reasoning with description logic, that have not previously been fully exploited in Chemistry. These changes open up the possibility of building an improved chemical semantic web, by making more use of necessary and sufficient conditions, allowing reasoning about chemical structure, highlighting ambiguous inconsistencies and improving alignment with the Gene Ontology (GO). This paper briefly discusses some of the problems with reasoning over the current version of ChEBI, to tackle these issues, and their potential solutions

Product, process and resource model coupling for knowledge-driven assembly automation

: Accommodating frequent product changes in a short period of time is a challenging task due to limitations of the contemporary engineering approach to design, build and reconfigure automation systems. In particular, the growing quantity and diversity of manufacturing information, and the increasing need to exchange and reuse this information in an efficient way has become a bottleneck. To improve the engineering process, digital manufacturing and Product, Process and Resource (PPR) modelling are considered very promising to compress development time and engineering cost by enabling efficient design and reconfiguration of manufacturing resources. However, due to ineffective coupling of PPR data, design and reconfiguration of assembly systems are still challenging tasks due to the dependency on the knowledge and experience of engineers. This paper presents an approach for data models integration that can be employed for coupling the PPR domain models for matching the requirements of products for assembly automation. The approach presented in this paper can be used effectively to link data models from various engineering domains and engineering tools. For proof of concept, an example implementation of the approach for modelling and integration of PPR for a Festo test rig is presented as a case study

The Infectious Disease Ontology in the Age of COVID-19

The Infectious Disease Ontology (IDO) is a suite of interoperable ontology modules that aims to provide coverage of all aspects of the infectious disease domain, including biomedical research, clinical care, and public health. IDO Core is designed to be a disease and pathogen neutral ontology, covering just those types of entities and relations that are relevant to infectious diseases generally. IDO Core is then extended by a collection of ontology modules focusing on specific diseases and pathogens. In this paper we present applications of IDO Core within various areas of infectious disease research, together with an overview of all IDO extension ontologies and the methodology on the basis of which they are built. We also survey recent developments involving IDO, including the creation of IDO Virus; the Coronaviruses Infectious Disease Ontology (CIDO); and an extension of CIDO focused on COVID-19 (IDO-CovID-19).We also discuss how these ontologies might assist in information-driven efforts to deal with the ongoing COVID-19 pandemic, to accelerate data discovery in the early stages of future pandemics, and to promote reproducibility of infectious disease research