An IoT based industry 4.0 architecture for integration of design and manufacturing systems.

This paper proposes an Internet of Things (IoT) based 5-stage Industry 4.0 architecture to integrate the design and manufacturing systems in a Cyber Physical Environment (CPE). It considers the transfer of design and manufacturing systems data through the Cloud/Web-based (CW) services and discusses an effective way to integrate them. In the 1st stage, a Radio-Frequency IDentification (RFID) technology containing Computer Aided Design (CAD) data/models of the product with the ability to design / redesign is scanned and sent to a secure Internet/Cloud Server (CS). Here the CAD models are auto identified and displayed in the Graphical User Interface (GUI) developed for the purpose. From the scanned RFID CAD data/models, the 2nd stage adopts unique machine learning technique(s) and identifies the design & manufacturing features information required for product manufacture. Once identified, the 3rd stage handles the necessary modelling changes as required to manufacture the part by verifying the suitability of process-based product design through user input from the GUI. Then, it performs a Computer Aided Process Planning (CAPP) sequence in a secure design cloud server designed using web-based scripting language. After this, the 4th stage generates Computer Aided Manufacturing (CAM) toolpaths by continuous data retrieval of design and tooling database in the web server by updating the RFID technology with all the information. The various processes involved the 3rd and 4th stages are completed by using ‘Agents’ (a smart program) which uses various search and find algorithms with the ability to handle the changes to the process plan as required. Finally, the 5th stage, approves the product manufacture instructions by completing the production plan with the approved sheets sent to the Computer Numerical Control (CNC) machine. In this article, the proposed architecture is explained through the concept of IoT data transfer to help industries driving towards Industry 4.0 by improving productivity, reducing lead time, protecting security and by maintaining internationals standards / regulations applied in their workplace

A Filosofia da Ciência e a Sua Extensão à Engenharia

A Escola de Viena da Filosofia da Ciência, que se desenvolveu nas primeiras décadas do século XX, tinha uma visão ambiciosa da Ciência. Acreditava, nomeadamente, que seria possível formular normas gerais para o processo científico, analisar a estrutura lógica dos conhecimentos científicos, e mostrar que a Ciência serve o objectivo racional de adquirir um conhecimento global e fiável do Universo. Os filósofos da Escola de Viena, que representaram em grande parte a corrente neo-positivista da Filosofia da Ciência, defendiam o chamado verificacionismo, segundo o qual as proposições das ciências empíricas só têm sentido se forem verificáveis por observações de carácter experimental. De acordo com os neo-positivistas, as construções teóricas susceptíveis de tornar possível explicar e prever, só seriam válidas se fossem apoiadas num procedimento hipotético-dedutivo resultante de uma combinação de indução e dedução

A multi-agent approach for design consistency checking

The last decade has seen an explosion of interest to advanced product development methods, such as Computer Integrated Manufacture, Extended Enterprise and Concurrent Engineering. As a result of the globalization and future distribution of design and manufacturing facilities, the cooperation amongst partners is becoming more challenging due to the fact that the design process tends to be sequential and requires communication networks for planning design activities and/or a great deal of travel to/from designers' workplaces. In a virtual environment, teams of designers work together and use the Internet/Intranet for communication. The design is a multi-disciplinary task that involves several stages. These stages include input data analysis, conceptual design, basic structural design, detail design, production design, manufacturing processes analysis, and documentation. As a result, the virtual team, normally, is very changeable in term of designers' participation. Moreover, the environment itself changes over time. This leads to a potential increase in the number of design. A methodology of Intelligent Distributed Mismatch Control (IDMC) is proposed to alleviate some of the related difficulties. This thesis looks at the Intelligent Distributed Mismatch Control, in the context of the European Aerospace Industry, and suggests a methodology for a conceptual framework based on a multi-agent architecture. This multi-agent architecture is a kernel of an Intelligent Distributed Mismatch Control System (IDMCS) that aims at ensuring that the overall design is consistent and acceptable to all participating partners. A Methodology of Intelligent Distributed Mismatch Control is introduced and successfully implemented to detect design mismatches in complex design environments. A description of the research models and methods for intelligent mismatch control, a taxonomy of design mismatches, and an investigation into potential applications, such as aerospace design, are presented. The Multi-agent framework for mismatch control is developed and described. Based on the methodology used for the IDMC application, a formal framework for a multi-agent system is developed. The Methods and Principles are trialed out using an Aerospace Distributed Design application, namely the design of an A340 wing box. The ontology of knowledge for agent-based Intelligent Distributed Mismatch Control System is introduced, as well as the distributed collaborative environment for consortium based projects

Meta-modeling design expertise

The general problem that this research addresses is that despite the efforts of cognitive studies to describe and document the behavior of designers in action and the evolution of computer-aided design from conceptual design to fabrication, efforts to provide computational support for high-level actions that designers execute during the creation of their work have made minimal progress. In this regard this study seeks answers to the following questions: What is the nature of design expertise? How do we capture the knowledge that expert designers embed in their patterns of organization for creating a coherent arrangement of parts? And how do we use this knowledge to develop computational methods and techniques that capture and reuse such expertise to augment the capability of designers to explore alternatives? The challenge is that such an expertise is largely based on experience, assumptions, and heuristics, and requires a process of elucidation and interpretation before any implementation into computational environments. This research adopts the meta-modeling process from the model-based systems engineering field (MBSE), understood as the creation of models of attributes and relationships among objects of a domain. Meta-modeling can contribute to elucidating, structuring, capturing, representing, and creatively manipulating knowledge embedded in design patterns. The meta-modeling process relies on abstractions that allow the integration of myriad physical and abstract entities independent from the complexity of the geometric models; mapping mechanisms that facilitate the interfacing of a repository of parts, functions, and even other systems; and computer-interpretable and human-readable meta-models that enable the generation and the assessment of both configuration specifications and geometric representations. For validation purposes three case studies from the domain of customs façade systems have been deeply studied using techniques of verbal analysis, complemented with digital documentation, for distilling the design knowledge that have been captured into the meta-models for reutilization in the generation of design alternatives. The results of this research include a framework for capturing and reusing design expertise, parametric modeling guidelines for reutilization, methods for multiplicity of external geometric representations, and the augmentation of the design space of exploration. The framework is the result of generalizing verbal analyses of the three case studies that allow the identification of the mechanics behind the application of a pattern of organization over physical components. The guidelines for reutilization are the outcome of the iterative process of automatically generating well-formed parametric models out of existing parts. The capability of producing multiple geometric representations is the product of identifying ae generic operation for interpreting abstract configuration specifications. The amplification of the design space is derived from the flexibility of the process to specify and represent alternatives. In summary, the adoption of the meta-modeling process fosters the integration of abstract constructs developed in the design cognition field that facilitate the manipulation of knowledge embedded in the underlying patterns of design organization. Meta-modeling is a mental and computational process based on abstraction and generalization that enable reutilization.Ph.D