Design automation for customised and large-scale additive manufacturing : a case study on custom kayaks

Additive Manufacturing (AM) offers the potential to increase the ability to customise large-scale plastic components. However, a substantial amount of manual work is still required during the customisation process, both in design and manufacturing. This paper looks into how the additive manufacturing of mass customised large-scale products can be supported. Data was collected through interaction with industrial partners and potential customers in a case study regarding the customisation of kayaks. As a result, the paper proposes a model-based methodology which combines design automation with a user interface. The results point to the benefit of the proposed methodology in terms of design efficiency, as well as in terms of displaying results to the end user in an understandable format

Innovation landscape and challenges of smart technologies and systems - a European perspective

Latest developments in smart sensor and actuator technologies are expected to lead to a revolution in future manufacturing systems’ abilities and efficiency, often referred to as Industry 4.0. Smart technologies with higher degrees of autonomy will be essential to achieve the next breakthrough in both agility and productivity. However, the technologies will also bring substantial design and integration challenges and novelty risks to manufacturing businesses. The aim of this paper is to analyse the current landscape and to identify the challenges for introducing smart technologies into manufacturing systems in Europe. Expert knowledge from both industrial and academic practitioners in the field was extracted using an online survey. Feedback from a workshop was used to triangulate and extend the survey results. The findings indicate three main challenges for the ubiquitous implementation of smart technologies in manufacturing are: i) the perceived risk of novel technologies, ii) the complexity of integration, and iii) the consideration of human factors. Recommendations are made based on these findings to transform the landscape for smart manufacturing

Industrial Design of Electric Machines Supported with Knowledge-Based Engineering Systems

The demand for electric machines has increased in the last decade, mainly due to applications that try to make a full transition from fuel to electricity. These applications encounter the need for tailor-made electric machines that must meet demanding requirements. Therefore, it is necessary for small-medium companies to adopt new technologies offering customized products fulfilling the customers’ requirements according to their investment capacity, simplify their development process, and reduce computational time to achieve a feasible design in shorter periods. Furthermore, they must find ways to retain know-how that is typically kept within each designer to retrieve it or transfer it to new designers. This paper presents a framework with an implementation example of a knowledge-based engineering (KBE) system to design industrial electric machines to support this issue. The devised KBE system groups the main functionalities that provide the best outcome for an electric machine designer as development-process traceability, knowledge accessibility, automation of tasks, and intelligent support. The results show that if the company effectively applies these functionalities, they can leverage the attributes of KBE systems to shorten time-to-market. They can also ensure not losing all knowledge, information, and data through the whole development process

Application of knowledge based engineering principles to intelligent automation systems

The automation of engineering processes provides many benefits over manual methods including significant cost and scheduling reduction as well as intangible advantages of greater consistency based on agreed methods, standardisation and simplification of complex problems and knowledge retention. Knowledge Based Engineering (KBE) and Design Automation (DA) are two sets of methodologies and technologies for automating engineering processes through software. KBE refers to the structured capture, modelling and deployment of engineering knowledge in high level intelligent systems that provide a wide scope of automation capability. KBE system development is supported by numerous mature methodologies that cover all aspects of the development process including: problem identification and feasibility studies, knowledge capture and modelling, and system design, development and deployment. Conversely, DA is the process of developing automated solutions to specific, well defined engineering tasks. The DA approach is characterised by agile software development methods, producing lower level systems that are intentionally limited in scope. DA-type solutions are more commonly adopted by industry than KBE applications due to shorter development schedules, lower cost and less complex development processes. However, DA application development is not as well supported by theoretical frameworks, and consequently, development processes can be unstructured and best practices not observed. The research presented in this thesis is divided into two key areas. Firstly, a methodology for automating engineering processes is proposed, with the aim of improving the accessibility of mature KBE methods to a broader industrial base. This methodology supports development of automation applications ranging in complexity from high level KBE systems to lower level DA applications. A complexity editing mechanism is introduced that relates detailed processes of KBE methodologies to a set of characteristics that can be exhibited by automated solutions. Depending on individual application requirements, complexity of automated solutions can lowered by deselecting one or more of these characteristics, omitting associated high-level processes from the development methodology. At the lowest level of complexity, the methodology provides a structured process for producing DA applications that incorporates principles of mature KBE methodologies. The second part of this research uses the proposed automation methodology to develop a system to automate the layout design of aircraft electrical harnesses. Increasing complexity of aircraft electrical systems has an associated increase in the number and size of electrical harnesses required to connect subsystems throughout the airframe. Current practices for layout design are highly manual, with many governing rules and best practices. The automation of this process will provide a significant reduction in low level, repetitive, manual work. The resulting automated routing tool implements path-finding techniques from computer game artificial intelligence and microprocessor design domains, together with new methods for incorporating the numerous design rules governing harness placement. The system was tested with a complex industrial test case, and was found to provide harness solutions in a fraction of the time and with comparable quality as equivalent manual design processes. The repeatability of the automated process can also minimise scheduling impacts caused by late design changes

Establishing a maturity model for design automation in sales-delivery processes of ETO products

Short delivery times are considered a competitive advantage in the engineer-to-order (ETO) sector. Design-related tasks contribute to a substantial amount of delivery times and costs since ETO products have to be either fully developed or adapted to customer specifications within tendering or order fulfillment. Approaches aiming at a computerised automation of tasks related to the design process, often termed design automation or knowledge-based engineering, are generally regarded as an effective means to achieve lead time and cost reductions while maintaining, or even improving product quality. In this study we propose a maturity model as a framework for analyzing and improving such activities in ETO companies. We contribute to the literature in being the first to investigate design automation in the ETO sector from a maturity perspective. Beyond that, we extend the extant literature on design automation, which is of a highly technical nature, by providing a framework considering organizational and managerial aspects. The findings indicate that five different levels of maturity can be achieved across the dimensions strategies, processes, systems, and people. Empirical cases give insight into these different levels. Our investigation draws from extant literature and a comparative case study involving four companies over two years

A new knowledge sourcing framework to support knowledge-based engineering development

New trends in Knowledge-Based Engineering (KBE) highlight the need for decoupling the automation aspect from the knowledge management side of KBE. In this direction, some authors argue that KBE is capable of effectively capturing, retaining and reusing engineering knowledge. However, there are some limitations associated with some aspects of KBE that present a barrier to deliver the knowledge sourcing process requested by the industry. To overcome some of these limitations this research proposes a new methodology for efficient knowledge capture and effective management of the complete knowledge life cycle. Current knowledge capture procedures represent one of the main constraints limiting the wide use of KBE in the industry. This is due to the extraction of knowledge from experts in high cost knowledge capture sessions. To reduce the amount of time required from experts to extract relevant knowledge, this research uses Artificial Intelligence (AI) techniques capable of generating new knowledge from company assets. Moreover the research reported here proposes the integration of AI methods and experts increasing as a result the accuracy of the predictions and the reliability of using advanced reasoning tools. The proposed knowledge sourcing framework integrates two features: (i) use of advanced data mining tools and expert knowledge to create new knowledge from raw data, (ii) adoption of a well-established and reliable methodology to systematically capture, transfer and reuse engineering knowledge. The methodology proposed in this research is validated through the development and implementation of two case studies aiming at the optimisation of wing design concepts. The results obtained in both use cases proved the extended KBE capability for fast and effective knowledge sourcing. This evidence was provided by the experts working in the development of each of the case studies through the implementation of structured quantitative and qualitative analyses

A process model in platform independent and neutral formal representation for design engineering automation

An engineering design process as part of product development (PD) needs to satisfy ever-changing customer demands by striking a balance between time, cost and quality. In order to achieve a faster lead-time, improved quality and reduced PD costs for increased profits, automation methods have been developed with the help of virtual engineering. There are various methods of achieving Design Engineering Automation (DEA) with Computer-Aided (CAx) tools such as CAD/CAE/CAM, Product Lifecycle Management (PLM) and Knowledge Based Engineering (KBE). For example, Computer Aided Design (CAD) tools enable Geometry Automation (GA), PLM systems allow for sharing and exchange of product knowledge throughout the PD lifecycle. Traditional automation methods are specific to individual products and are hard-coded and bound by the proprietary tool format. Also, existing CAx tools and PLM systems offer bespoke islands of automation as compared to KBE. KBE as a design method incorporates complete design intent by including re-usable geometric, non-geometric product knowledge as well as engineering process knowledge for DEA including various processes such as mechanical design, analysis and manufacturing. It has been recognised, through an extensive literature review, that a research gap exists in the form of a generic and structured method of knowledge modelling, both informal and formal modelling, of mechanical design process with manufacturing knowledge (DFM/DFA) as part of model based systems engineering (MBSE) for DEA with a KBE approach. There is a lack of a structured technique for knowledge modelling, which can provide a standardised method to use platform independent and neutral formal standards for DEA with generative modelling for mechanical product design process and DFM with preserved semantics. The neutral formal representation through computer or machine understandable format provides open standard usage. This thesis provides a contribution to knowledge by addressing this gap in two-steps: • In the first step, a coherent process model, GPM-DEA is developed as part of MBSE which can be used for modelling of mechanical design with manufacturing knowledge utilising hybrid approach, based on strengths of existing modelling standards such as IDEF0, UML, SysML and addition of constructs as per author’s Metamodel. The structured process model is highly granular with complex interdependencies such as activities, object, function, rule association and includes the effect of the process model on the product at both component and geometric attributes. • In the second step, a method is provided to map the schema of the process model to equivalent platform independent and neutral formal standards using OWL/SWRL ontology for system development using Protégé tool, enabling machine interpretability with semantic clarity for DEA with generative modelling by building queries and reasoning on set of generic SWRL functions developed by the author. Model development has been performed with the aid of literature analysis and pilot use-cases. Experimental verification with test use-cases has confirmed the reasoning and querying capability on formal axioms in generating accurate results. Some of the other key strengths are that knowledgebase is generic, scalable and extensible, hence provides re-usability and wider design space exploration. The generative modelling capability allows the model to generate activities and objects based on functional requirements of the mechanical design process with DFM/DFA and rules based on logic. With the help of application programming interface, a platform specific DEA system such as a KBE tool or a CAD tool enabling GA and a web page incorporating engineering knowledge for decision support can consume relevant part of the knowledgebase

Towards automated conceptual design space exploration

In\ua0mature and safety-concerned industries, such as the aerospace industry, product development is often incremental and design solutions are limited to improvements of an existing design. Radical changes to the known product architecture are avoided, for reasons of reliability, lack of technology or lack of design space exploration (DSE) methods. This thesis aims to investigate into the challenges for DSE, and how it can be improved to be faster, wider and more systematic. This research has been undertaken in four different research projects, addressing the challenges of the aerospace industry. The process of exploring the design space, the set of all possible designs, can be divided into three phases: to define the design space boundaries, to populate this design space with concepts, and lastly, to analyse the different concepts to find the one which provides the highest value. A deficiency in the description of functions and constraints which constitute the design space dimensions and boundaries, rooted in the lack of methods, has been identified to reduce the available search space already in the beginning. To populate this search space, developers need to generate representations of their new designs. These representations, commonly 3D geometries in the form of CAD models, are too rigid in the form they are used today. Therefore, it is expensive to create many variants, which differ in solutions and shape. This reduces the design space population to only a few concepts, derived from the legacy design. The analysis of alternative concepts is challenged through different maturities and variety of concepts.The coverage of multiple hierarchical search spaces, from geometry over solutions to value, has been identified as a driver for wider DSE. Furthermore, the need for a product development approach that is capable to bridge the levels of modelling abstraction. Enhanced Function-Means (EF-M) modelling, a function model applied in all studies referenced in this thesis, bridges the abstraction from a verbal description to a teleological graph, while enabling a more systematic capture of the design space boundaries. However, a subsequent gap towards geometry models could be observed in all studies. This hindered a faster design space exploration, since extensive manual labour is required to bridge these abstraction levels. For further work, the closing of the abstraction gap in the product modelling methods is seen as the primary goal for further work, either by extending the already applied function- and geometry modelling methods, or by including other frameworks

Manufacturing compliance analysis for architectural design: a knowledge-aided feature-based modeling framework

Given that achieving nominal (all dimensions are theoretically perfect) geometry is challenging during building construction, understanding and anticipating sources of geometric variation through tolerances modeling and allocation is critical. However, existing building modeling environments lack the ability to support coordinated, incremental and systematic specification of manufacturing and construction requirements. This issue becomes evident when adding multi-material systems produced off site by different vendors during building erection. Current practices to improve this situation include costly and time-consuming operations that challenge the relationship among the stakeholders of a project. As one means to overcome this issue, this research proposes the development of a knowledge-aided modeling framework that integrates a parametric CAD tool with a system modeling application to assess variability in building construction. The CAD tool provides robust geometric modeling capabilities, while System Modeling allows for the specification of feature-based manufacturing requirements aligned with construction standards and construction processes know-how. The system facilitates the identification of conflicting interactions between tolerances and manufacturing specifications of building material systems. The expected contributions of this project are the representation of manufacturing knowledge and tolerances interaction across off-site building subsystems to identify conflicting manufacturing requirements and minimize costly construction errors. The proposed approach will store and allocate manufacturing knowledge as Model-Based Systems Engineering (MBSE) design specifications for both single and multiple material systems. Also, as new techniques in building design and construction are beginning to overlap with engineering methods and standards (e.g. in-factory prefabrication), this project seeks to create collaborative scenarios between MBSE and Building Information Modeling (BIM) based on parametric, simultaneous, software integration to reduce human-to-data translation errors, improving model consistency among domains. Important sub-stages of this project include the comprehensive review of modeling and allocation of tolerances and geometric deviations in design, construction and engineering; an approach for model integration among System Engineering models, mathematical engines and BIM (CAD) models; and finally, a demonstration computational implementation of a System-level tolerances modeling and allocation approach.Ph.D

Modelo de conocimiento para la planificación automática de la inspección en máquinas de medir por coordenadas

384 p.En esta tesis se presenta un modelo de conocimiento que sirve de base para la planificación automática de la inspección con Máquinas de Medir por Coordenadas (MMC) en el ciclo de desarrollo de productos. El modelo es flexible y está abierto a cualquier sistema de inspección o incluso a sistemas híbridos que incluyan la tecnología por contacto para geometrías sencillas típicas en piezas mecánicas basadas en primitivas o sus composiciones, y sistemas sin contacto para inspección de superficies complejas. Para el desarrollo del modelo se han aplicado técnicas de elicitación del conocimiento y metodologías de identificación y representación de éste, como son metodologías SADT, mapas conceptuales, diagramas de flujo, diagramas entidad-relación, etc. Para cada una de las actividades involucradas en el proceso de planificación, el conocimiento que se representa procede tanto de la sistematización de reglas ya conocidas pero no representadas formalmente y de reglas definidas exprofeso en esta tesis, al haberse constatado que no había base de conocimiento práctica donde apoyarse en algunos aspectos. Además, se ha estructurado y almacenado el conocimiento a través de la definición de una ontología, a la que se ha denominado ONTO-Process, válida para ser utilizada en la planificación de cualquier otro proceso del ciclo de vida de una pieza. Las reglas así definidas y representadas se han validado a través de su aplicación a dos piezas test. Estas dos piezas se han definido de tal manera que permiten jugar con los diferentes conceptos de conocimiento tratados en los modelos desarrollados