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Modeling and simulation for additive manufacturing (AM) is commonly used in industry. Nevertheless, a central issue remaining is the integration of different models focusing on different objectives and targeting different levels of details. The objective of this work is to increase the prediction capability of characteristics and performances of additively manufactured parts and to co-design parts and processes. The paper contributes to this field of research by integrating part's performance model and additive technology process model into a single early integrated model. The paper uses the dimensional analysis conceptual modeling (DACM) framework in an AM perspective to generate causal graphs integrating the AM equipment and the part to be printed. DACM offers the possibility of integrating existing knowledge in the model. The framework supported by a computer tool produces a set of governing equations representing the relationships among the influencing variables of the integrated model. The systematic identification of the weaknesses and contradictions in the system and qualitative simulation of the system are some of the potential uses of the model. Ultimately, it is a way to create better designs of machines and parts, to control and qualify the manufacturing process, and to control three-dimensional (3D) printing processes. The DACM framework is tested on two cases of a 3D printer using the fused filament fabrication (FFF) powder bed fusion. The analysis, applied to the global system formed of the 3D printer and the part, illustrates the existence of contradictions. The analysis supports the early redesign of both parts and AM process (equipment) and later optimization of the control parameters.

Time pressure imposed to the engineering design process is one fundamental constraint pushing engineers to rush into known solutions, to avoid analysing properly the environment of a design problem, to avoid modelling design problems and to take decision based on isolated evidences. Early phases in particular have to be kept short despite the large impact of decisions taken at this stage. Significant efforts are currently spent within different engineering communities to develop a model-based design approach adapted to conceptual stages. Developing such type of models is also challenging due to the fuzziness of the information and due to the complexity of the concepts and processes manipulated at this stage. Currently few support tools are really capable of really supporting an analysis of the early design concepts and architectures. Simultaneously the approach should be fast, easy to use and should provide a real added-value to efficiently support the decision and the design process. The present article is presenting a framework based on a progressive transformation of the design concepts. The final material generated by this transformation process is an oriented graph with different types of classified variables. This graph can be processed as described in the article to automatically exhibit the conflicts or contradictions present in the design concept architecture. The article is proposing two main contributions which are a real move toward model development at conceptual stage and the possibility to process those models to detect solution weaknesses. The discussion is presenting further developments and possibilities associated with this method.

Today, one of the major challenges in full-vehicle model creation is to get domain models from different experts while detecting any potential inconsistency problem before the Integration, Verification, Validation, and Qualification phase. To overcome such challenges, the conceptual design phase has been adapted to the current model development process. For that, the system engineers start to define the most relevant system architecture by respecting quality and time constraints. Next, the simulation model architects design the delivered system architecture in a more formal way with a modeling and simulation point of view to support the integration of domain-level simulation models in a consistent fashion. Finally, the model architects negotiate with different simulation model providers with the aim of specifying vehicle- and domain-level simulation models and their interface connections. To improve knowledge sharing between the mentioned actors, we propose a model identity card (MIC) for classifying simulation model knowledge, including input/output parameters, method, and usage specifications. The fundamental concepts that form the basis of all simulation models are identified and typed for implementation into a computational environment. An industrial case study of the engine–after-treatment model is used to show how MICs and the integrated model design phase might be used in a given scenario. A validation protocol is conducted through a heuristic observation to estimate the rate of model rework and ambiguity reduction.

Model-based product design using computer simulation has become a standard design practice in most companies in mechanical engineering. However, there is a need for efficient simulation tools that can provide design-supporting information already at early design phase when the most important decisions are made. Design process and design tools need to be agile and enable iterative process where the design and its requirements can effectively be iterated. Low-fidelity models can be part of the solution for time issue in early design phase. Low-fidelity prototypes are simplified representations of functions and concepts in the virtual prototype. Axiomatic design with low-fidelity modelling approach is a promising concept for achieving design-supporting information in an efficient way. In this method, there is a linear mapping between design parameters and system characteristics. Non-linear models of the system are linearized at the nominal point. An engineering design analysis tool (EDA tool) to enhance EDA is constructed and presented in this paper. For evaluation of the usefulness of this tool, a case study is presented. The case study deals with a simple hydraulic crane that is manufactured from steel plate. The results of the case study design are compared with results achieved with conventional CAD and FEM tools. Modelling accuracy and required modelling and simulation efforts are compared in both cases.

Design appears to be a key and critical stage of product lifecycle. Different models have been introduced in previous research to describe the conceptual design process. The RFBS model is one of them extending and deepening the existing FBS models. A previous paper was presenting the model and assuming the possible execution of the process tasks automatically. The present paper provides an overview of the progresses that have been made in this direction during the past years. The model-driven engineering philosophy underlying the RFBS model of knowledge is concretely exemplified in this paper. The implementation through ontology and language such as SysML that was part of the model-driven engineering philosophy is concretely described in this paper in form of computer-aided tools dedicated to the conceptual design stages.

The early stages of the design process are keys in the development of products and services. Nevertheless, they are marked by multiple constraints imposed on them, such as, most notably, a limited amount of time available for modelling and evaluating ideas and concepts. The present article develops an approach for modelling, and simulating initial design solutions during these critical early stages. The final objective is to minimize the amount of prerequisite knowledge a designer should have on the artefact being designed in order to propose, develop, and evaluate early models. First, the current work analyses the conditions necessary to develop a modelling and comparison environment for early design solutions. This is done through mathematical considerations of the design process. In a second part, the work proposes a modelling and simulation approach and develops the machinery behind it. The approach integrates and maps a series of normalized semantic descriptions of functions, generic engineering components and variables, a set of elementary laws associated with these components, and a set of elementary base units. All these elements are used to refine and guide the modelling process. This process is uses the Vaschy-Buckingham theorem followed by an approximation of the generic law describing the general behaviour of elementary components. This combination leads to an approximated model of the behaviour of the studied artefact. The model is further developed by implementing the behaviour in a system dynamics tool using two basic bricks of the system dynamics language, converters and flows. In a final part, the approach is illustrated through the case study of a beam structure.

Conceptual design has been broken down into sub-processes and elementary tasks in methodologies. These methodologies proposed suggest their systematic application. This paper assumes the possible execution of these tasks automatically. Nevertheless, it is necessary for computers to integrate the knowledge required during the conceptual design process. Knowledge models have been proposed, for instance Gero's Function–Behaviour–Structure (FBS) model for design. This paper presents the integration of methodologies with a model of knowledge for conceptual design in accordance with model-driven engineering. Our proposition extends the FBS model and presents its practical implementation through ontology and language such as SysML.

The application of additive manufacturing technologies in the industry is growing fast. This leads to an increasing need for reliable modeling techniques in the field of additive manufacturing. A methodology is proposed to systematically assess the influence of process parameters on the final characteristics of additively manufactured parts. The current study aims at presenting a theoretical framework dedicated to the modeling of the additive manufacturing technology. More specifically, the framework is used in the context of the study to plan and optimize the experimental process to minimize the amount of experiments required to populate the model. The framework presented is based on the Dimensional Analysis Conceptual Modelling framework (DACM). DACM is an approach supporting the production of models. This approach is designing networks representing a system architecture and behavior using an approach sharing similarities with neural networks. Based on the proposed approach, it is possible to detect where supplementary experimental data have to be collected to complete the model generated by the DACM approach. The modeling of the Direct Material Deposition process is conducted as an illustrative case study. The scope of the approach is vast and supported by validated scientific methods combined to form the core of the DACM method. The DACM framework is step by step extracting information from a description of the system architecture to create semi-automatically a model that can be simulated and used for multiple types of analyses associated for example with innovation and design improvement. The current paper will focus on the usage of the DACM framework, recently developed in a project, in the field of additive manufacturing.

Complex product development processes are evolving towards simulation driven design which leads to many heterogeneous computational models and design teams that interact with each other. However, this interaction creates a bottleneck for communication and models reuse throughout the design process because, very often, the model provider (i.e. analysts) and model users (i.e. designers) do not have the same level of understanding. In addition, the tools such as PDM (Product Data Management) or SDM (Simulation Data Management) consider the numerical models as black-box documents and they cannot access or link parameters and variables of models. The poverty of semantics in terms of simulation logics and design leads to a lack of interoperability between the contributing disciplinary simulation components, herein called numerical models. To reinforce this semantics, it is necessary to create a semantically-rich model characterization support to reduce knowledge gap between model provider and user, and to achieve a higher level of reuse. This work aims to introduce the first necessary step, herein, creation of domain ontology for formally characterizing reusable numerical model. Based on this common vocabulary, in automotive context, a Model Identity Card (MIC) is developed as an intermediate support which characterizes a model into five attributes; Physical Object, Interface, Methods, Means Usage, Validation and Verification. The MIC is illustrated with a Vehicle Thermic Comfort model example and a computer interface is developed to collect a series of representative MICs in a database.Copyright © 2013 by ASME

The need for support related to the complexity management of systems engineering problems, specifically for requirements management and changes is especially necessary during the early stages of the systems engineering process. Indeed, these stages have a tremendous impact on the overall outcome of a project. If not anticipated at early stages, changes in requirements are leading to changes in the design and in the later implementation stages, resulting in an unexpected increase in costs (monetary, time, etc.). The framework proposed in this article for requirements change prediction consists of a three steps process. First, requirements are modeled using SysML with predefined relationships. Second, all the relationships between requirements in the SysML model are transformed into an adjacency matrix also named DSM. A higher order Dependency Structure Matrix is applied; this matrix-based methodology allows support in the prediction of which requirements will be affected after a change in a specific requirement. Third, the change propagation path is visualized. Using this framework, it is possible to predict the possible propagation of changes in requirements. In addition, it is also possible to identify the requirements that can be reused. This can help to save the time and cost for developing a new system.

The scheduling of the design activities in product development process is a crucial step in early stages in order to achieve the project in time and cost-effective manner. In complex product development process, many dependency relationships or feedback loop could exist between design activities with multiple technical domains. Sequencing the design activities is a decision making process in order to reduce these feedback loops, and therefore, reduce amount of required information flows between activities. In recent years, methods for sequencing the design activities in design process have been proposed in order to reduce lead-time development and cost. The purpose of this research work is to present a methodology for design process sequencing in product development project by using Design Structure Matrix (DSM) for visualization a complex process and Discrete Differential Evolution (DDE) for sequencing the design tasks. The tests performed in this article have shown that this approach provides very competitive results in term of the quality of obtained solutions when compared to Genetic algorithms (GA). In additional it is a simple, effective and easy to use since the amount of control parameters to set is reduced.Copyright © 2012 by ASME

The early development process of products imposes to fulfill numerous types of requirements simultaneously. Those requirements are often qualitative and imprecise. The main tasks of a development team consist of grasping and understanding customer needs, refining these needs, synthesizing concepts of solutions, evaluating and selecting appropriate solutions. Evaluating and selecting solutions is a critical stage because various types of metrics are used to measure performances of concepts. This paper presents both theoretical contributions and practical implementations. This article presents a theoretical analysis of resource consumption and environmental impact from the viewpoint of exergy. In addition, this article provides a theoretical answer to the issue of qualitative modeling of early design concepts of solutions. This article opens a fruitful perspective for combining heterogeneous requirements in a coherent and systematic manner by using dimensional analysis calculus.

Environmental evaluation analysis made at an early design stage is an important practical problem because existing approaches such as the LCA method require detailed information about the studied product or service. Consequently, to be efficient such a method requires a product or service located in an advanced development phase. An exergy method offers an appropriate solution for an environmental evaluation analysis at an early design stage. The theoretical model has been validated in previous journal articles. The goal of the present article is to validate the concordance of the results provided by the exergy and LCA approach, as LCA is a widely used approach even though the scientific reliability of LCA techniques has been questioned. This paper addresses the last phase of the validation of the exergy approach by comparing results of a case study analysed through both commercial LCA software and an exergy approach.

Early design is widely accepted inside the engineering design community as a crucial design stage. This is due to the fact that decisions taken at this stage constrain heavily the final performance of products. This article presents a design framework for the early design stage of mechatronic products. This framework provides a scientifically coherent methodology for refinement, analysis, modelling, comparison and evaluation of design solutions at early stage of the design process. A System Modelling Language (SysML) is proposed as a powerful modelling language properly adapted to mechatronic requirements. In addition, the article proposes to combine SysML with dimensional analysis and qualitative physics in order to provide a design tool able to carry out also early simulations, comparisons and evaluations.

In engineering design, the needs of stakeholders are often captured and expressed in natural language (NL). While this facilitates such tasks as sharing information with non-specialists, there are several associated problems including ambiguity, incompleteness, understandability, and testability. Traditionally, these issues were managed through tedious procedures such as reading requirements documents and looking for errors, but new approaches are being developed to assist designers in collecting, analysing, and clarifying requirements. The quality of the end-product is strongly related to the clarity of requirements and, thus, requirements should be managed carefully. This paper proposes to combine diverse requirements quality measures found from literature. These metrics are coherently integrated in a single software tool. This paper also proposes a new metric for clustering requirements based on their similarity to increase the quality of requirement model. The proposed methodology is tested on a case study and results show that this tool provides designers with insight on the quality of individual requirements as well as with a holistic assessment of the entire set of requirements.Copyright © 2013 by ASME