Search

This paper focuses on finite element modelling of temperature field in cylindrical grinding. The developed model is validated against two established and validated thermal models in cylindrical grinding. The models' results are compared in terms of suitability for a digital twin application. The computation time requirement for a real-time temperature estimator in grinding digital twin is defined. The FEM model offers the most fidelity, but computational demands prevent its use in a digital twin. Therefore, the need for coupling the FEM model with an AI-based approaches for future work is highlighted to achieve temperature estimation within the wheel contact time. publishedVersion

Research on human health and safety impacts of wire arc additive manufacturing is often overshadowed by the need for weld quality and mechanical strength improvements. To address this gap, a review of research literature is conducted focusing on the influence of welding process parameters, welding fumes, and fume exposure on worker health. The review uses a causal graph to classify research literature into two domains: manufacturing technology and public health. The graph serves as a precursor to development of a Bayesian network model, whose expected benefits, steps for implementation, and likely challenges that would be encountered during implementation are discussed. publishedVersion

In practical design work, a designer needs to consider the feasibility of a part for a manufacturing using additive manufacturing (AM) instead of conventional manufacturing (CM) technology. Traditionally and by default parts are assumed to be manufactured using CM and using AM as an alternative need to be justified. AM is currently often a more expensive manufacturing method than CM, but its employment can be justified due to number of reasons: improved part features, faster manufacturing time and lower cost. Improved part features means usually reduced mass or complex shape. However, in low volume production lower manufacturing time and lower part cost may rise to the most important characteristics.In this paper, we present a practical feasibility model, which analyses the added value of using AM for manufacturing. The approach is demonstrated in the paper on four specific parts. They represent real industrial design tasks that are ordered from an engineering office company. These parts were manufactured by Selective Laser Meting (SLM) technology and the original design done for conventional manufacturing is also presented and used for comparison purpose.

Dimensional Analysis Conceptual Modelling (DACM) is a framework used for conceptual modelling and simulation in system and product designs. The framework is based on cause–effect analysis b...

International audience; The quality of requirements is fundamental in engineering projects. Requirements are usually expressed partly or totally in a natural language (NL) format and come from different documents. Their qualities are difficult to analyze manually, especially when hundreds of thousands of them have to be considered. The assistance of software tools is becoming a necessity. In this article, the goal was to develop a set of metrics supported by NL processing (NLP) methods supporting different types of analysis of requirements and especially the dependencies between requirements. An NLP approach is used to extract requirements from text; to analyze their quality, links, similarities , and contradictions; and to cluster them automatically. The analysis framework includes different combinations of methods such as cosine similarity, singular value decomposition, and K-means clustering. One objective is to assess the possible combinations and their impacts on detections to establish optimal metrics. Three case studies exemplify and support the validation of the work. Graphs are used to represent the automatically clustered requirements, as well as similarities and contradictions. A new contradiction analysis process that includes a rules-based approach is proposed. Finally, the combined results are presented as graphs, which unveil the semantic relationships between requirements. Subsection 4.8 compares the results provided by the tool and the results obtained from experts. The proposed methodology and network presentation not only support the understanding of the semantics of the requirements but also help requirements engineers to review the interconnections and consistency of requirements systems and manage trace-ability. The approach is valuable during the early phases of projects when requirements are evolving dynamically and rapidly.

With the increasing design dimensionality, it is more difficult to solve multidisciplinary design optimization (MDO) problems. Many MDO decomposition strategies have been developed to reduce the dimensionality. Those strategies consider the design problem as a black-box function. However, practitioners usually have certain knowledge of their problem. In this paper, a method leveraging causal graph and qualitative analysis is developed to reduce the dimensionality of the MDO problem by systematically modeling and incorporating the knowledge about the design problem into optimization. Causal graph is created to show the input–output relationships between variables. A qualitative analysis algorithm using design structure matrix (DSM) is developed to automatically find the variables whose values can be determined without resorting to optimization. According to the impact of variables, an MDO problem is divided into two subproblems, the optimization problem with respect to the most important variables, and the other with variables of lower importance. The novel method is used to solve a power converter design problem and an aircraft concept design problem, and the results show that by incorporating knowledge in form of causal relationship, the optimization efficiency is significantly improved.

Increased competitiveness in the manufacturing industry demands optimizing performance at each level of an enterprise. Optimizing performance in terms of indicators such as manufacturing cost requires knowledge of cost-inducing variables from product design and manufacturing, and optimization of these variables. However, the number of variables that affect manufacturing cost is very high and optimizing all variables is time intensive and computationally difficult. Thus, it is important to identify and optimize select few variables that have high potential for inducing cost. Towards that goal, a dimension reduction method combining dimensional analysis conceptual modelling framework and graph centrality theory is proposed. The proposed method integrates existing knowledge of the cost inducing variables, their interactions, and input-output relationship for different functions or behavior of a system, in the form of a causal graph. Propagation of optimization objectives in the causal graph is checked to identify contradictory influences on the variables in the graph. Following the contradiction analysis, graph centrality theory is used to rank the different regions within the graph based on their relative importance to the optimization problem and to cluster the variables into two optimization groups namely, less important variables and most important variables relative to optimizing cost. The optimization problem is formulated to fix less important variables at their highest or lowest levels based on their interaction to cost and to optimize the more important variables to minimize cost. The proposed dimension reduction method is demonstrated for an optimization problem, to minimize the production cost of the bladder and key mechanism for a high-field superconducting magnet at CERN, capable of producing a 16 Tesla magnetic field. It was found that the graph region representing the electromagnetic force and resultant stress generated during energizing of the magnet ranked highest for influence on the bladder and key manufacturing cost. An optimization of the stress and its associated variables to minimize the manufacturing cost is performed using a genetic algorithm solver in Matlab.

Key performance indicators (KPIs) are used to monitor and improve production cost, quality, and time. A plethora of manufacturing KPIs are currently in use, with others continually being developed to meet organizational needs. However, obtaining the optimum KPI values at different organizational levels is challenging due to the complex interactions between manufacturing decisions, variables, and the desired targets. A Bayesian network is developed to characterize the interrelationships between manufacturing decisions and variables, selected KPI, and total production cost. For an additive manufacturing case, the approach enables appropriate KPI value estimation for achieving desired production cost targets in a manufacturing enterprise. publishedVersion

Implementation of additive manufacturing into product manufacturing suffers from the challenge of part defects prediction. Due to interdependencies of design variables and manufacturing parameters in achieving suitable part quality, modelling methods are necessary to provide simulation capabilities for part quality analysis at early stages of product development. A systematic methodology is proposed to extract cause-effect relationships among variables and to transform this causal model into a Bayesian network. The Bayesian network is then used to predict the effect of specific design and manufacturing parameters on part defects and to estimate the needed input parameters backwards, based on acceptable output values. publishedVersion

Additive manufacturing (AM) continues to rise in popularity due to its various advantages over traditional manufacturing processes. AM interests industry, but achieving repeatable production quality remains problematic for many AM technologies. Thus, modeling different process variables in AM using machine learning can be highly beneficial in creating useful knowledge of the process. Such developed artificial neural network (ANN) models would aid designers and manufacturers to make informed decisions about their products and processes. However, it is challenging to define an appropriate ANN topology that captures the AM system behavior. Toward that goal, an approach combining dimensional analysis conceptual modeling (DACM) and classical ANNs is proposed to create a new type of knowledge-based ANN (KB-ANN). This approach integrates existing literature and expert knowledge of the AM process to define a topology for the KB-ANN model. The proposed KB-ANN is a hybrid learning network that encompasses topological zones derived from knowledge of the process and other zones where missing knowledge is modeled using classical ANNs. The usefulness of the method is demonstrated using a case study to model wall thickness, part height, and total part mass in a fused deposition modeling (FDM) process. The KB-ANN-based model for FDM has the same performance with better generalization capabilities using fewer weights trained, when compared to a classical ANN.

This article documents a study on artificial neural networks (ANNs) applied to the field of engineering and more specifically a study taking advantage of prior domain knowledge of engineering systems to improve the learning capabilities of ANNs by reducing the dimensionality of the ANNs. The proposed approach ultimately leads to training a smaller ANN, offering advantage in training performances such as lower Mean Squared Error, lower cost and faster convergence. The article proposes to associate functional architecture, Pi numbers, and causal graphs and presents a design process to generate optimized knowledge-based ANN (KB-ANN) topologies. The article starts with a literature survey related to ANN and their topologies. Then, an important distinction is made between system behavior centered topologies and ANN centered topologies. The Dimensional Analysis Conceptual Modeling (DACM) framework is introduced as a way of implementing the system behavior centered topology. One case study is analyzed with the goal of defining an optimized KB-ANN topology. The study shows that the KB-ANN topology performed significantly better in term of the size of the required training set than a conventional fully-connected ANN topology. Future work will investigate the application of KB-ANNs to additive manufacturing.

Additive manufacturing (AM) continues to rise in popularity due to its various advantages over traditional manufacturing processes. AM interests industry, but achieving repeatable production quality remains problematic for many AM technologies. Thus, modeling the influence of process variables on the production quality in AM can be highly beneficial in creating useful knowledge of the process and product. An approach combining dimensional analysis conceptual modeling, mutual information based analysis, experimental sampling, factors selection, and modeling based on Knowledge-Based Artificial Neural Network (KB-ANN) is proposed for Fused Deposition Modeling (FDM) process. KB-ANN reduces the excessive amount of training samples required in traditional neural networks. The developed KB-ANN’s topology for FDM, integrates existing literature and expert knowledge of the process. The KB-ANN is compared to conventional ANN using prescribed performance metrics. This research presents a methodology to concurrently perform experiments, classify influential factors, limit the effect of noise in the modeled system, and model using KB-ANN. This research can contribute to the qualification efforts of AM technologies.

How to measure and train for adaptability has emerged as a priority in military contexts in response to emergent threats and technologies associated with asymmetric warfare. While much research effort has attempted to characterize adaptability in terms of accuracy and response time using traditional executive function cognitive tests, it remains unclear and undefined how adaptability should be measured and thus how simulation-based training should be designed to instigate and modulate adaptable behavior and skills. Adaptable reasoning is well-exemplified in the rescue effort of Apollo 13 by NASA engineers who repurposed available materials available in the spacecraft to retrieve the astronauts safely back to earth. Military leaders have anecdotally referred to adaptability as “improvised thinking” that repurposes “blocks of knowledge” to device alternative solutions in response to changes in conditions affecting original tasks while maintaining end-state commander’s intent. We review a previous feasibility study that explored the specification of Reusable Modeling Primitives for models and simulation systems building on Dimensional Analysis and Design Structure Matrix for Complexity Management formal methods. This Dimensional Analysis Conceptual Modeling (DACM) paradigm is rooted in science and engineering critical thinking and is consistent with the stated anecdotal premises as it facilitates the objective dimensional decomposition of a problem space to guide the corresponding dimensional composition of possible solutions. Arguably, adaptability also concerns the capability to overcome contradictions, detections, and reductions, which we present in an exemplar addressing the contradiction of increased drag due to increased velocity inherent to torpedoes. We propose that the DACM paradigm may be repurposed as a critical thinking framework for teaching the identification of relevant components in a theater of military operations and how the properties of those components may be repurposed to fashion alternative solutions to tasks involving navigation, call-for-fires, line-of-sight cover, weather and atmospheric effect responses, and others.

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.

This article presents the dimensional analysis conceptual modeling (DACM) framework, intended as a conceptual modeling mechanism for lifecycle systems engineering. DACM is a novel computer-aided method originally developed for military projects, but it's now available for other applications, too. The DACM framework is a powerful approach for specifying, discovering, validating, and reusing building blocks as well as analyzing system behavior in early development stages. This framework is based on dimensional analysis combined with causal graphs to represent the interactions and interdependencies among system variables. The framework's algorithms are codified into software applications to facilitate its use. This article provides a practical presentation of the steps that encompass the transformation from problem to solution space, key system variables extraction, causal ordering, clustering of variables, and qualitative analyses. The authors provide two examples that cover in detail the DACM's mathematic machinery for deriving a system's behavioral laws from a causal graph. The entire DACM approach is supported by a computer-based application that integrates all the steps of the framework presented in this article.

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.

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.

article accessible aussi en ligne via le lien suivant : www.cairn.info/revue-journal-of-innovation-economics-2014-3-page-59.htm.; International audience; The "hierarchical and linear model of innovation" (HLMI) is often used to describe how innovations are produced. HLMI presents several shortcomings and one possible way of overcoming them is to consider innovations from a system perspective. In order to achieve this, this article uses Kline and Rosenberg's chain-linked model (CLM, 1985) as a starting point and builds up on it, proposing an improvement rendering CLM more coherent with its systemic bases. The proposed improvement suggests conceiving innovation systems as associations of building blocks and using contemporary engineering diagram-based languages to display them. Nevertheless, this improvement does not challenge the dynamic features of innovation systems

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.

Managing requirements changes of complex systems and the potential impact of such changes represents a big issue for companies. Currently, commercial modelers propose tools for analyzing the direct impact of requirements changes on system design or code but the analysis of requirement change on other requirements remains seldom studied. This paper proposes an approach for the impact analysis of changes in requirements combined with a ranking of importance of requirements in graph based requirements network. Warshall algorithm is used in this paper for performing the impact analysis. Along with this approach, PageRank algorithm is used for ranking requirements according to their importance. Requirements hierarchy and their textual description of importance are considered as input for calculating their impact as well as their importance within the network of requirements. This combination of Warshall and PageRank algorithms provide significant results for helping designers in decision-making process of modifying requirements for future design versions.

Being able to quickly model and simulate very early design solutions in the design process is an important practical issue for engineering designers. Early design is characterized by the small amount of quantitative data available at the beginning of the development process. The task is becoming cumbersome for engineers when in addition they do not possess extensive knowledge of the domain of interest. In this context, traditional modeling and simulation methods are disqualified for supporting the early engineering design choices because they require too much details and precise quantitative information. The approach considered in this article to supply the deficiency of traditional modeling methods is combining three domains of physics and mathematics: qualitative physics, dimensional analysis and graph-based representation. The present article develops the general framework which is emerging from this combination. The authors develop the framework to the fast modeling and simulation of an air bearing. The structure of the article is the following, first the basis of the modeling and simulation method are briefly presented. In a second step the entire approach is developed on the case of an air bearing concept with the goal of making the presentation as pedagogical as possible. A causal ordering heuristic is used and combined with the topology of the concept to test. This is gradually leading to a causal graph which is transformed into a flow graph that can be simulated in system dynamics simulation tools. A new and easy approach to discover the laws governing the system dynamic model is also explained. Finally the model is simulated and analyzed. As a result, the method presented in this article offers several advantages: 1- it can be supported by a dedicated computer aided approach, 2- it brings simulation capabilities at very early design stage level where it is seldom present.

Network-on-chip (NoC) has been proposed for SoC (System-on-Chip) as an alternative to on-chip bus-based interconnects to achieve better performance and lower energy consumption. Several approaches have been proposed to deal with NoCs design and can be classified into two main categories, design-time approaches and run-time approaches. Design-time approaches are generally tailored for an application domain or a specific application by providing a customized NoC. All parameters, such as routing and switching schemes, are defined at design time. Run-time approaches, however, provide techniques that allow a NoC to continuously adapt its structure and its behavior (i.e., at runtime). In this paper, performance evaluation of a flow control algorithm for congestion avoidance in NoCs is presented. This algorithm allows NoC elements to dynamically adjust their inflow by using a feedback control-based mechanism. Analytical and simulation results are reported to show the viability of this mechanism for congestion avoidance in NoCs.

Early design is a critical stage in product and service development. The choices made during this period influence the final spread and acceptance of the artifact and therefore must be made in the best possible manner. Different aspects must be taken into account during decision making such as value, risk awareness, environmental impacts, etc. The present work studies value in engineering projects using dimensional analysis and its integration in the decision making framework while putting an accent on value considerations.

As stated by Zeng (Zeng, 2011), design comes from an environment, serves the environment, and changes the environment. Understanding the environment of a product is recognized by the research community to have a crucial impact on quality and success of products, services and complex systems. In this context, the aim of the Journal of Integrated Design and Process Science (JIDPS) is to publish research findings covering transdisciplinary notions of design and process. The focus of the journal is aligned with three fundamental objectives which are; a) the understanding of design and process crossing boundaries of natural, human, and built environments, b) the principles, methods, and tools, and c) the applications of design and process science to engineering and social problems. The present transaction is covering well those three lines of research by providing researches associated with: 1The optimization of the scheduling and organization of the system development process, 2The integration of product service system tools into the product development process, 3The analysis of the consistency in the architecture of large scale systems, 4The risk management in new product and service process, The present issue is trying to understand and act on the environment in large of systems or services by understanding the processes associated with the design of those systems and services. The issue is also including research work about the principles, the methods and the tools. The first paper, titled “A Combined Design Structure Matrix (DSM) and Discrete Differential Evolution (DDE) Approach for Scheduling and Organizing System Development Tasks Modelled using SysML” by Nonsiri et al, aims to develop a method supported by Artificial Intelligence approaches for automatically optimizing scheduling and organization of system development tasks. The purpose of this work is to support the scheduling and organization of tasks that can become very complex in system engineering process. Significant savings in term of development time can be made by providing a method that is optimizing the amount of feedbacks and iterations to the minimal level simply required for the successful development of the system. In this article, the authors are proposing to combine a computational approach (Discrete Differential Evolution) with Model Based Systems Engineering (MBSE) for minimizing iterations and reducing lead-time development. The present article is contributing to recent research works using Design Structure Matrixes (DSM) and computational methods for visualizing and analysing systems engineering processes. The practical use of the proposed framework is demonstrated on the case study of a mobile robot developed for the Eurobot competition. The article also

Product development is a challenging activity. The process begins with a description and representation of a design problem in form of a requirements document. It involves two phases: elicitation by description in Natural Language (NL) and clarification of the description. NL implies interpretation of terms within a context to avoid later misunderstanding. The paper proposes a methodology to elicit and refine the initial needs. The elicitation is done by finding support information from several sources such as patent databases, encyclopaedias and commercial websites. The refinement supported by a computer-based approach is done on different levels (grammar, words and context selection) to reduce the ambiguity of the requirements descriptions. The initial description is refined by an automatic questioning process. This is followed by an assisted search and selection of answers from different web-based sources. Relevant answers are selected using a similarity metric. A case study is used to demonstrate the approach.

This paper presents three interconnected developments made during the course of a recent collective research work, the development of a systematic graph-based search tool for physical contradictions, a ranking approach for defining the order of criticality of the design contradictions and the associated analysis of the different design strategies that can be used to solve those contradictions or to enhance performance indicators. The systematic graph-based search for physical contradictions is using the set of elementary variables necessary to describe the system as basic input. The initial set is extracted based on taxonomy of variables combining classification work from NIST and classification of variables derived from the Bond Graph theory. The contradiction search method is in a second step classifying the set of variables into three categories: the constraint variables imposed to the designers by the context and the environment, the design variables on which the designer as the possibility to act and the performance variables that are used to evaluate the performance of the designed system. In a third step, interactions between variables are searched using two possibilities: a causal ordering algorithm developed during the course of the research or via a collective work of experts. The result of this step is a directed graph starting from the constraints variables and ending with the performance variables. In the fourth step objectives have to be assigned to the performance variables (minimal value, maximal value or target value). Those objectives are propagated back into the graph by analyzing the impact of the variables interacting with the performance variables. A physical contradiction is detected each time it is discovered that a design variable is associated with two contradictory objectives. Following this approach, a contradiction is represented as a node in the directed graph. It is possible to systematically map the different design strategies that can be used and to rank the possible impact of those design strategies. The article presents a concrete application of the approach on the case study of an air bearing and demonstrates the novelty of the approach to generate new viewpoints and insight in the analysis of the early stages of the development process. The potential impact of such type of design support is potentially very important. A future step will consists of developing a computer aided tool implementing the method.

Additive manufacturing has been presented as a novel and competitive method to achieve unprecedented part shapes and material complexities. Though this holds true in niche markets, the economic viability of additive manufacturing for large-scale industrial production is still in question. Companies often struggle to justify their investment in additive manufacturing due to challenges in the integration of such technologies into mainstream production. First, most additive technologies exhibit a relatively low production rate when compared with traditional production processes. Second, there is a lack of robust design for additive manufacturing methods and tools that enable the leveraging of the attendant unique capabilities, including the ability to form organic part geometries and automated part consolidations. Third, there is a dearth of systematic part screening methods to evaluate manufacturability in additive manufacturing. To tackle the challenge of manufacturability evaluation, the present work proposes a novel approach derived from latent semantic analysis and dimensional analysis to evaluate parts and their production for a variety of selected metrics. The selected metrics serve as descriptors of design features and manufacturing functions, which are developed using functional modeling and dimensional analysis theory. Singular-value decomposition and Euclidean distance measurement techniques are used to determine the relative manufacturability for a set of parts for a specified manufacturing process technology. The utility of the method is demonstrated for laser powder bed fusion technology. While demonstrated for additive manufacturing here, the developed approach can be expanded for any given set of manufacturing processes. Expansion of this systemic manufacturability analysis method can support part design decision-making, process selection, and design and manufacturing optimization.