This scientific paper introduces a Design Automation (DA) framework that streamlines the Design for Additive Manufacturing (DfAM) process. The framework is designed to simplify the creation and evaluation of different design options by automating geometry creation using high-level CAD templates and setting up and connecting Computer-Aided Engineering (CAE) models to perform functional and manufacturing evaluations. By considering manufacturing constraints early in the design process, the framework aims to investigate various design alternatives and facilitate design changes late in the development process without additional manual work. This framework provides a comprehensive view of the entire DfAM process, integrating everything from functional requirements to manufacturing evaluation and preparation into the same design automation framework. To demonstrate the usefulness of the framework, the authors used it to design a hydraulic pump. Compared to the original design, the design created with the proposed framework reduces pressure drop by more than 50% and reduces the pump's weight by 35%. Furthermore, on an assembly level, the framework consolidates four components into two and eliminates two sealings. In summary, the Design Automation framework introduced in this paper simplifies the DfAM process by enabling automation of geometry creation and the setup and connection of CAE models. The framework facilitates the exploration of different design alternatives early in the process, considering manufacturing constraints, and enables design changes later in the development process without manual work. The benefits of the framework are illustrated through the design of a hydraulic pump, where it achieved significant improvements in performance, weight, and assembly complexity. 

In times of rapid transformation of society in general and domains of technology in particular, questions are raised on how to effectively organise higher engineering education. As a response, this study examines the curriculum composition of eleven engineering programs to investigate curriculum nativeness, a novel approach for assessing curriculum characteristics. In addition to forming the construct nativeness, this study establishes a way to measure curriculum nativeness by determining the number of credits originating from what is characterised as native courses. Native refers to the way curriculum content reflects the main subject classification, connecting the content of the profession with the content of the program curriculum. This measure is used in correlation analyses and other dependency studies to assess performance of the students during their first year, including total grade point and attrition. The measure of curriculum nativeness is also used to compare programs. The results indicate that the level of native content in a curriculum influences student performance comparable to that of other learning types that are known to promote student achievement. In addition, this study indicates that native content credits are more frequently earned than non-native credits.

Purpose - The purpose of this paper is to present a Design for Additive Manufacturing (DfAM) methodology that connects several methods, from geometrical design to post-process selection, into a common optimisation framework.

Design/methodology/approach - A design methodology is formulated and tested in a case study. The outcome of the case study is analysed by comparing the obtained results with alternative designs achieved by using other design methods. The design process in the case study and the potential of the method to be used in different settings are also discussed. Finally, the work is concluded by stating the main contribution of the paper and highlighting where further research is needed.

Findings - The proposed method is implemented in a novel framework which is applied to a physical component in the case study. The component is a structural aircraft part that was designed to minimise weight while respecting several static and fatigue structural load cases. An addition goal is to minimise the manufacturing cost. Designs optimised for manufacturing by two different AM machines (EOS M400 and Arcam Q20+), with and without post-processing (centrifugal finishing) are considered. The designs achieved in this study show a significant reduction in both weight and cost compared to one AM manufactured geometry designed using more conventional methods and one design milled in aluminium.

Originality/value - The method in this paper allows for the holistic design and optimisation of components while considering manufacturability, cost and component functionality. Within the same framework, designs optimised for different setups of AM machines and post-processing can be automatically evaluated without any additional manual work.

This paper presents and analyses the research front on development methodologies that can be applied to flight simulations for development purposes. This field includes any flight simulator used during the development phase of an aircraft, when flight test or other validation data is still scarce. A review of the literature published between 1999-2019 is performed. As flight-specific literature on the topic is limited, a broader view on flight simulators is adopted. Simulators are regarded as risk-averse scientific software; that is, software created to understand a phenomenon and whose primary goal is to be correct. This perspective highlights the lack of suitable established software development methodologies (SwDev) for this software type. Two solutions to this problem have been identified: one treating scientific SwDev as a knowledge acquisition process, and another one treating it as development of enabling systems, following the established product development processes. These solutions need to be completed with methodologies to deal with the multidisciplinarity of the flight simulation problem, such as model exchange standards or workflows for multidisciplinary collaboration.

In order to efficiently design and deliver customized products, it is crucial that the process of translating customer needs to engineering characteristics and into unique products is smooth and without any misinterpretations. The paper proposes a method that combines design optimization with value-driven design to support and automate configuration of customized products. The proposed framework is applied to a case example with spiral staircases, a product that is uniquely configured for each customer from a set of both standard and customized components; a process that is complex, iterative and error-prone. In the case example, the optimization and value-driven design models are used to automate and speed-up the process of delivering quotations and design proposals that could be judged based on both engineering characteristics as well as their added value, thereby increasing the knowledge at the sales stage. Finally, a multi-objective optimization algorithm is employed to generate a set of Pareto-optimal solutions that contain four clusters of solutions that dominate the baseline design. Hence the decision-maker is given a set of optimal solutions to choose from when balancing different economical and technical characteristics.

This paper proposes an optimization framework based on the OpenMDAO software library intended for engineer-to-order products and applies it to the conceptual design of a Mobile Miner. A Mobile Miner is a complex machine and a flexible alternative to Tunnel Boring Machines for small-scale tunneling and mining applications. The proposed framework is intended for use in early design and quotation stages with the objective to get fast estimates of important product characteristics, such as excavation rate and cutter lifetime. The ability to respond fast to customer requests is vital when offering customized products for specific applications and thereby to stay competitive on the global market. This is true for most engineer-to-order products and especially for mining equipment where each construction project is unique with different tunnel geometries and rock properties. The presented framework is applied to a specific use-case where the design of the miner's cutter wheel is in focus and a set of Pareto optimal designs are obtained. Furthermore, the framework extends the capabilities of OpenMDAO by including support for mixed-variable formulations and it supports an exploratory approach to design optimization.

System-of-Systems Engineering (SoSE) has become a constantly growing field within product development for complex systems. Systems are becoming more and more connected with other systems and the operational environment in general. This takes the development process to new levels of complexity where high degrees of uncertainties are expected due to ever occurring changes in the operational environment, and other external factors such as politics, economy, and technology. This creates a need of being able to understand the influence of changes early in the development process and to facilitate the systems? perseverance. The focus of the development shifts from fulfilling specific requirements, to being able to meet needs and deliver capabilities over time. Additionally, modeling and simulation for complex systems and System-of-Systems (SoS) becomes a valued alternative to the economically prohibited and almost impossible prototype testing. In consideration of this problem, the presented work introduces a method for both modeling and simulation of a SoS. The method uses ontology with description logic reasoning to derive and narrow down a SoS design space which is further analyzed using Agent Based Simulation (ABS). A Search and Rescue (SAR) scenario is used as a case study to test the method. Measures of Effectiveness (MoE), based on the time it takes to find a rescue subject and the cost of doing so, are used to evaluate the SoS performances. The presented method is envisioned to be used early in the development of complex systems and SoS to increase the overall understanding of them.

In order to enable easy modification of results from a design optimization process in a CAD tool, a flexible representation of the geometry is needed. This is not always trivial however, since many file formats are not importable as modifiable geometry into the CAD tool, and if they are, they might not represent the geometry in a way that enables easy modification. To mitigate this problem a design automation (DA) and a machine learning (ML) approach are developed and compared using a test case from an optimization process used to optimize hose routing in tight spaces. In the test case used, the geometry from the optimization process consists of center curves represented as a large number of points. To enable easy modification a more flexible representation is needed such as a spline with a few well-placed control points. Both the DA and ML approach can approximate center curves from the optimization process as splines containing a varying number of control points but do show different properties. The DA approach is considerably slower than the ML but adds a lot of flexibility regarding accuracy and the number of control points used.

With industries striving towards increased customisation of complex products through engineer-to-order, methods are continuously sought to rationalise the product development process. To this end, a framework is proposed using CAD configurators, utilising design automation and knowledge-based engineering to integrate sales and design processes in product development. The application of this framework to the design of spiral staircases is described and analysed, with results showing decreased lead-times and a decreased risk for design errors.

The operational environment of aerospace products can have a big impact on their performance, and therefore it is an aspect that should be considered as early as possible in their development process. In this light, this paper presents an analysis and optimization framework that can be used in the development of unmanned aerial vehicles (UAV) in order to efficiently explore the design space when considering system-of-systems (SoS) formulations. In this work, the problem is decomposed with a multilevel architecture, and it is divided into three parts, which enable a multifidelity evaluation of the SoS, constituent systems (CS), and subsystems (SS) levels. To be able to capture the collaboration between different assets at an SoS level the framework includes an in-house tool for performing agent-based simulations, whereas the specifications of each CS are analyzed through a set of basic aeronautical disciplines and a number of SS models. The framework is tested against two surveillance scenarios by considering existing as well as new UAV designs, and overall it is shown that the exploration of SoS enables to identify better capabilities; that the operational environment poses different requirements on all system levels; and that fleet tactics have a profound effect on mission performance.

This paper presents a method for multi-objective optimization of hose assembly routing. Hose routing is a non-trivial task which demand a lot of iterations, especially with the increased complexity in modern vehicles. The proposed method utilizes design automation through multi-objective optimization of routing assemblies containing multiple hoses. The method is intended as a decision support and automation-tool, that reduces the number of iterations needed. The method has been implemented and tested on a case, concerning a set of hoses in an engine compartment, showing credible results.

Purpose

This paper aims to review recent research in design for additive manufacturing (DfAM), including additive manufacturing (AM) terminology, trends, methods, classification of DfAM methods and software. The focus is on the design engineer’s role in the DfAM process and includes which design methods and tools exist to aid the design process. This includes methods, guidelines and software to achieve design optimization and in further steps to increase the level of design automation for metal AM techniques. The research has a special interest in structural optimization and the coupling between topology optimization and AM.

Design/methodology/approach

The method used in the review consists of six rounds in which literature was sequentially collected, sorted and removed. Full presentation of the method used could be found in the paper.

Findings

Existing DfAM research has been divided into three main groups – component, part and process design – and based on the review of existing DfAM methods, a proposal for a DfAM process has been compiled. Design support suitable for use by design engineers is linked to each step in the compiled DfAM process. Finally, the review suggests a possible new DfAM process that allows a higher degree of design automation than today’s process. Furthermore, research areas that need to be further developed to achieve this framework are pointed out.

Originality/value

The review maps existing research in design for additive manufacturing and compiles a proposed design method. For each step in the proposed method, existing methods and software are coupled. This type of overall methodology with connecting methods and software did not exist before. The work also contributes with a discussion regarding future design process and automation.

The introduction of Additive Manufacturing opens up possibilities for creating lighter, better and customized products. However, to take advantage of the possibilities of Additive Manufacturing, the design engineer is challenged. In this paper, a general design process for the creation of complex products is proposed and evaluated. The proposed method aims to aid a design process in which Topology Optimization (TO) is used for concept development, and the result is then interpreted into a Master Model (MM) supporting design evaluations during detailed design. At the same time as the MM is created, information regarding manufacturing is saved in a database. This makes it possible to automatically generate and export models for manufacturing or CAE analyses. A tool that uses Knowledge-Based Engineering (KBE) to realize the presented methodology has been developed. The tool is specialized for the creation of structural components that connect to other components in an assembly. A case study, part of an aircraft door, has been used for evaluation of the tool. The study shows that the repetitive work when interpreting the topology-optimized design could be reduced. The result comes in the form of a parametric CAD model which allows fast changes and the coupled database enables the export of models for various purposes.

Finger design automation is highly demanded from robot industries to fulfill the requirements of the agile market. Nevertheless, literature lacks a promising approach to automate the design process of reliable fingers for industrial robots. Hence, this work proposes the generic optimized finger design (GOFD) method which automates the design process of single- and multi-function finger grippers. The proposed method includes an optimization algorithm to minimize the design process time. The method is utilized to generate fingers for several groups of objects. Results show that the GOFD method outperforms existing methods and is able to reduce the design time by an average of 16,600 s. While the proposed method substantially reduces the design process time of fingers, the quality of grasps is comparable to the traditional exhaustive search method. The grasp quality of GOFD deviates only 0.47% from the absolute best grasp known from the exhaustive search method in average. The designed fingers are lastly manufactured and experimentally verified.

This paper presents and discusses three novel and interconnected means and measures with the aim to better understand the situation for the freshman student during the first year of study. Ultimately, this contribution seeks answers to what fosters student persistence. Exploration of possible answers is conducted by utilizing faculty registries of curriculum composition and study records, for investigating how different categories of curriculum content impact the performance of the students. Furthermore, the use of questionnaires for measuring coping capabilities among the freshman students is also explored, and finally, techniques of gathering real-time behavioral and environmental data for further analyzing is also discussed.

In conclusion, the paper discusses different categories of data sources and presents a set of tools and measures that could be employed by program planners and curriculum designers when evaluating and comparing the content of different engineering curricula and the relation to the performance of the students.

This paper presents a data management and visualization tool that was developed in parallel with a Multidisciplinary Design Optimization (MDO) framework in order to enable a more effective use of the obtained results within the Product Development Process (PDP). To this date, the main problem is that the majority of MDO case studies conclude by suggesting a small number of optimal configurations, which do not really hold any meaningful value for the decision makers since they represent only a narrow area of the design space. In this light, the proposed tool aims to provide designers with new possibilities in respect to post-processing of large data sets, and subsequently, to allow the non-technical teams to be engaged and benefit from the use of MDO in the company practices. As an example, an Unmanned Aerial Vehicle (UAV) configurator developed by using the Graphical User Interface (GUI) of MATLAB is herein presented, and it is shown that a tool for handling the results can be the logical next step towards integrating MDO in the manufacturing industry. Overall, this work aims to demonstrate the benefits of the present visualization and management tool as a complementary addition to an existing optimization framework, and also to determine if this approach can be the right strategy towards improving the MDO method for an eventual use in the PDP of complex pro-ducts like UAVs.

In this paper, a method of designing for Additive Manufacturing (AM) is proposed, implemented, and evaluated in a case study. In the proposed method, Topological Optimization is combined with a Multidisciplinary Design Optimization (MDO) framework that handles multi-objective optimization. Both the weight and amount of support material needed during manufacturing are minimized. In the proposed method, the topological optimized structure is remodelled into a parametric CAD model. The CAD model is then combined with an FE-model that calculates the stresses in the material and a model that calculates the amount of support structure needed. Two different optimization formulations are evaluated and compared in the case study.

In the case study an upright of a Formula Student racing car is designed. Several design evaluations are performed resulting in a set of Pareto optimal designs that could be used for decision-making where the trade-off between the two objectives is considered. It is concluded that the proposed method fulfils its purpose by being able to identify designs that would be difficult to come up with manually. Several suggestions for further studies in order to improve the method are also discussed.

The aim of this paper is to present the most common practices in multidisciplinary design optimization (MDO) of aerial vehicles over the past decade. The literature sample is identified through established internet search engines, and a stringent review methodology is implemented in order to ensure the selection of the most relevant sources. In this work, the primary emphasis is on the assessment of the state-of-the-art framework development strategies, while at a secondary level, the objective is to identify the possible improvement directions by evaluating the research trends and gaps. As an additional contribution, statistical studies are also provided, and it is shown how MDO of aerial vehicles has evolved in terms of problem formulation, disciplinary modeling, analysis capabilities, tool implementation, and general applicability. Given this foundation as well as the results of the review, this work concludes by presenting a roadmap for guiding academia and industry in respect to the application of MDO on aerial vehicles. Overall, the roadmap together with the literature review is not only expected to serve as a guide for newcomers into the MDO field but also as an elementary basis which will allow researchers to conduct additional studies in this important and constantly evolving area of design.

This paper presents a multidisciplinary design optimization framework applied to the development of unmanned aerial vehicles with a focus on radar signature and sensor performance requirements while simultaneously considering the flight trajectory. The primary emphasis herein is on the integration and development of analysis models for the calculation of the radar cross section and sensor detection probability, whereas traditional aeronautical disciplines such as aerodynamics and mission simulation are also taken into account in order to ensure a flyable concept. Furthermore, this work explores the effect of implementing trajectory constraints as a supplementary input to the multidisciplinary design optimization process and presents a method that enables the optimization of the aircraft under a three-dimensional flight scenario. To cope with the additional computational cost of the high-fidelity radar cross section and sensor calculations, the use of metamodels is also investigated and an efficient development methodology that can provide high-accuracy approximations for this particular problem is proposed. Overall, the operation and performance of the framework are evaluated against five surveillance scenarios, and the obtained results show that the implementation of trajectory constraints in the optimization has the potential to yield better designs by 12–25% when compared to the more “traditional” problem formulations.

This paper explores the use of Multidisciplinary Design Optimization (MDO) in the development of Unmanned Aerial Vehicles (UAVs) when the requirements include a collaboration in a System of Systems (SoS) environment. In this work, the framework considers models that can capture the mission, stealth, and surveillance performance of each aircraft, while at the same time, a dedicated simulation module assesses the total cooperation effect on a given operational scenario. The resulting mixed continuous and integer variable problem is decomposed with a multi-level architecture, and in particular, it is treated as a fleet allocation problem that includes a nested optimization routine for sizing a “yet-to-be-designed” aircraft. Overall, the models and the framework are evaluated through a series of optimization runs, and the obtained Pareto front is compared with the results from a traditional aircraft mission planning method in order to illustrate the benefits of this SoS approach in the design of UAVs.

In early design phases, it is vital to be able to screen the design space for a set of promising design alternatives for further study. This article presents a method able to balance several objectives of different mathematical natures, with high impact on the design choices. The method (MOSART) handles multi-objective optimization for safety and reliability trade-offs. The article focuses on optimization problem approach and processing of results as a base for decision-making. The output of the optimization step is the selection of specific system elements obtaining the best balance between the targets. However, what is a good base for decision can easily transform into too much information and overloading of the decision-maker. To solve this potential issue, from a set of Pareto optimal solutions, a smaller sub-set of selected solutions are visualized and filtered out using preference levels of the objectives, yielding a solid base for decision-making and valuable information on potential solutions. Trends were observed regarding each system element and discussed while processing the results of the analysis, supporting the decision of one final best solution.

Risk assessment is a systematic and iterative process, which involves risk analysis, where probable hazards are identified, and then corresponding risks are evaluated along with solutions to mitigate the effect of these risks. In this article, the outcome of a risk assessment process will be detailed, where a large industrial robot is used as an intelligent and flexible lifting tool that can aid operators in assembly tasks. The realization of a collaborative assembly station has several benefits, such as increased productivity and improved ergonomic work environment. The article will detail the design of the layout of a collaborative assembly workstation, which takes into account the safety and productivity concerns of automotive assembly plants. The hazards associated with hand-guided collaborative operations will also be presented.

Multidisciplinary design optimization has become a powerful technique to facilitate continuous improvement of complex and multidisciplinary products. Parametric modeling is an essential part with tremendous impact on the flexibility and robustness of multidisciplinary design optimization. This article investigates the effect of relational and non-relational parameterization techniques on the robustness and flexibility of the conceptual design of a multidisciplinary product. Bench marking between relational and non-relational parameterization and their effect on flexibility and robustness indicate that the relational parameterization is an efficient method in the multidisciplinary design optimization process. The inherent properties of the method contribute to an efficient parametric modeling with improved communication between different disciplines. This enhances the performance of the multidisciplinary design optimization process and allows a more flexible and robust design. The considered disciplines are computer-aided design, computational fluid dynamics, finite element analysis, and dynamic simulation. A high-fidelity geometry created in a computer-aided design environment is computer-aided design centric approach and later used in computational fluid dynamics, finite element analysis for a better understanding of the product as it leads to precise outcomes. The proposed approach is implemented for the conceptual design of a novel product, a tidal power plant developed by Minesto AB using a multidisciplinary design optimization process.

The aim of this paper is to show how a method able of trade-offs such as system safety, reliability, weight and cost can be practically implemented in industry (SAAB Aeronautics). The scope is to facilitate the decision-making on the optimal design in early design phases. The method consists of several steps guiding the user to model each objective, verify and validate the models, perform optimization and finally visualize and select the results. Within the practical implementation of this method, several challenges are addressed and solved. For example, one challenge is to implement the trade-off method using the existing programs. Another challenge is the user friendliness of the implementation. In order to solve these challenges, the analysis is started and performed in Matlab. A Graphical User Interface guides the user to select the analysis to perform, budgets/requirements for each objective and parameters with influence on end-result. Data regarding the safety and reliability objectives, exported from Reliability Workbench program to Excel, is imported to Matlab, where the analysis is performed. The results are extracted into an Excel file, where the user can work further on visualization and selection. Two small examples are used to demonstrate this practical implementation of the trade-off method. Lessons learned are presented, strengths, limitations and development potential.

This paper presents a Multidisciplinary Design Optimization (MDO) framework that is intended to be employed in the early design stages of Unmanned Aerial Vehicles (UAVs) when the primary focus is on the tradeoffs between the mission, stealth, and surveillance performance requirements. The proposed MDO framework takes into account the aircraft’s geometry, the aerodynamics, the trim, the stability, and the simulation of the mission, but it also includes two additional models for computing the Radar Cross Section (RCS) and the sensor performance. A multi-level solution architecture is implemented in order to tackle the increased complexity of the problem, and it is shown that this type of decomposition can be a more efficient optimization approach compared to the traditional single-level formulation. The operation of the framework is evaluated through single objective optimizations by using the weighted sum method, while it is also investigated whether or not metamodels can be a viable alternative to the computationally expensive RCS and sensor analysis models. Overall, the results show that the mission, stealth, and surveillance performance are conflicting objectives, and therefore, their concurrent consideration in an optimization framework can help increase the available knowledge early on in the design of UAV applications.

Design automation of industrial grippers is a hot research topic for robot industries. However, literature lacks a standard experimental method to enable researchers to validate their approaches. Thus, this paper proposes a generic experimental method to verify existing finger design approaches. The introduced method is utilized to validate the methods Generic Automated Finger Design (GAFD), Manually Designed Fingers (MDF) and the eGrip tool. Experimental results are compared and the strengths and weaknesses of each method are presented. (C) 2017 The Authors. Published by Elsevier B.V.

Designing robust end-effector plays a crucial role, in performance of a robot workcell. Design automation of industrial grippers fingers/jaws is therefore of the highest interest in the robot industry. This paper systematically reviews the enormous studies performed in relevant research areas for finger design automation. Key processes for successfully achieving automatic finger design are identified and research contributions in each key process are critically reviewed. The proposed approaches in each key process are analyzed, verified and benchmarked. The most promising methods to accomplish finger design automation are highlighted and presented. (C) 2016 Elsevier B.V. All rights reserved.

This paper proposes a method to compare the performances of different methods for robust design optimization of computationally demanding models. Its intended usage is to help the engineer to choose the optimization approach when faced with a robust optimization problem. This paper demonstrates the usage of the method to find the most appropriate robust design optimization method to solve an engineering problem. Five robust design optimization methods, including a novel method, are compared in the demonstration of the comparison method. Four of the five compared methods involve surrogate models to reduce the computational cost of performing robust design optimization. The five methods are used to optimize several mathematical functions that should be similar to the engineering problem. The methods are then used to optimize the engineering problem to confirm that the most suitable optimization method was identified. The performance metrics used are the mean value and standard deviation of the robust optimum as well as an index that combines the required number of simulations of the original model with the accuracy of the obtained solution. These measures represent the accuracy, robustness, and efficiency of the compared methods. The results of the comparison show that sequential robust optimization is the method with the best balance between accuracy and number of function evaluations. This is confirmed by the optimizations of the engineering problem. The comparison also shows that the novel method is better than its predecessor is.

In order to help decision-makers in the early design phase to improve and make more cost-efficient system safety and reliability baselines of aircraft design concepts, a method (Multi-objective Optimization for Safety and Reliability Trade-off) that is able to handle trade-offs such as system safety, system reliability, and other characteristics, for instance weight and cost, is used. Multi-objective Optimization for Safety and Reliability Trade-off has been developed and implemented at SAAB Aeronautics. The aim of this paper is to demonstrate how the implemented method might work to aid the selection of optimal design alternatives. The method is a three-step method: step 1 involves the modelling of each considered target, step 2 is optimization, and step 3 is the visualization and selection of results (results processing). The analysis is performed within Architecture Design and Preliminary Design steps, according to the company’s Product Development Process. The lessons learned regarding the use of the implemented trade-off method in the three cases are presented. The results are a handful of solutions, a basis to aid in the selection of a design alternative. While the implementation of the trade-off method is performed for companies, there is nothing to prevent adapting this method, with minimal modifications, for use in other industrial applications.

There are limited experimental data to characterize the mechanical response of human myometrium. A method is presented in this work to identify mechanical parameters describing the active response of human myometrium from the in vivo intrauterine pressure measurements. A finite element model is developed to compute the intrauterine pressure during labor in response to an increase in the intracellular calcium ion concentration within myometrial smooth muscle cells. The finite element model provides the opportunity to tune mechanical parameters in order to fit the computed intrauterine pressure to in vivo measurements. Since the model is computationally expensive, a cheaper meta-model is generated to approximate the model response. By fitting the meta-model response to the in vivo measurements, the parameters used to determine the active response of human myometrial smooth muscle are identified.

The work presented in this paper explores several concepts related to the design of complex engineering products and emphasizes on the effects of considering Multidisciplinary Design Optimization (MDO) in the development process. This paper is by no means a comprehensive literature review, but instead, the aim is to discuss some key points through theory and references to common MDO applications. In this respect, the central topics which are addressed herein are the enhancement of the generic product development process, the road towards a better integration of the organization’s functions, the methods to manage complex system architectures, and finally, the shortcomings of the MDO field. As a link to more tangible industrial applications, Unmanned Aerial Vehicles (UAVs) are chosen as an illustrative example due to their technical complexity as well as the demanding requirements of the corresponding market. Overall, the paper shows that despite the current state-of-the-art limitations, MDO can be a valuable tool within the “traditional” design process that has the potential to enable products of better quality while simultaneously reducing the total development time and effort.

An efficient methodology for verification, validation, and credibility assessment of simulation models and simulator applications is an enabler for the aeronautical industrys increasing reliance on modeling and simulation in system design and verification and on training. As a complement to traditional document-centric approaches, this paper presents a method for credibility assessment of simulator applications, in which credibility information is presented to end users directly during simulation. The central idea is that each model in a simulator is extended with a metamodel describing different aspects of credibility. The metamodel includes a number of static credibility measures and a dynamic measure that may vary during simulation. The concept is implemented and tested in two system simulators for the Saab Gripen fighter aircraft. According to the evaluation, the concept facilitates an intuitive overview of model dependencies, as well as credibility information for individual models and for a simulator as a whole. This implies a support for detecting test plan deficiencies or that a simulator configuration is not a suitable platform for the execution of a particular test. Furthermore, model developers and end users are encouraged to reflect upon central credibility aspects like intended use, model fidelity, and test worthiness in their daily work.

Finger design automation for grippers is one of the areas of highest interest for robot industries. The few studies that have been carried out in the finger design automation research area are limited to objects with specific geometrical properties (e.g. polyhedral). This paper introduces the Generic Automated Finger Design (GAFD) method that contains the essential key processes for automatic design of reliable fingers. The proposed method is implemented on two geometrically complex workpieces and appropriate fingers are designed. The results are discussed in detail and benchmarked against existing approaches.

Multi-function fingers that are able to handle multiple workpieces are crucial in improvement of a robot workcell. Design automation of multi-function fingers is highly demanded by robot industries to overcome the current iterative, time consuming and complex manual design process. However, the existing approaches for the multi-function finger design automation are unable to entirely meet the robot industries need. This paper proposes a generic approach for design automation of multi-function fingers. The proposed approach completely automates the design process and requires no expert skill. In addition, this approach executes the design process much faster than the current manual process. To validate the approach, multi-function fingers are successfully designed for two case studies. Further, the results are discussed and benchmarked with existing approaches.

This study investigates the performance of the collaborative multidisciplinary design optimization framework and how it facilitates the knowledge integration process. The framework is used to design and optimize an innovative concept of a tidal water power plant. The case study helps to highlight the challenges that may occur during implementation. The result is presented as a modified framework with less implementation difficulties. The improved framework shows significant reduction in design time and improvement in collaborative design optimization for a design team. The geometry of the product is optimized to minimize weight and maximize the power generated by the turbine with respect to some mechanical constraints.

Uncertainty Quantification (UQ) is vital to ensure credibility in simulation results and to justify model-based design decisions – especially in early development phases when system level measurement data for traditional model validation purposes are scarce. Central UQ challenges in industrial applications are computational cost and availability of information and resources for uncertainty characterization. In an attempt to meet these challenges, this paper proposes a framework for early and approximate UQ intended for large simulation models of dynamical systems. A Modelica simulation model of an aircraft environmental control system including a liquid cooling circuit is used to evaluate the industrial applicability of the proposed framework.

The paper presents a framework that can be used to design and optimize a balancing mechanism for an industrial robot. The framework has the capability to optimize three different concepts - a mechanical, a pneumatic and a hydro-pneumatic. Several disciplines are included in the framework, such as dynamic and static analyses of the robot performance. Optimization is performed for each concept and the obtained optimal designs are all better then the reference design. This means that the framework can be used both as a tool to optimize the balancing mechanism and also to support concept selection.

In a product development process, it is crucial to understand and evaluate multiple and synergic aspects of systems such as performance, cost, reliability, and safety. These aspects are mainly considered during later stages of the design process. However, in order to improve the foundations for decision-making, this article presents methods that are intended to increase the engineering knowledge in the early design phases. In complex products, different systems from a multitude of engineering disciplines have to work tightly together. Collaborative design is described as a process where a product is designed through the collective and joint efforts of domain experts. A collaborative multidisciplinary design optimization process is therefore proposed in the conceptual design phase in order to increase the likelihood of more accurate decisions being taken early on. The performance of the presented framework is demonstrated in an industrial application to design aircraft systems in the conceptual phase.

This paper presents and compares different modifications made to the Complex-RF optimization algorithm with the aim of improving its performance for computationally expensive models. The modifications reduces the required number of objective function evaluations by creating and using surrogate models of the objective function iteratively during the optimization process. The chosen surrogate model type is a second order response surface. The performance of the modified algorithm is compared with a number of existing algorithms and demonstrated for a few analytical and engineering problems.

To better utilize the potential of system simulation models and simulators, industrially applicable methods for Verification, Validation and Uncertainty Quantification(VV&UQ) are crucial. This paper presents an exploratory case study of VV&UQ techniquesapplied on models integrated in aircraft system simulators at Saab Aeronauticsand in driving simulators at the Swedish National Road and Transport Research Institute(VTI). Results show that a large number of Verification and Validation (V&V)techniques are applied, some of which are promising for further development and use insimulator credibility assessment. Regarding the application of UQ, a large gap betweenacademia and this part of industry has been identified, and simplified methods areneeded. The applicability of the NASA Credibility Assessment Scale (CAS) at the studied organizations is also evaluated and it can be concluded that the CAS is consideredto be a usable tool for achieving a uniform level of V&V for all models included in asimulator, although its implementation at the studied organizations requires tailoringand coordination.

This article presents an optimization framework that is used to optimize a hydro-pneumatic balancing cylinder for industrial robots. A balancing cylinder is a device that is used to balance the gravitational torque of one of the main axes of a high-loaded serial industrial robot. The design of components for an industrial robot is multi-disciplinary, since disciplines such as multibody dynamics, drive train design and robot control are needed. The design process is also multi-objective since the functionality of the balancing cylinder should be optimal, while its size and cost should be minimal. The article therefore also contains a discussion about multi-disciplinary and multi-objective optimization of complex products.

Even though the realism of driving simulators increases constantly, there is a potential issue with how representative the test is compared to a real life scenario. An alternative to simulators is to present a mixture of real and simulated environment to the driver and perform the scenario at a test track when driving a real vehicle. This enables an efficient way of testing that inherits many of the advantages of driving simulators as well as some of the advantages of physical testing in prototype vehicles. The present paper is a compilation of previous research in augmented reality in vehicle driving situations, focusing on technical limitations of Head-Mounted-Displays.

The main objective of a reliability study should always be to provide information as a basis for decisions, e.g. concept choice, design requirements, investment, choice of suppliers, design changes or guaranty claims. The choice of reliability method depends on the time allocated for the reliability study, the design stage, the problem at hand and the competence and resources available.

During a reliability study the engineer focuses on providing a graphical means of evaluating the relationships between different parts of the system, gathering or assessing the reliability data for the components and interpreting the results of the analyses. Even though the commercial software tools available claim to provide answers to most reliability questions, choosing which method is best suited is not an easy task. Often several methods can be applied and none of them will fit the purpose perfectly.

This paper presents a guideline for choosing the best suited reliability method in early design phases from two aspects: objective and system characteristics. The methods studied are the most common methods available in commercial software tools: Reliability Block Diagram (RBD), Fault Tree (FT), Event Tree (ET), Markov Analysis (MA) and Stochastic Petri Network (SPN). The guideline considers two aspects: the characteristics of the system studied and the scope of the analysis. The applicability of each of the five chosen methods is assessed for all possible combinations of system characteristics and objective. A study has been made at Saab Aeronautics to evaluate the practical use of the analysed methods and how this guideline can improve the selection of appropriate reliability methods in early design phases.

This paper compares different approaches for using kriging metamodels for robust design optimization, with the aim of improving the knowledge of the performance of the approaches. A popular approach is to first fit a metamodel to the original model and then perform the robust design optimization on the metamodel. However, it is also possible to create metamodels during the optimization. Additionally, the metamodel need not necessarily reanimate the original model; it may also model the mean value, variance or the actual objective function. The comparisons are made with two analytical functions and a dynamic simulation model of an aircraft system as an engineering application. In the comparisons, it is seen that creating a global metamodel before the optimization begins slightly outperforms the other approaches that involve metamodels.

When developing a safety critical system, there are many aspects that need to be balanced against each other in order to reach an optimal design such as safety requirements, reliability goal, performance specifications and budget constraints. In an early design stage, it is vital to be able to screen the design space for a set of promising design alternatives for further studies. This paper proposes an approach capable of investigating the trade-offs described above, combining the techniques for system safety and reliability analysis with optimization methods. Markov analysis is employed for modeling the system safety and reliability characteristics and a Genetic Algorithm is used for optimization. The proposed method is applied to the design of an electric supply system for an aircraft, involving selection of components from different suppliers. First a model is built for each objective, i.e. cost, safety, and reliability. The models are validated and optimization is performed. The obtained result is the selection of suppliers for each component in the system in order to achieve a balance between system safety, reliability, and other design objectives.

Moving towards renewable sources of energy has become one of the most important energy-related strategies in recent decades. High-altitude wind power (HAWP) has been discovered in 1833 as a source of useful energy. Wind power density (Watts/m²)  can significantly increase (~6 times) by going from 80 to 500 m altirude The global capacity of 380 TW (terawau) as well as abundance, strength, and relative persistency of wind in higher altitude are eye-catch ing poinls to consider HAWP as a reliable energy source in the future. A research project called "THOR" has been initiated at Linköping University by a group of master students (soon to graduate) as proof of concept of airborne wind energy (AWES). THOR is about feasibility analysis of different concepts of HAWP and proof of concept of balloon based AWES as one of the appropriate existing concepts. THOR is intended to be a research platform at Linköping University for further development of AWES concepts i n future.

A common viewpoint in both academia and industry is that that Verification, Validation and Uncertainty Quantification (VV&UQ) of simulation models are vital activities for a successful deployment of model-based system engineering. In the literature, there is no lack of advice regarding methods for VV&UQ. However, for industrial applications available methods for Uncertainty Quantification (UQ) often seem too detailed or tedious to even try. The consequence is that no UQ is performed, resulting in simulation models not being used to their full potential.

In this paper, the effort required for UQ of a detailed aircraft vehicle system model is estimated. A number of methodological steps that aim to achieve a more feasible UQ are proposed. The paper is focused on 1‑D dynamic simulation models of physical systems with or without control software, typically described by Ordinary Differential Equations (ODEs) or Differential Algebraic Equations (DAEs). An application example of an aircraft vehicle system model is used for method evaluation.

Context: "Reuse" and "Model Based Development" are two prominent trends for improving industrial development efficiency. Product lines are used to reduce the time to create product variants by reusing components. The model based approach provides the opportunity to enhance knowledge capture for a system in the early stages in order to be reused throughout its lifecycle. This paper describes how these two trends are combined to support development and support of a simulator product line for the SAAB 39 Gripen fighter aircraft.

Objective: The work aims at improving the support (in terms of efficiency and quality) when creating simulation model configurations. The objective is to increase the level of reuse when combining and customizing models for usage in a range of development and training simulators.

Method: The research has been conducted with an interactive approach using prototyping and demonstrations, and the evaluation is based on an iterative and a retrospective method.

Results: A product line of simulator models for the SAAB 39 Gripen aircraft has been analyzed and defined in a Product Variant Master. A configurator system has been implemented for creation, integration, and customization of stringent simulator model configurations. The system is currently under incorporation in the standard development process at SAAB Aeronautics.

Conclusion: The explicit and visual description of products and their variability through a configurator system enables better insights and a common understanding so that collaboration on possible product configurations improves and the potential of software reuse increases. The combination of application fields imposes constraints on how traditional tools and methods may be utilized. Solutions for Design Automation and Knowledge Based Engineering are available, but their application has limitations for Software Product Line engineering and the reuse of simulation models.

This contribution discusses aspects and benefits from involving physical representations when teaching engineering design and Computer Aided Engineering at Linköping University, Sweden.

The paper presents a syllabus for a comprehensive introductory CAD course. The course is populated by some 300 students on the Mechanical Engineering Master’s and Bachelor’s programs, as well as the Design and Product Development Master’s program. Assessment is made via a project where the students are assigned to model and optimize a small catapult. The catapult is then produced, using cheap materials, by the hands of the students who modeled it. Finally, the catapult is validated by entering a contest, where it is judged in respect of accuracy, weight, and cost. The catapult assignment is constructed in such a way that the students are forced to seek individual ways of applying their newly acquired knowledge of the CAD tool. Some 100 catapults are produced but the material cost for each catapult is only about €4.

The low-cost nature of the catapults originates from research conducted at the Division of Machine Design at Linköping University, where the concept of Low-cost Demonstrators for enhancement of the conceptual design phase has been developed over the past decade. The results from this research point towards several benefits from using physical representations alongside the common digital tools during the early stages of the product development process. Furthermore, evaluation of parameters such as the students’ performance and their own opinions of the course show notable enhancement compared to previous courses.