This paper explores novel methodologies for enabling Multidisciplinary Design Optimization (MDO) of complex engineering products. To realize MDO, Knowledge Based Engineering (KBE) is adopted with the aim of achieving design reuse and automation. The aim of the on-going research at Linköping University is to shift from manual modelling of disposable geometries to Computer Aided Design (CAD) automation by introducing generic high-level geometry templates. Instead of repeatedly modelling similar instances of objects, engineers should be able to create more general models that can represent entire classes of objects. The proposed methodology enables utilization of commercial design tools, hence taking industrial feasibility into consideration. High Level CAD templates (HLCt) will be proposed and discussed as the building blocks of flexible and robust CAD models, which in turn enables high-fidelity geometry in the MDO loop. Quantification of the terms flexibility and robustness is also presented, providing a means to measure the quality of the geometry models. Finally, application examples are presented in which the outlined framework is evaluated. The applications have been chosen from three ongoing research projects aimed at automating the design of transport aircraft, industrial robots, and micro air vehicles.
In development and support of complex products such as power plants, automotive vehicles, or aircrafts, modeling and simulation has become an important activity as a basis for knowledge capture. Simulation is used in several steps of the product lifecycle; for evaluation of early design, for system verification, and for user training. With emerging techniques such as tools for high-level modeling, multi-core computing, and visualization, the number of useful models is growing. This paper focuses on reuse of multipurpose models and configuration support in a product line context. A configurator prototype system is presented. The simulator set created from validated models is considered to be a secondary product line. The product set which the simulation models represent is considered to be the primary product line. The Saab Gripen fighter aircraft, together with simulators in which the aircraft behavior, performance, and handling qualities are represented, is used to exemplify application. Integration principles of the systems for simulator configuration, Software Configuration Management, and Product Data Management (PDM) are studied. Preliminary results show that a configurator tool can be used, but there is need to map structures between the simulation and PDM domains.
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.
In the planning and concept study phases of the next generation Gripen fighter aircraft, methods and tools studies have been performed. Capabilities and limitations of the Simulink toolset have been evaluated to explore how it can support model based systems/software engineering. In this paper, three different approaches of Simulink usage for functional development are presented:
The driver for choosing approach is threefold; high quality, short development time and low cost. Some experiences based on these prerequisites are presented, mainly concerning the aspects of scalability, such as; model architecture, license model and project ramp-up challenges. The results are also compared to the existing SystemBuild based development environment. When introducing high-end engineering practices and tools such as Simulink in an organization developing safety-critical products, it is important to make sure that also basic management practices (e.g. Requirements-, Configuration- and Change Management) are thoroughly handled.
This paper describes a workflow for designing experiences whileinteracting with an advanced driver assistant system. Future driver assistancesystems that utilize sensors and Car2X-communication in order to detect threatsin the car environment can help the driver to avoid collisions. To increase theacceptance of such a system, the interaction between the driver and the systemshould be able to generate positive experiences. To generate those experiences,a story-based design workflow was used. Concepts created with this workflowshould be able to address specific psychological needs of the driver. Theimplementation of this workflow revealed different schemes of positiveexperiences during driver interaction in critical situations.
In this paper, an approach for modular design of industrial robots is presented. The approach is to introduce an objectoriented simulation model of the robot and combine this with a discrete optimization algorithm. The simulation model of the industrial robot is developed in Modelica, an object oriented modeling and simulation language, and simulated in the Dymola tool. The optimization algorithm used is a modification of the Complex method that has been developed in Matlab and connected to the simulation program. The optimization problem includes selecting components such as gearboxes and motors from a component catalogue and the objective function considers minimization of cost with constraints on gear box lifetime. Furthermore, the correctness of the model has been verified by comparison with an in-house simulation code with high accuracy.
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.
This paper describes a framework for development and validation of multipurpose simulation models. The presented methodology enables reuse of models in different applications with different purposes. The scope is simulation models representing physical environment, physical aircraft systems or subsystems, avionics equipment, and electronic hardware. The methodology has been developed by a small interdisciplinary team, with experience from Modeling and Simulation (M&S) of vehicle systems as well as development of simulators for verification and training. Special care has been taken to ensure usability of the workflow and method descriptions, mainly by means of 1) a user friendly format, easy to overview and update, 2) keeping the amount of text down, and 3) providing relevant examples, templates, and checklists. A simulation model of the Environmental Control System (ECS) of a military fighter aircraft, the Saab Gripen, is used as an example to guide the reader through the workflow of developing and validating multipurpose simulation models. The methods described in the paper can be used in both military and civil applications, and are not limited to the aircraft industry.
To support early model validation, this paper describes a method utilizing information obtained from the common practice component level validation to assess uncertainties on model top level. Initiated in previous research, a generic output uncertainty description component, intended for power-port based simulation models of physical systems, has been implemented in Modelica. A set of model components has been extended with the generic output uncertainty description, and the concept of using component level output uncertainty to assess model top level uncertainty has been applied on a simulation model of a radar liquid cooling system. The focus of this paper is on investigating the applicability of combining the output uncertainty method with probabilistic techniques, not only to provide upper and lower bounds on model uncertaintiesbut also to accompany the uncertainties with estimated probabilities.It is shown that the method may result in a significant improvement in the conditions for conducting an assessment of model uncertainties. The primary use of the method, in combination with either deterministic or probabilistic techniques, is in the early development phases when system level measurement data are scarce. The method may also be used to point out which model components contribute most to the uncertainty on model top level. Such information can be used to concentrate physical testing activities to areas where it is needed most. In this context, the method supports the concept of Virtual Testing.
This paper proposes a pragmatic approach enabling early model validation activities with a limited availability of system level measurement data. The method utilizes information obtained from the common practice of component validation to assess uncertainties on model top level. Focusing on industrial applicability, the method makes use of information normally available to engineers developing simulation models of existing or not yet existing systems. This is in contrast to the traditional sensitivity analysis requiring the user to quantify component parameter uncertainties – a task which, according to the authors’ experience, may be far from intuitive. As the proposed method enables uncertainties to be defined for a component’s outputs (characteristics) rather than its inputs (parameters), it is hereby termed output uncertainty. The method is primarily intended for use in large-scale mathematical 1-D dynamic simulation models of physical systems with or without control software, typically described by Ordinary Differential Equations (ODE) or Differential Algebraic Equations (DAE).It is shown that the method may result in a significant reduction in the number of uncertain parameters that require consideration in a simulation model. The uncertainty quantification of these parameters also becomes more intuitive. Since this implies a substantial improvement in the conditions of conducting sensitivity analysis or optimization on large-scale simulation models, the method facilitates early model validation. In contrast to sensitivity analysis with respect to a model’s original component parameters, which only covers one aspect of model uncertainty, the output uncertainty method enables assessment also of other kinds of uncertainties, such as uncertainties in underlying equations or uncertainties due to model simplifications. To increase the relevance of the method, a simulation model of a radar liquid cooling system is used as an industrial application example.
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.
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.
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.
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.
Structure borne noise in a machine rises from piston force and bending moments among others. This noise arises directly from the pump shell. In this study, a transfer function methodology is employed for mapping simulated internal pump dynamics, such as piston forces and bending moments, on to structure borne noise. Using these transfer functions, it is possible to predict how, for instance, changed valve plate timing affects simulated piston forces and bending moments and in turn how that will affect audible noise. Hence, it is possible to design an objective function that directly reflects audible noise. The transfer functions are experimentally obtained and are valid for a specific machine shell and to some minor extent the room’s acoustical properties. Also, fluid borne noise is important to consider when designing a quiet machine. Fluid borne noise arises mainly from flow pulsation created inside the machine.
Simulation of the internal pump dynamics, and optimisations, are carried out using a pump model developed in the simulation tool HOPSAN. The design application is a hydraulic machine of bent axis type with seven pistons. The theory outlined and the method proposed in the paper can also be applied to other types of hydraulic machines. The paper shows how both structure borne noise and fluid borne noise can be considered using multi-objective optimisation. The paper shows how different noise reduction features affect the sound pressure level and the flow pulsation. The paper also compare the pump and motor case.
Den här presentationen behandlar en metod som effektivt reducerar flödespulsationer i variabla hydraulmaskiner, genom att förskjuta dödpunktens läge. Det realiseras genom att införa en fast inklinationsvinkel vinkelrätt mot den normala deplacementsvinkeln. Genom att förskjutningsvinkeln ändrar kolvarnas dödpunkt kommer förkompressionen och efterexpansionen att variera när deplacementet ändras. Tidigare arbeten visar, både teoretiskt och experimentellt, fördelarna med förskjutningsvinkeln för pumpar men inga utförligare utredningar för maskiner som arbetar både som motor och pump.
This paper considers using the cross-angle in variable displacement hydraulic machines. The cross-angle is a fixed displacement angle around the axis perpendicular to the normal displacement direction. The cross-angle changes the angles to the pistons top and bottom dead centres as a function of the fraction of displacement in such a way that the valve plate timing is varied and different pre-compression and decompression angles are obtained. A non-gradient optimisation technique, the Complex method, is used together with a comprehensive simulation model in order to find the optimal cross-angle for a variable displacement hydraulic motor. The paper shows that the cross-angle can be used to reduce noise in variable displacement motors. One issue that makes the motor application more difficult is the increased dependence between outlet and inlet flow ripple which is not found in pump applications. Furthermore, the paper discusses how to use the cross-angle for machines which can work both as a motor and a pump.
Noise is a well known challenge for hydraulic systems and hydrostatic machines is one of the largest noise contributors in a hydraulic system. The noise from the machine originates from flow pulsations in the discharge and suction ports, as well as pulsations in piston forces and bending moments. To the design a quite hydraulic machine is a difficult task where many different objectives need to be considered. This paper presents a generic method for how optimization based on simulation models could be used to design quieter hydraulic machines. In order to stay competitive on a global market an efficient product development process is essential for all manufacturing industries. By using simulation-s tools in the design process, the product can be analysed before the actual product is manufactured. Furthermore, in order to find an optimal design of the machine with respect to noise, a comprehensive dynamic simulation model is needed. The model contains all important noise contributors. In the paper, the simulation models are used together with a non-gradient optimization method in order to find the best possible design. A vital part when using optimization to support design is always to formulate the objective function. As mentioned above, noise is generated from different sources and all these sources need to be considered when the objective function is formulated. For example a design that minimizes flow pulsations in the suction port will surely perform badly in some other objective. Therefore noise minimization could be looked upon as a typical multi-objective optimization problem. It is also not evident how the different objective should be ranked because the observed noise level is strongly depending on the system in which the machine is to be used. The paper also considers whether the objective function should be formulated in time or frequency domain. Traditionally, simulation of machine performance is conducted in the time domain, but the human ear hears noise in the frequency domain and perceives high and low frequencies differently. Furthermore, transformation from piston forces into emitted noise is much higher at high-frequency content than low-frequency content. This makes it natural to formulate the objective function in frequency domain, which raises the question of how the different harmonic should be ranked. In the paper a number of different approaches to formulate the objective function is presented and evaluated. The objectives considered are flow pulsation in both discharge and suction ports, as well as pulsation in piston forces and bending moments. Furthermore, the objectives are studied in both time and frequency domain. The design application is a variable hydraulic machine of bent axis type with nine pistons, which is operated both as a pump and a motor. However, the methods presented in the paper could be applied to other types of hydraulic machines as well.
In this study, a transfer function methodology is employed for mapping simulated internal pump dynamics, such as piston forces and bending moments, on to audible noise. Using these transfer functions, it is possible to predict how, for instance, changed valve plate timing affects simulated piston forces and bending moments and in turn how that will affect audible noise. Hence, it is possible to design an objective function that directly reflects audible noise. The transfer functions are experimentally obtained and are valid for a specific machine shell and to some minor extent the room’s acoustical properties. Simulation of the internal pump dynamics, and optimisations, are carried out using a pump model developed in the simulation tool HOPSAN. The design application is a fixed hydraulic machine of bent axis type with seven pistons. The theory outlined and the method proposed in the paper can also be applied to other types of hydraulic machines. The paper shows how different noise reduction features affect the sound pressure level and also motor mode compared to pump mode.
In this paper, overall manipulability measure and stroke of workspace are proposed and evaluated as design criteria for optimal kinematics design of a family of industrial robots. The object of study is a 6 degree of freedom serial robot manipulator where individual family members (robots) share arms from a common platform. The paper presents a formal mathematical framework where the product family design problem is stated as an optimization problem and where optimization is used to find an optimal product family. The paper illustrates how the proposed kinematic design criteria may be used to support the optimal kinematics design of a family of industrial robots, and it also visualizes the tradeoff between the size of the common platform and the kinematics performance of individual robots. Copyright © 2009 by ASME.
Designing a drive train for an industrial robot is a demanding task where a set of design variables need to be determined so that optimal performance is obtained for a wide range of different duty cycles. The paper presents a method where singular value decomposition (SVD) is used to reduce the design variable set. The application is a six degree of freedom serial manipulator, with nine drive train parameters for each axis and the objective is to minimize the cycle time on 122 representative design cycles without decreasing the expected lifetime of the robot. The optimization is based on a simulation model of the robot and conducted on a reduced set of the initial duty cycles and with the design variables suggested by the SVD analysis. The obtained design reduces the cycle time with 1.6% on the original design cycles without decreasing the life time of the robot.
It has become a common practice to conduct simulation-based design of industrial robotic cells, where Mechatronic system model of an industrial robot is used to accurately predict robot performance characteristics like cycle time, critical component lifetime, and energy efficiency. However, current robot programming systems do not usually provide functionality for finding the optimal design of robotic cells. Robot cell designers therefore still face significant challenge to manually search in design space for achieving optimal robot cell design in consideration of productivity measured by the cycle time, lifetime, and energy efficiency. In addition, robot cell designers experience even more challenge to consider the trade-offs between cycle time and lifetime as well as cycle time and energy efficiency. In this work, utilization of multi-objective optimization to optimal design of the work cell of an industrial robot is investigated. Solution space and Pareto front are obtained and used to demonstrate the trade-offs between cycle-time and critical component lifetime as well as cycle-time and energy efficiency of an industrial robot. Two types of multi-objective optimization have been investigated and benchmarked using optimal design problem of robotic work cells: 1) single-objective optimization constructed using Weighted Compromise Programming (WCP) of multiple objectives and 2) Pareto front optimization using multi-objective generic algorithm (MOGA-II). Of the industrial robotics significance, a combined design optimization problem is investigated, where design space consisting of design variables defining robot task placement and robot drive-train are simultaneously searched. Optimization efficiency and interesting trade-offs have been explored and successful results demonstrated.
There is an ongoing trend in the European Military a/c industry towards cooperation between nations when purchasing and between manufacturers when developing and producing a/c. Different manufacturers at different locations develop different parts or sub-systems. When using this approach a vital part of a fast and precise system evaluation is the use of simulation models. In order to stay competitive it is not only sufficient to be able to build large simulation models but also to do it fast.
This paper describes the conclusions regarding a modelling strategy of large fluid systems drawn from the building of a simulation model of the JAS 39 Gripen fuel system. An overall process is suggested into which the activities of building a model are fitted. This is however not the main objective; it is more important to identify the different issues and activities at the engineering level. If these are properly dealt with, the model development time will be reduced, if not, the wrong model may be designed. "Wrong" here means a model that does not do the job, or solves a problem other than the one intended by the stakeholder.
This paper describes early considerations that have to be made when designing an aircraft fuel system. Emphasis is placed on illustrating the impact of top-level aircraft requirements on low-level practicalities such as fuel system design. Choosing between concepts is one of the most critical parts of any design process. Different concepts have different advantages, and the concept that is the best choice is often dependent on the top-level requirements. This paper shows how optimization has been used successfully at Saab Aerospace as a tool that supports concept selection. The example studied is the design of a fuel transfer system for a ventral drop tank and the optimization results in different conceptual designs depending on the top-level requirements.
This paper describes how the House of Quality matrix has been quantified for use in conceptual design. The House of Quality matrix is used for visualizing the relationships between subsystem design parameters and top-level requirements. The idea is then to insert quantified values of the subsystems’ characteristics as coupling elements, thus visualizing both the requirements-subsystems relationship and system performance. Here, a spreadsheet program (MS Excel) with a built-in modeling/solver tool has been used to model the subsystems. This makes the matrix interactive, thus facilitating trade studies between requirements and system design. By adding probabilistic analysis it is possible to explore the entire range of system behavior early on, rather than just focusing on one or more worst case scenarios as has previously often been the case, and thus promoting the selection of more optimal solutions. The quantitative approach also opens up for mathematically formal optimization which has been exploited by deriving Pareto fronts for visualization of conflicting objectives, one such objective being. The design application used as illustrative example is conceptual design of an aircraft fuel system.
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.
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.
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.
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.
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.
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.
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.
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.
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.
One of the most important drawbacks with hydraulic systems is noise and vibration, which mainly originate from the hydrostatic pump. A great number of noise-reducing design features have been developed, but they are all, to a greater or lesser extent, sensitive to variations in operational conditions. The present paper is concerned with optimal design and experimental verification of the cross-angle in an axial piston pump. The cross-angle is a small fixed incline of the swash plate in the direction that is perpendicular to the traditional displacement direction. It enables effective noise reduction throughout the whole range of displacement angles.
Simulation-based optimization is used to design a pump with optimal cross-angle and a matching valve plate. The design is manufactured and experimentally evaluated. Source flow measurements using the two-microphone method show good agreement between simulation and experiments, which verifies the applicability of the simulation model used. The benefits from using the cross-angle are then verified by comparing it with a pump with a traditional swash plate design, i.e. without the cross-angle. Both source flow measurements and sound level measurements in an anechoic chamber show good improvements from using the cross-angle.
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.
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.
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.
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.
One important challenge in the early phases of product development is to apply reliability methods for estimating the safety and reliability of the system when information about the chosen equipment and components is limited. For systems consisting of units with several degraded states, and not only “up” and “down”, the results from reliability and system safety analysis are often difficult to interpret and use. The main contribution of this paper is to evaluate the applicability of different reliability methods for analyzing an overall system concept in early development stages. Furthermore, the paper constitutes the first step of a methodology intended to address the issues outlined above from a practical point of view. In the paper, two static methods, Reliability Block Diagram and Fault Tree Analysis, and one dynamic method, Markov Analysis, have been applied to conceptual design of an aircraft electrical system. These three methods have been evaluated regarding usefulness, modeling possibilities and applicability in the conceptual design. Each method is, from a practical point of view, dependent on the limitations of the software that is used. In order to overcome this issue the calculations and partly the modeling have been performed in three different software tools.
Two iterations have been performed for Markov Analysis, and the results are used to evaluate the method regarding applicability and possibilities of modeling the system and to find out what results can be gained by extending the model.
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.
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.
Design optimization is becoming and increasingly important tool for design. In order to have an impact on the product development process it must permeate all levels of the design in such a way that a holistic view is maintained through all stages of the design. One important area is in the case of optimization based on simulation, which generally requires non-gradient methods and as a consequence direct-search methods is a natural choice. The idea in this paper is to use the design optimization approach in the optimization algorithm itself in order to produce an efficient and robust optimization algorithm. The result is a single performance index to measure the effectiveness of an optimization algorithm, and the COMPLEX-RF optimization algorithm, with optimized parameters.