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  • 1.
    Björn, Johansson
    et al.
    Linköping University, Department of Management and Engineering, Machine Design. Linköping University, The Institute of Technology.
    Ölvander, Johan
    Linköping University, The Institute of Technology. Linköping University, Department of Mechanical Engineering, Machine Design.
    Pettersson, Marcus
    Linköping University, The Institute of Technology. Linköping University, Department of Mechanical Engineering, Machine Design.
    Component Based Modelling And Optimization For Modular Robot Design2007In: ASME Design Automation Conferance,2007, Las Vegas: ASME , 2007, p. 911-920Conference paper (Refereed)
    Abstract [en]

    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.

  • 2.
    Lundén Johansson, Björn
    et al.
    Linköping University, Department of Management and Engineering, Machine Design . Linköping University, The Institute of Technology.
    Pettersson, Marcus
    Linköping University, Department of Management and Engineering, Machine Design . Linköping University, The Institute of Technology.
    Ölvander (Andersson), Johan
    Linköping University, Department of Management and Engineering, Machine Design . Linköping University, The Institute of Technology.
    A Component Based Optimization Approach for Robot Modular Design2007In: Proceedings of DETC'2007 ASME Design Automation Conference, September, Las Vegas, Nevada, USA, 2007Conference paper (Other academic)
  • 3.
    Pettersson, Marcus
    Linköping University, Department of Management and Engineering, Machine Design . Linköping University, The Institute of Technology.
    Design Optimization in Industrial Robotics: Methods and Algorithms for Drive Train Design2008Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Robot manufacturers, like many other manufacturers, are experiencing increasing competition in a global market where one way to confront the challenge is by making the development process more efficient. One way to speed up the time to market for new products is to take advantage of design optimization based on simulation models. By optimizing performance with the help of dynamic simulation, an immense amount of both time and money may be saved.

    In this thesis, design optimization strategies for industrial robot design are studied. Often, the trade-offs between performance, cost and quality are essential for design decisions. These tradeoffs can be investigated with the help of simulation models. Generating the trade-offs can be both cumbersome and time-consuming, but the process may be partly automated with the help of optimization algorithms. How the optimization problem needs to be formulated to generate the trade-off is discussed in this thesis.

    Robot design problems usually consist of a mixture of deciding continuous parameters as well as selecting components from catalogs and databases. Hence, there is a need for optimization algorithms which can handle variables of both a discrete and a continuous nature. A new method has been developed to address this problem. The method has also been improved by adding adaptive characteristics for further efficient design optimization.

    The ideas in this thesis have been applied to both simulation models of conceptual degrees of elaboration as well as simulation models of complete robot systems. An optimization procedure which shows how optimization can be used in the early phases of a development process is developed. The objective of the optimization is to determine optimal gearboxes and arm lengths from an acceleration capability perspective. An optimization based design method for robot drive trains is also presented. For further efficient use of already installed robots the concept of application adapted performance optimization is introduced. This means that the robot control is optimized with respect to thermal and fatigue load for the specific program that the robot performs. The motion program itself, i.e. the path planning, can be optimized at the same time in order to get the most out of the robot.

    List of papers
    1. Industrial Robot Design Optimization in the Conceptual Design Phase
    Open this publication in new window or tab >>Industrial Robot Design Optimization in the Conceptual Design Phase
    Show others...
    2004 (English)In: Proceedings of IEEE International Conference on Mechatronics and Robotics, Aachen , Germany, 2004Conference paper, Published paper (Other academic)
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-13228 (URN)
    Available from: 2008-04-28 Created: 2008-04-28 Last updated: 2009-05-29
    2. Methods for Discrete Design Optimization
    Open this publication in new window or tab >>Methods for Discrete Design Optimization
    2005 (English)In: Proceedings of ASME 31st Design Automation Conference, September 24-28, Long Beach, USA, 2005Conference paper, Published paper (Other academic)
    Abstract [en]

    One area in design optimization is component based design where the designer has to choose between many different discrete alternatives. These types of problems have discrete character and in order to admit optimization an interpolation between the alternatives is often performed. However, in this paper a modified version of the non-gradient algorithm the Complex method is developed where no interpolation between alternatives is needed. Furthermore, the optimization algorithm itself is optimized using a performance metric that measures the effectiveness of the algorithm. In this way the optimal performance of the proposed discrete Complex method has been identified. Another important area in design optimization is the case of optimization based on simulations. For such problems no gradient information is available, hence non-gradient methods are therefore a natural choice. The application for this paper is the design of an industrial robot where the system performance is evaluated using comprehensive simulation models. The objective is to maximize performance with constraints on lifetime and cost, and the design variables are discrete choices of gear boxes for the different axes.

    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-13229 (URN)
    Available from: 2008-04-28 Created: 2008-04-28 Last updated: 2009-05-29
    3. On Optimal Drive Train Design in Industrial Robots
    Open this publication in new window or tab >>On Optimal Drive Train Design in Industrial Robots
    2005 (English)In: Proceedings of IEEE International Conference on Industrial Technology, Hong Kong, December 2005, 2005, p. 254-259Conference paper, Published paper (Other academic)
    Abstract [en]

    In this paper optimization is used to determine which gearboxes to use in an industrial robot. The paper also presents a procedure where tradeoff information is generated based on consecutive optimizations. Thereby the method provides the designer with information about critical tradeoffs between conflicting objectives. This type of information is very valuable when negotiating between different design alternatives. Generating these tradeoffs is traditionally a time consuming process, but by introducing optimization this process can be partly automated. The design variables studied are composed of continuous and discrete parameters, where the latter are associated with different gearbox alternatives and the continuous variables with the speed-torque limitation of the gearboxes. A non-gradient based optimization algorithm which can handle mixed variables problems is used to solve the highly non-linear problem. The outcome from an industrial point of view is minimization of cost and simultaneously balance the trade-off between lifetime and performance.

    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-13230 (URN)10.1109/ICIT.2005.1600645 (DOI)
    Available from: 2008-04-28 Created: 2008-04-28 Last updated: 2009-05-29
    4. A Component Based Optimization Approach for Robot Modular Design
    Open this publication in new window or tab >>A Component Based Optimization Approach for Robot Modular Design
    2007 (English)In: Proceedings of DETC'2007 ASME Design Automation Conference, September, Las Vegas, Nevada, USA, 2007Conference paper, Published paper (Other academic)
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-13231 (URN)
    Available from: 2008-04-28 Created: 2008-04-28 Last updated: 2009-03-25
    5. Application Adapted Performance Optimization for Industrial Robots
    Open this publication in new window or tab >>Application Adapted Performance Optimization for Industrial Robots
    2007 (English)In: IEEE International Symposium on Industrial Electronics, 4-7 June 2007, Vigo, Spain, 2007, p. 2047-2052Conference paper, Published paper (Other academic)
    Abstract [en]

    Industrial robots are designed for a large spectrum of user scenarios. This implies that the robot cannot be tailor made for each situation and hence its full potential might not always be fully exploited. For further efficient use of robots the concept of application adapted performance optimization is introduced. This means that the robot control is optimized with respect to thermal and fatigue load for the specific program, which the robot performs. Simultaneously the motion program itself i.e. the path planning can be optimized in order to get the most out of the robot. These ideas are tested on a six axis robot in a press tending application.

    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-13232 (URN)10.1109/ISIE.2007.4374923 (DOI)
    Available from: 2008-04-28 Created: 2008-04-28 Last updated: 2009-03-16
    6. Adaptive Complex Method for Efficient Design Optimization
    Open this publication in new window or tab >>Adaptive Complex Method for Efficient Design Optimization
    2007 (English)In: Proceedings of ASME 33rd Design Automation Conference, September 4-7, Las Vegas, USA, 2007, p. 265-272Conference paper, Published paper (Other academic)
    Abstract [en]

    Box’s Complex method for direct search has shown promise when applied to simulation based optimization. In direct search methods, like Box’s Complex method, the search starts with a set of points, where each point is a solution to the optimization problem. In the Complex method the number of points must be at least one plus the number of variables. However, in order to avoid premature termination and increase the likelihood of finding the global optimum more points are often used at the expense of the required number of evaluations. The idea in this paper is to gradually remove points during the optimization in order to achieve an adaptive Complex method for more efficient design optimization. The proposed method shows encouraging results when compared to the Complex method with fix number of points and a quasi-Newton method.

    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-13233 (URN)10.1115/DETC2007-34773 (DOI)0-7918-3806-4 (ISBN)0-7918-4807-8 (ISBN)
    Conference
    ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
    Available from: 2008-04-28 Created: 2008-04-28 Last updated: 2014-08-21
    7. Drive Train Optimization for Industrial Robots
    Open this publication in new window or tab >>Drive Train Optimization for Industrial Robots
    2008 (English)In: IEEE Transactions on RoboticsArticle in journal (Refereed) Submitted
    Identifiers
    urn:nbn:se:liu:diva-13234 (URN)
    Available from: 2008-04-28 Created: 2008-04-28 Last updated: 2010-04-23
  • 4.
    Pettersson, Marcus
    et al.
    Linköping University, Department of Management and Engineering, Machine Design . Linköping University, The Institute of Technology.
    Andersson (Ölvander), Johan
    Linköping University, Department of Management and Engineering, Machine Design . Linköping University, The Institute of Technology.
    Krus, Petter
    Linköping University, Department of Management and Engineering, Machine Design . Linköping University, The Institute of Technology.
    Methods for Discrete Design Optimization2005In: Proceedings of ASME 31st Design Automation Conference, September 24-28, Long Beach, USA, 2005Conference paper (Other academic)
    Abstract [en]

    One area in design optimization is component based design where the designer has to choose between many different discrete alternatives. These types of problems have discrete character and in order to admit optimization an interpolation between the alternatives is often performed. However, in this paper a modified version of the non-gradient algorithm the Complex method is developed where no interpolation between alternatives is needed. Furthermore, the optimization algorithm itself is optimized using a performance metric that measures the effectiveness of the algorithm. In this way the optimal performance of the proposed discrete Complex method has been identified. Another important area in design optimization is the case of optimization based on simulations. For such problems no gradient information is available, hence non-gradient methods are therefore a natural choice. The application for this paper is the design of an industrial robot where the system performance is evaluated using comprehensive simulation models. The objective is to maximize performance with constraints on lifetime and cost, and the design variables are discrete choices of gear boxes for the different axes.

  • 5.
    Pettersson, Marcus
    et al.
    Linköping University, Department of Management and Engineering, Machine Design . Linköping University, The Institute of Technology.
    Andersson (Ölvander), Johan
    Linköping University, Department of Management and Engineering, Machine Design . Linköping University, The Institute of Technology.
    Krus, Petter
    Linköping University, Department of Management and Engineering, Machine Design . Linköping University, The Institute of Technology.
    On Optimal Drive Train Design in Industrial Robots2005In: Proceedings of IEEE International Conference on Industrial Technology, Hong Kong, December 2005, 2005, p. 254-259Conference paper (Other academic)
    Abstract [en]

    In this paper optimization is used to determine which gearboxes to use in an industrial robot. The paper also presents a procedure where tradeoff information is generated based on consecutive optimizations. Thereby the method provides the designer with information about critical tradeoffs between conflicting objectives. This type of information is very valuable when negotiating between different design alternatives. Generating these tradeoffs is traditionally a time consuming process, but by introducing optimization this process can be partly automated. The design variables studied are composed of continuous and discrete parameters, where the latter are associated with different gearbox alternatives and the continuous variables with the speed-torque limitation of the gearboxes. A non-gradient based optimization algorithm which can handle mixed variables problems is used to solve the highly non-linear problem. The outcome from an industrial point of view is minimization of cost and simultaneously balance the trade-off between lifetime and performance.

  • 6.
    Pettersson, Marcus
    et al.
    Linköping University, Department of Management and Engineering, Machine Design . Linköping University, The Institute of Technology.
    Andersson (Ölvander), Johan
    Linköping University, Department of Management and Engineering, Machine Design . Linköping University, The Institute of Technology.
    Krus, Petter
    Linköping University, Department of Management and Engineering, Machine Design . Linköping University, The Institute of Technology.
    Wäppling, D.
    Feng, Xiaolong
    Linköping University, Department of Management and Engineering, Machine Design . Linköping University, The Institute of Technology.
    Industrial Robot Design Optimization in the Conceptual Design Phase2004In: Proceedings of IEEE International Conference on Mechatronics and Robotics, Aachen , Germany, 2004Conference paper (Other academic)
  • 7.
    Pettersson, Marcus
    et al.
    Linköping University, Department of Management and Engineering, Machine Design. Linköping University, The Institute of Technology.
    Ölvander Andersson, Johan
    Linköping University, Department of Management and Engineering, Machine Design. Linköping University, The Institute of Technology.
    Adaptive Complex Method for Efficient Design Optimization2007In: Proceedings of ASME 33rd Design Automation Conference, September 4-7, Las Vegas, USA, 2007, p. 265-272Conference paper (Other academic)
    Abstract [en]

    Box’s Complex method for direct search has shown promise when applied to simulation based optimization. In direct search methods, like Box’s Complex method, the search starts with a set of points, where each point is a solution to the optimization problem. In the Complex method the number of points must be at least one plus the number of variables. However, in order to avoid premature termination and increase the likelihood of finding the global optimum more points are often used at the expense of the required number of evaluations. The idea in this paper is to gradually remove points during the optimization in order to achieve an adaptive Complex method for more efficient design optimization. The proposed method shows encouraging results when compared to the Complex method with fix number of points and a quasi-Newton method.

  • 8.
    Pettersson, Marcus
    et al.
    Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology.
    Ölvander (Andersson), Johan
    Linköping University, The Institute of Technology.
    Drive Train Optimization for Industrial Robots2008In: IEEE Transactions on RoboticsArticle in journal (Refereed)
  • 9.
    Pettersson, Marcus
    et al.
    Linköping University, Department of Management and Engineering, Machine Design . Linköping University, The Institute of Technology.
    Ölvander Andersson, Johan
    Linköping University, Department of Management and Engineering, Machine Design . Linköping University, The Institute of Technology.
    Andersson, Henric
    Linköping University, Department of Management and Engineering, Machine Design . Linköping University, The Institute of Technology.
    Application Adapted Performance Optimization for Industrial Robots2007In: IEEE International Symposium on Industrial Electronics, 4-7 June 2007, Vigo, Spain, 2007, p. 2047-2052Conference paper (Other academic)
    Abstract [en]

    Industrial robots are designed for a large spectrum of user scenarios. This implies that the robot cannot be tailor made for each situation and hence its full potential might not always be fully exploited. For further efficient use of robots the concept of application adapted performance optimization is introduced. This means that the robot control is optimized with respect to thermal and fatigue load for the specific program, which the robot performs. Simultaneously the motion program itself i.e. the path planning can be optimized in order to get the most out of the robot. These ideas are tested on a six axis robot in a press tending application.

  • 10.
    Pettersson, Marcus
    et al.
    Linköping University, Department of Management and Engineering, Machine Design . Linköping University, The Institute of Technology.
    Ölvander, Johan
    Linköping University, Department of Management and Engineering, Machine Design . Linköping University, The Institute of Technology.
    Drive Train Optimization for Industrial Robots2009In: IEEE TRANSACTIONS ON ROBOTICS, ISSN 1552-3098, Vol. 25, no 6, p. 1419-1424Article in journal (Refereed)
    Abstract [en]

    This paper presents an optimization strategy for finding the trade-offs between cost, lifetime, and performance when designing the drive train, i.e., gearboxes and electric motors, for new robot concepts. The method is illustrated with an example in which the drive trains of two principal axes on a six-axis serial manipulator are designed. Drive train design for industrial robots is a complex task that requires a concurrent design approach. For instance, the mass properties of one motor affect the torque requirements for another, and the method needs to consider several drive trains simultaneously. Since the trajectory has a large impact on the load on the actuators when running a robot, the method also includes the trajectory generation itself in the design loop. It is shown how the design problem can be formalized as an optimization problem. A non-gradient-based optimization algorithm that can handle mixed variable problems is used to solve the highly nonlinear problem. The outcome from an industrial point of view is minimization of cost and the simulataneous balancing of the trade-off between lifetime and performance.

  • 11.
    Pettersson, Marcus
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Mechanical Engineering, Machine Design.
    Ölvander, Johan
    Linköping University, The Institute of Technology. Linköping University, Department of Mechanical Engineering, Machine Design.
    Andersson, Henric
    Linköping University, The Institute of Technology. Linköping University, Department of Mechanical Engineering, Machine Design.
    Adaptive Performance for Industrial Robotics2007In: IEEE International Symposium on Industrial Electronics,2007, Vigo, Spain: IEEE , 2007Conference paper (Refereed)
  • 12.
    Tarkian, Mehdi
    et al.
    Linköping University, Department of Management and Engineering, Machine Design. Linköping University, The Institute of Technology.
    ölvander, johan
    Linköping University, Department of Management and Engineering, Machine Design. Linköping University, The Institute of Technology.
    Feng, Xiaolong
    Linköping University, Department of Management and Engineering, Machine Design. Linköping University, The Institute of Technology.
    Pettersson, Marcus
    Linköping University, Department of Management and Engineering, Machine Design. Linköping University, The Institute of Technology.
    Product Platform Automation for Optimal Configuration of Industrial Robot Families2011In: Proceedings of the 18th International Conference on Engineering Design (ICED11), Vol. 4, 2011, p. 1-10Conference paper (Refereed)
    Abstract [en]

    Product platform design is a well recognized methodology to effectively increase range and variety of products and simultaneously decrease internal variety of components by utilizing modularization. The tradeoff between product performance and product family commonality has to be carefully balanced in order to for the company to meet market requirements and simultaneously obtain economy of scale. This paper presents a framework based on high fidelity analyses tools that concurrently optimize an industrial robot family as well as the common platform. The product family design problem is formally stated as a multi-objective optimization problem, which is solved using a multi-objective Genetic Algorithm.

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