Henry Arenbeck

Reliability-Based Optimal Design and Tolerancing for Multibody Systems Using Explicit Design Space Decomposition

This paper introduces a new approach for the optimal geometric design and tolerancing of multibody systems. The approach optimizes both the nominal system dimensions and the associated tolerances by solving a reliability-based design optimization (RDBO) problem under the assumption of truncated normal distributions of the geometric properties. The solution is obtained by first constructing the explicit boundaries of the failure regions (limit state function) using a support vector machine, combined with adaptive sampling and uniform design of experiments. The use of explicit boundaries enables the treatment of systems with discontinuous or binary behaviors. The explicit boundaries also allow for an efficient calculation of the probability of failure using importance sampling. The probability of failure is subsequently approximated over the whole design space (the nominal system dimensions and the associated tolerances), thus making the solution of the RBDO problem straightforward. The proposed approach is applied to the optimization of a web cutter mechanism.

Simulation Based Optimal Tolerancing for Multibody Systems

This paper introduces a new methodology for probabilistic optimal design of multibody systems. Specifically, the effects of dimensional uncertainties on the behavior of a system are considered. The proposed reliability-based optimization method addresses difficulties such as high computational effort and non-smoothness of the system’s responses, for example, as a result of contact events. The approach is based on decomposition of the design space into regions, corresponding to either acceptable or non-acceptable system performance. The boundaries of these regions are defined using Support Vector Machines (SVMs), which are explicit in terms of the design parameters. A SVM can be trained based on a limited number of samples, obtained from a design of experiments, and allows a very efficient estimation of probability of failure, even when Monte Carlo Simulation (MCS) is used. A modularly structured tolerance analysis scheme for automatic estimation of system production cost and probability of system failure is presented. In this scheme, detection of failure is based on multibody system simulation, yielding high computational demand. A SVM-based replication of the failure detection process is derived, which ultimately allows for automatic optimization of tolerance assignments. A simple multibody system, whose performance usually shows high tolerance sensitivity, is chosen as an exemplary system for illustration of the proposed approach. The system is optimally designed for minimum manufacturing cost while satisfying a target performance level with a given probability.Copyright

Multibody simulation with body coordinates using a novel quaternion-based relative revolution constraint

Multibody simulation has evolved significantly over the last decades but simultaneously has become a field of specialists and commercial software products. Body coordinates avoid complex simulation algorithms even for arbitrary system topology and simulation scenario, while exhibiting lower but in most practical cases still satisfactory computational efficiency. Currently, when body coordinates are used, special measures are required to robustly constrain the angle of relative revolution between two bodies to a predefined value. This constraint is required, e.g. for inverse dynamic simulation of a mechanism with rotational actuators. This paper presents a new formulation of this constraint, which is efficiently evaluated and makes special measures as mentioned above obsolete, thus increasing straightforwardness and simplicity of consequent multibody simulation. A multibody simulation program which is based on the new constraint formulation and provides a plain, universal, and adaptable simulation platform is presented in addition. The program is defined in Matlab and Octave, open source, freely accessible and was validated against the Matlab toolbox SimMechanics revealing similar simulation accuracy.

A proposal for patient-tailored supervision of movement performance during end-effector-based robot-assisted rehabilitation of the upper extremities

Abstract Millions of people worldwide suffer from stroke each year. One way to assist patients cost-effectively during their rehabilitation process is using end-effector-based robot-assisted rehabilitation. Such systems allow patients to use their own movement strategies to perform a movement task, which encourages them to do self-motivated training but also allow compensation movements if they have problems executing the movement tasks. Therefore, a patient supervision system was developed on the basis of inertial measurement units and a patient-tailored movement interpretation system. Very light and small inertial measurement units were developed to record the patients’ movements during a teaching phase in which the desired movement is shown to the patient by a physiotherapist. During a following exercise phase, the patient is training the previously shown movement alone with the help of an end-effector-based robot-assisted rehabilitation system, and the patient’s movement is recorded again. The data from the teaching and exercise phases are compared with each other and evaluated by using fuzzy logic tailored to each patient. Experimental tests with one healthy subject and one stroke patient showed the capability of the system to supervise patient movements during the robot-assisted end-effector-based rehabilitation.

Improvement of Practical Orientation in Teaching by Introduction of an Innovative Laboratory and Competition

Abstract In this paper, an innovative concept for a laboratory and a student competition for the improvement of practical orientation of control engineering teaching are presented. The overall goals of the new teaching concepts are to consolidate the students control theory knowledge, to establish practical relevance of the lectures, to gain experience and to develop creativity as well as team-working skills. A laboratory is planned as an introduction to automatic control for students with no engineering background. The goal is to give the students a practical and basic overview of control theory. In addition, a student contest is introduced, which is focused on autonomous vehicle control problems such as path following and advanced cruise control.

Detection of Rehabilitation-Relevant Events during Endeffector Based Robot Assisted Rehabilitation of Upper Extremities

When patients are training with robotic rehabilitation systems they are often lacking supervision by an expert like a physiotherapist, who can detect rehabilitation-relevant events (e.g. fatigue or spasticity) and intervene if necessary. Thus, a patient supervision suitable for robot assisted rehabilitation which analyses the patient’s movement as well as neurological events is presented here. The supervision is achieved by comparing kinematic and kinetic data during the rehabilitation exercise with the kinematic and kinetic data recorded beforehand during the teaching of the exercise. A fuzzy logic system is used to evaluate the compared kinematic data to evaluate the movement of the patient during each repetition. The kinetic data is used for detection of spasticity. Preliminary measurements with healthy subjects were performed as well to extract mathematical parameters which correspond to rehabilitation-relevant events.

Patient Supervision During Endeffector Based Robot Assisted Rehabilitation of Upper Extremities

About 250.000 people in Germany suffer from stroke each year. One way to assist patients during their rehabilitation process is an endeffector based robot assisted system. A 7 degree of freedom robot is mounted on the patient’s hand. During a teaching phase the physiotherapist guides the patient’s hand to perform an everyday movement. During this phase the robot passively follows the patient’s hand and records its endeffector position. After the teaching phase the therapist leaves and the exercise phase starts. The robot continuously replays the recorded trajectory and thus guides the patient’s hand like the physiotherapist. This approach strongly encourages patients to do self-motivated training. However supervision of the patient is missing because compensation movements and neurological events like spasticity can occur which cannot be detected by the robot.