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Warmefjord K.,Chalmers University of Technology | Carlson J.S.,Fraunhofer Chalmers Research Center | Soderberg R.,Chalmers University of Technology
Journal of Computing and Information Science in Engineering | Year: 2016

In the auto body assembly process, fixtures position parts during assembly and inspection. Variation in the positioning process propagates to the final assembly. To control the assembly fixtures, repeatability studies are used. Those studies are, however, usually done with long intervals and the fixtures might be afflicted with variation between studies. There are also other sources of variation in the final assembly, such as variation in parts due to previous manufacturing steps. To separate variation caused by fixtures and the variation caused by previous manufacturing processes, a multivariate fixture failure subspace control chart is proposed. Copyright © 2016 by ASME.


Shellshear E.,Fraunhofer Chalmers Research Center
Visual Computer | Year: 2014

Detecting self-collision for cables and similar objects is an important part of numerous models in computational biology (protein chains), robotics (electric cables), hair modeling, computer graphics, etc. In this paper the 1D sweep-and-prune algorithm for detecting self-collisions of a deforming cable comprising linear segments is investigated. The sweep-and-prune algorithm is compared with other state-of-the-art self-collision detection algorithms for deforming cables and is shown to be up to an order of magnitude faster than existing algorithms for cables with a high proportion of segments moving. We also present a multi-threaded version of the algorithm and investigate its performance. In addition, we present worst-case bounds for 1D sweep-and-prune algorithms whereby the colliding objects do not exceed a certain object density, and apply these results to deforming cables. © Springer-Verlag 2013.


Warmefjord K.,Chalmers University of Technology | Carlson J.S.,Fraunhofer Chalmers Research Center
ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE) | Year: 2012

In the auto body assembly process, fixtures are used to position parts during assembly and inspection. If there is variation in the positioning process, this will propagate to the final assembly. There are also other sources of variation in the final assembly, such as variation in parts due to previous manufacturing steps. To facilitate the separation of the different sources of variation, and thereby also improve fault diagnosis, a fixture failure subspace control chart is proposed. This control chart is based on a multivariate T 2-chart, but only variations in the fixture failure subspace are considered. The method is applied to two industrial case studies with satisfying results. Copyright © 2012 by ASME.


Warmefjord K.,Chalmers University of Technology | Soderberg R.,Chalmers University of Technology | Carlson J.S.,Fraunhofer Chalmers Research Center
ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE) | Year: 2010

The repeatability of the assembly fixtures influences the geometrical outcome of an assembly. To control the fixtures, capability studies are conducted. Those studies give however just information about the variability in a number of inspection points. In this paper, a method for transforming the variation in inspection data to variation in the contacts between workpiece and locators is described. By doing this, the fault localizing of the fixture is facilitated. Further, the accuracy of the variation simulations used to evaluate different concepts and designs can be improved. Usually, when data from a repeatability study are used as input to a variation simulation, the tolerances are only applied in the points that actually were inspected. The suggested methodology makes it possible to transform the tolerances containing the repeatability of the fixture to tolerances on the locating scheme, and they are thereby affecting every point in the simulation model, not only the inspected ones. The method is tested on a case study and the effect of including fixture repeatability in a variation simulation is investigated. ©2010 by ASME.


Segeborn J.,Volvo Car Corporation | Segerdahl D.,Fraunhofer Chalmers Research Center | Carlson J.S.,Fraunhofer Chalmers Research Center | Carlsson A.,Volvo Car Corporation | Soderberg R.,Chalmers University of Technology
ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE) | Year: 2010

The balancing of weld work load between executing stations and its robots has a significant influence on achievable production rate and equipment utilization. However, no automatic simulation based method for line balancing has been formulated up to this point. In practice, it is still manually conducted. Therefore in this work we propose two novel methods for load balancing of welds in multi station sheet metal assembly lines to minimize line cycle time. The methods are based on superimposition of the scenes/geometries of all line stations, with maintained robot positioning relative to the work piece, creating a "multi station". The weld load is balanced between all multi station robots, whereupon the individual robots are combined into stations and coordinated station wise for simultaneous operation. Furthermore one of the proposed methods reduces the subsequent need for robot coordination, by introducing some restrictions on the load balancing: Firstly, for each robot, the weld load is balanced over the other station robots such that the working envelopes are maximally separated. Secondly, for each robot, the weld load is balanced over equivalently positioned robots in other line stations, based on previous station load balancing techniques. The proposed line balancing methods are applied on two industrial case studies which each involves the balancing of about 200 automotive stud welds between 3 stations, each of 4 robots. One of the proposed methods produces line cycle times close to that of the slowest uncoordinated robot, which can be considered a theoretical optimum of the line cycle time. Corresponding algorithm running time is about 30 minutes on an Intel Core 2 Quad with 8 GB RAM. ©2010 by ASME.

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