Towards a Pittsburgh-style LCS for learning manufacturing machinery parametrizations

Heider, Michael; Pätzel, David; Hähner, Jörg

doi:10.1145/3377929.3389963

Cited by 9 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CAROL is developed in the context of a research project with the aim to optimize the parameterization process of fused deposition modelling by studying environmental influences on the process (Nordsieck et al, 2019). To this end, we extract rules based on operator interactions (Nordsieck et al, 2021) and combine them with learning systems (Heider et al, 2020) which requires an accurate dataset. To build this dataset, we perform extensive series of test prints of different objects.…”

Section: Case Studymentioning

confidence: 99%

CAD-based Grasp and Motion Planning for Process Automation in Fused Deposition Modelling

Wiedholz

Heider

Nordsieck

et al. 2021

Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics

Self Cite

View full text Add to dashboard Cite

Planning the right grasp pose and motion into it has been a problem in the robotic community for more than 20 years. This paper presents a model-based approach for a Pick action of a robot that increases the automation of FDM based additive manufacturing by removing a produced object from the build plate. We treat grasp pose planning, motion planning and simulation-based verification as separate components to allow a high exchangeability. When testing a variety of different object geometries, feasible grasps and motions were obtained for all objects. We also found that the computation time is highly dependent on the random seed, leading us to employ a system of budgeted runs for which we report the estimated success probability and expected running time. Within the budget, some objects never found feasible picks. Thus, we rotated these objects by 90 • which lead to a substantial improvement in success probabilities.

show abstract

Section: Case Studymentioning

confidence: 99%

CAD-based Grasp and Motion Planning for Process Automation in Fused Deposition Modelling

Wiedholz

Heider

Nordsieck

et al. 2021

Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics

Self Cite

View full text Add to dashboard Cite

show abstract

“…A current trend in manufacturing is to move from a fixed set of parameters to adaptive ones (Heider et al, 2020). The most promising parameters are chosen according to the system's state and this enables the configuration to be more specialised.…”

Section: Future Workmentioning

confidence: 99%

An Evolutionary Calibration Approach for Touch Interface Filter Chains

Rosenbauer,

Maier,

Gerber

et al. 2021

Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics

View full text Add to dashboard Cite

Touch interfaces are human machine interface (HMI) that can be found in a wide range of products ranging from mobile phones over cars to home appliances. Many of these HMIs measure digital signals which are used to detect touch events. These signals are processed using filters in order to decide whether there is a touch event or not. The filterchain must be functional even if the signal contains heavy noise. Thus a precise calibration of the individual filters is necessary. We employ a genetic algorithm (GA) to choose the filter parameters automatically. We evaluate our approach in a series of experiments which includes simulated as well as real data. We additionally compare our GA with manually calibrated parameters and thereby show the superiority of our method in terms of the accuracy of the calibration provided. A cost-intensive manual calibration can thus be avoided.

show abstract

“…From a machine learning point of view our work is deeply linked to LCS research. Applications range from traffic control (Tomforde et al, 2008), over distributed camera control (Stein et al, 2017) to manufacturing (Heider et al, 2020a). We are also not the first to apply XCSF to a real-world problem.…”

Section: Related Workmentioning

confidence: 99%

XCSF for Automatic Test Case Prioritization

Rosenbauer

Stein

Pätzel

et al. 2020

Proceedings of the 12th International Joint Conference on Computational Intelligence

Self Cite

View full text Add to dashboard Cite

Testing is a crucial part in the development of a new product. Due to the change from manual testing to automated testing, companies can rely on a higher number of tests. There are certain cases such as smoke tests where the execution of all tests is not feasible and a smaller test suite of critical test cases is necessary. This prioritization problem has just gotten into the focus of reinforcement learning. A neural network and an XCS classifier system have been applied to this task. Another evolutionary machine learning approach is the XCSF which produces, unlike XCS, continuous outputs. In this work we show that XCSF is superior to both the neural network and XCS for this problem.

show abstract

Towards a Pittsburgh-style LCS for learning manufacturing machinery parametrizations

Cited by 9 publications

References 0 publications

CAD-based Grasp and Motion Planning for Process Automation in Fused Deposition Modelling

CAD-based Grasp and Motion Planning for Process Automation in Fused Deposition Modelling

An Evolutionary Calibration Approach for Touch Interface Filter Chains

XCSF for Automatic Test Case Prioritization

Contact Info

Product

Resources

About