Towards Automated Parameter Optimisation of Machinery by Persisting Expert Knowledge

被引：2

作者：

Nordsieck, Richard ^{[1
]}

Heider, Michael ^{[2
]}

Angerer, Andreas ^{[1
]}

Haehner, Joerg ^{[2
]}

机构：

[1] XITASO GmbH IT & Software Solut, Augsburg, Germany

[2] Univ Augsburg, Organ Comp Grp, Augsburg, Germany

来源：

ICINCO: PROCEEDINGS OF THE 16TH INTERNATIONAL CONFERENCE ON INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS, VOL 1 | 2019年

关键词：

Additive Manufacturing; Transfer Learning; Domain Adaption; Machine Learning; Knowledge Representation; SYSTEM;

D O I：

10.5220/0007953204060413

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Commissioning of machines takes up a considerable share of time and money of the total cost of developing a machine. Our project aims at developing an approach to decrease the time needed to commission machines by automating parameter optimisation with the help of formalised expert knowledge. The approach will be developed on the Fused Deposition Modelling (FDM) process, which is an additive manufacturing technique. We pay particular attention to keeping the approach sufficiently abstract to be applied to machines from other domains to benefit its industrial application.

引用

页码：406 / 413

页数：8

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