Alternate identifier:
(KITopen-DOI) 10.5445/IR/1000157789
Related identifier:
-
Creator/Author:
Ströbel, Robin [Institut für Produktionstechnik]

Probst, Yannik [Institut für Produktionstechnik]

Fleischer, Jürgen [Institut für Produktionstechnik]
Contributors:
-
Title:
Training and validation dataset of milling processes for time series prediction
Additional titles:
-
Description:
(Abstract) Ziel des Datensatzes ist das Training sowie die Validierung von Modellen zur Prognose von Zeitreihen für Fräsprozesse. Hierfür wurden an einer DMG CMX 600 V durch eine Siemens Industrial Edge Prozesse mit einer Abtastrate von 500 Hz aufgenommen. Es wurde ein Prozess für das Modelltraining und ein Prozess für die Validierung aufgenommen, welche sowohl für die Bearbeitung von Stahl sowie von Aluminium verwendet wurden. Es wurden mehrere Aufnahmen mit und ohne Werkstück (Aircut) aufgenommen, um möglichst viele Fälle abdecken zu können.
(Abstract) The aim of the data set is the training as well as the validation of models for the prediction of time series for milling processes. For this purpose, processes with a sampling rate of 500 Hz were recorded on a DMG CMX 600 V by a Siemens Industrial Edge. A process for model training and a process for validation were recorded, which were used for both steel and aluminum machining. Several recordings were made with and without the workpiece (aircut) in order to cover as many cases as possible.
(Technical Remarks) Documents: -Design of Experiments: Information on the paths such as the technological values of the experiments -Recording information: Information about the recordings with comments -Data: All recorded datasets. The first level contains the folders for training and validation both with and without the workpiece. In the next level, the individual test executions are located. The individual recordings are stored in the form of a JSON file. This consists of a header with all relevant information such as the signal sources. This is followed by the entries of the recorded time series. -NC-Code: NC programs executed on the machine -Workpiece: Pictures of the raw parts as well as the machined workpieces. The pictures show the unfinished part on the left, the training part in the middle and a part with two validation runs on the right. Experimental data: -Machine: DMG CMX 600 V -Material: S235JR, 2007 T4 -Tools: -VHM-Fräser HPC, TiSi, ⌀ f8 DC: 5mm -VHM-Fräser HPC, TiSi, ⌀ f8 DC: 10mm -VHM-Fräser HPC, TiSi, ⌀ f8 DC: 20mm -Schaftfräser HSS-Co8, TiAlN, ⌀ k10 DC: 5mm -Schaftfräser HSS-Co8, TiAlN, ⌀ k10 DC: 10mm -Schaftfräser HSS-Co8, TiAlN, ⌀ k10 DC: 5mm -Workpiece blank dimensions: 150x75x50mm License: This work is licensed under a Creative Commons Attribution 4.0 International License. Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0).
Keywords:
Machine tool
Time series prediction
Machine Learning
Milling
CNC
Related information:
-
Language:
-
Production year:
Subject areas:
Engineering
Resource type:
Dataset
Data source:
-
Software used:
-
Data processing:
-
Publication year:
Rights holders:
Ströbel, Robin

Probst, Yannik

Fleischer, Jürgen
Funding:
-
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Archiving date:
2023-06-22
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kitopen
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