A paradox approach to detect internal defects of a production part

Muaaz Abdul Hadi; Milot Gashi; Vignesh Manjunath; Stefan Trabesinger; Johannes Schmid

A paradox approach to detect internal defects of a production part

Muaaz Abdul Hadi^* (Inventor), Milot Gashi (Inventor), Vignesh Manjunath (Inventor), Stefan Trabesinger (Inventor), Johannes Schmid (Inventor)

^*Corresponding author for this work

Research output: Patent

Abstract

The unique approach detects the defects/anomalies in a production part during its manufacturing (machining, grinding, etc.,) phase. This is done via the machine generated High Frequency Data (HF-Data) and the energy consumption plot. The specific defect/anomaly detected part will then be tested at the state-of-the-art (usual) end-of-line (EOL) testbed.

Original language	English
Publication status	Submitted - 27 Jun 2022

Fields of Expertise

Mobility & Production

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

2 Article

High-frequency machine datasets captured via Edge Device from Spinner U5-630 milling machine
Abdul Hadi, M., Brillinger, M., Trabesinger, S. & Schmid, J., 3 Dec 2021, In: Data in Brief. 39, C, 5 p., 107670.
Research output: Contribution to journal › Article › peer-review

Open Access
Sustainable peak power smoothing and energy-efficient machining process thorough analysis of high-frequency data
Abdul Hadi, M., Brillinger, M., Wuwer, M., Schmid, J., Trabesinger, S., Jäger, M. & Haas, F., 10 Oct 2021, In: Journal of Cleaner Production. 318, 11 p., 128548.
Research output: Contribution to journal › Article › peer-review

Open Access

Cite this

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title = "A paradox approach to detect internal defects of a production part",

abstract = "The unique approach detects the defects/anomalies in a production part during its manufacturing (machining, grinding, etc.,) phase. This is done via the machine generated High Frequency Data (HF-Data) and the energy consumption plot. The specific defect/anomaly detected part will then be tested at the state-of-the-art (usual) end-of-line (EOL) testbed. ",

author = "{Abdul Hadi}, Muaaz and Milot Gashi and Vignesh Manjunath and Stefan Trabesinger and Johannes Schmid",

year = "2022",

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T1 - A paradox approach to detect internal defects of a production part

AU - Abdul Hadi, Muaaz

AU - Gashi, Milot

AU - Manjunath, Vignesh

AU - Trabesinger, Stefan

AU - Schmid, Johannes

PY - 2022/6/27

Y1 - 2022/6/27

N2 - The unique approach detects the defects/anomalies in a production part during its manufacturing (machining, grinding, etc.,) phase. This is done via the machine generated High Frequency Data (HF-Data) and the energy consumption plot. The specific defect/anomaly detected part will then be tested at the state-of-the-art (usual) end-of-line (EOL) testbed.

AB - The unique approach detects the defects/anomalies in a production part during its manufacturing (machining, grinding, etc.,) phase. This is done via the machine generated High Frequency Data (HF-Data) and the energy consumption plot. The specific defect/anomaly detected part will then be tested at the state-of-the-art (usual) end-of-line (EOL) testbed.

M3 - Patent

ER -

A paradox approach to detect internal defects of a production part

Abstract

Fields of Expertise

UN SDGs

Fingerprint

Research output

High-frequency machine datasets captured via Edge Device from Spinner U5-630 milling machine

Sustainable peak power smoothing and energy-efficient machining process thorough analysis of high-frequency data

Cite this