loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Selvine G. Mathias and Daniel Grossmann

Affiliation: Technische Hochschule Ingolstadt, Esplanade 10, 85049 Ingolstadt, Germany

Keyword(s): Acoustic Emission, Tool Wear, Data Imputations, Statistical Approach.

Abstract: Acoustic emission (AE) signals obtained during machining processes can be used to detect, locate and assess flaws in structures made of metal, concrete or composites. This paper aims to characterize AE signals using derived parameters from raw signatures along with statistical feature extractions to correlate with tool wear readings. Missing tool wear values are imputed using domain knowledge rules and compared to AE signals using machine learning models. The amount of effect on tool wear is formulated using Bayesian Inferences on derived parameters such as areas under the raw signal curve in addition to comparisons with the supervised models for predictions. Using the constructed models and formulation, the presented study also includes a trace-back pseudo-algorithm for determining the stage in process where tool wear values begin to approach the wear limits.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 34.228.239.171

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Mathias, S. and Grossmann, D. (2021). Efficacy of Statistical Formulations on Acoustic Emission Signals for Tool Wear Predictions. In Proceedings of the 2nd International Conference on Innovative Intelligent Industrial Production and Logistics - IN4PL; ISBN 978-989-758-535-7, SciTePress, pages 108-115. DOI: 10.5220/0010676400003062

@conference{in4pl21,
author={Selvine G. Mathias. and Daniel Grossmann.},
title={Efficacy of Statistical Formulations on Acoustic Emission Signals for Tool Wear Predictions},
booktitle={Proceedings of the 2nd International Conference on Innovative Intelligent Industrial Production and Logistics - IN4PL},
year={2021},
pages={108-115},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010676400003062},
isbn={978-989-758-535-7},
}

TY - CONF

JO - Proceedings of the 2nd International Conference on Innovative Intelligent Industrial Production and Logistics - IN4PL
TI - Efficacy of Statistical Formulations on Acoustic Emission Signals for Tool Wear Predictions
SN - 978-989-758-535-7
AU - Mathias, S.
AU - Grossmann, D.
PY - 2021
SP - 108
EP - 115
DO - 10.5220/0010676400003062
PB - SciTePress