A Data-Driven Framework for Predicting Machining Stability - Employing Simulated Data, Operational Modal Analysis, and Enhanced Transfer Learning

Aug 4, 2026

Upload by Sage

Chatter, a self-excited vibration phenomenon, poses a major threat to machining operations, especially high-speed milling, where it can shorten tool life, reduce material removal efficiency, and damage workpiece quality. The study proposes a data-driven framework that predicts machining stability using 140,000+ simulated datasets combined with Operational Modal Analysis, enhanced Transfer Learning, and Receptance Coupling Substructure Analysis. The Random Forest classifier provides robust, accurate chatter prediction and classification across diverse operational modes. Results highlight improved operational efficiency and more reliable machining quality, supporting predictive maintenance and stable manufacturing.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages20

Reading Time50 min

AccessFree to download

Abstract

Introduction

Background and motivation

Why chatter prediction is challenging

Existing chatter prediction methods

Proposed approach (OMA with simulated data)

Springer Nature 2021 LATEX template A Data-Driven Framework for Predicting Machining Stability: Employing Simulated Data, Operational Modal Analysis, and Enhanced Transfer Learning Matthew Alberts 1 , Sam St. John 1 , Simon Odie 1 , Jamie Coble4*, Anahita Khojandi 1 , Bradley Jared3 , Tony Schmitz2,3 and Jaydeep Karandikar2 1* Department of Industrial and Systems Engineering, University of Tennessee, 851 Neyland Drive, Knoxville, 37996, Tennessee, USA. 2 Manufacturing Science Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, 37830, Tennessee, USA. 3 Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, 1512 Middle Drive, Knoxville, 37996, Tennessee, USA. 4 Department of Nuclear Engineering, University of Tennessee, 863 Neyland Drive, Knoxville, 37996, Tennessee, USA. *Corresponding author(s). E-mail(s): [jamie@utk.edu](jamie@utk.edu) ; Contributing [authors: malberts@vols.utk.edu](authors: malberts@vols.utk.edu); [sstjohn3@vols.utk.edu](sstjohn3@vols.utk.edu) ; [sodie@vols.utk.edu](sodie@vols.utk.edu) ; [khojandi@utk.edu](khojandi@utk.edu) ; [bhjared@utk.edu](bhjared@utk.edu) ; [tony.schmitz@utk.edu](tony.schmitz@utk.edu) ; [karandikarjm@ornl.gov](karandikarjm@ornl.gov) ; Abstract Chatter, a self-excited vibration phenomenon, presents a significant challenge in machining operations, particularly in high-speed milling, where it can degrade tool life, reduce material removal efficiency, and compromise workpiece quality. This study introduces a novel, data-driven approach to predicting machining stability, leveraging over 140,000 simulated datasets alongside advanced techniques such as Operational Modal Analysis (OMA), enhanced Transfer Learning (TL), and Receptance Coupling Substructure Analysis (RCSA) . The integration of these methods allows for the accurate prediction and classification of chatter across diverse operational modes. Our Random Forest (RF) classification model, trained with this comprehensive dataset, demonstrates substantial improvements in predictive accuracy and robustness. The results underscore the model’s potential to enhance operational efficiency and machining quality by providing reliable chatter predictions across a broad range of machining configurations. This research offers a significant advancement in the predictive maintenance of machining processes, enabling more stable and efficient manufacturing operations. Keywords: transfer learning, operational modal analysis, chatter, stability, additive manufacturing 1 Introduction Effective and accurate machining technology stands as a foundational element in advanced additive manufacturing, notably within the aerospace and automotive sectors, where the ability to foresee machining stability holds Springer Nature 2021 LATEX template 2 A Data-Driven Framework for Predicting Machining Stability: Employing Simulated Data, Operational Modal A paramount importance. Specifically, the integration of high-speed milling (HSM) has significantly bolstered productivity and flexibility within the hybrid manufacturing industry [1] . However, the widespread embrace of HSM comes with its own set of challenges, primarily stemming from the detrimental impact of chatter on tool longevity, material removal efficiency, and the overall quality of the workpiece [2] . Also, with additive manufacturing changing machining conditions are encountered as one machine may produce many different parts after going under tool head change outs or having other settings changed. Chatter, dating back to the 1950s, has been extensively researched, and foundational work by Tlusty [3], Tobias [4], and Koenigsberger & Tlusty [5] laid the groundwork for contemporary applications. Chatter has shown to significantly impact the automation of the process, hinder cutting efficiency, diminish machining accuracy, and even cause tool damage [6], [7] . Chatter,self-excited vibration that occurs between the cut

FAQs

What problem does the document address in machining operations?

It targets chatter, a self-excited vibration that degrades tool life, lowers material removal efficiency, and compromises workpiece quality, particularly in high-speed milling.

How does the proposed framework predict machining stability?

It uses a data-driven workflow built on 140,000+ simulated datasets, applying Operational Modal Analysis, enhanced Transfer Learning, and RCSA to enable accurate prediction and classification of chatter across operational modes.

What model is used for classification and what is the reported benefit?

A Random Forest (RF) classification model is trained on the comprehensive dataset, delivering improved predictive accuracy and robustness for chatter prediction.

Aug 4, 2026

Upload by Sage