A methodology for digitalization and optimization of milling processes
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Date
2026-03-06
Authors
Σούφλας, Αθανάσιος
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Abstract
The scope of the present research is to develop a methodology to support the digitalization of machining, focusing on the optimization of machining process quality, while ensuring their viability for industrial adoption. The current literature has been extensively studied, and three key digitalization barriers have been identified: (i) the disruption of the current workflow of the machining personnel, due to digital tools; (ii) the compatibility of existing approaches with legacy machine tools and (iii) the integration and operational complexity. These findings drive the technical objectives of this research, which has been executed in three main pillars, targeting different quality loss causes.
Pillar 1 focuses on the minimization of the machining load-induced error in robot machining. A Multi-Body Simulation of the machining robot has been developed, utilizing Reduced Order Models to minimize computational time. Based on synthetic datasets from this simulation, surrogate optimization techniques are developed, for workpiece placement and posture optimization during robot machining. The simulation and optimization software are tightly interfaced with CAM to minimize disruption of the current workflow of process planners. The benefits of the proposed tools in terms of dimensional accuracy and reduction of robot vibrations are demonstrated by machining a real component.
Pillar 2 focuses on the development of an in-process chatter detection system. A retrofittable monitoring setup is developed, based on an industrial accelerometer and DAQ, and is validated regarding the quality of the produced data. Features are extracted from the vibration signals in the frequency domain, where the energy shift of the signal from harmonic frequencies to chatter is tracked, and in the time domain, where the cyclostationary properties of the signal are investigated. The calculated features are input in a Support Vector Machine classifier that detects chatter with high accuracy. The generalizability of the approach is demonstrated through the installation of the system in multiple machine tools.
Pillar 3 focuses on the development of an in-process tool wear prediction system. Cutting force and temperature data from an instrumented milling head are used as a basis for tool wear estimation. By leveraging the machining theory on cutting force modelling, relevant features are extracted from the signals and used as input to a Decision Tree classifier to predict tool wear status and a Random Forest regressor to predict the actual flank wear level. The generalizability of the approach is validated by exposing it to new, unknown datasets.
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Keywords
Milling, Machine dynamics, Chatter, Tool wear