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Massachusetts Institute of Technology

G-Code Based Toolpath Simulation for Predicting CNC Energy Consumption

Abstract

dc:description.abstract

Machining is an energy intensive process, and being able to model the energy consumption of machining would allow manufacturers to consider how to reduce their energy footprint. While many models have been developed for estimating energy consumption, they are not easily applicable or accessible to CNC machining, where the material removal rate is variable. This thesis develops a G-code based simulation that uses a voxel mesh to virtually recreate material removal, approximating the material removal rate at discretized points in the machining process. Using an energy consumption model and machine power data, material removal rates are related to the power consumption of machining the part. The simulation pipeline was validated using power data collected from literature, and for a constant material removal rate the model has shown average absolute error of 3.17% predicting power and 2.89% predicting specific energy consumption for simulated test geometries.

Degree

thesis:*
Name thesis:degree_name
Bachelor
Department dc:contributor.department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2025

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Anziani, Jonathan
Advisor dc:contributor.advisor
  • Hart, A. John

Rights

dc:rights
Statement dc:rights
  • In Copyright - Educational Use Permitted
  • Copyright retained by author(s)

Identifiers

dc:identifier.*
Handle dc:identifier.uri
https://hdl.handle.net/1721.1/162446
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/162446

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
related terms
citation

Anziani, Jonathan. G-Code Based Toolpath Simulation for Predicting CNC Energy Consumption. Massachusetts Institute of Technology, 2025. https://hdl.handle.net/1721.1/162446