Mian, Naeem S. (2010) Efficient machine tool thermal error modelling strategy for accurate offline assessment. Doctoral thesis, University of Huddersfield.
- Accepted Version
Restricted to Repository staff only until December 2017.
Available under License Creative Commons Attribution Non-commercial No Derivatives.
The requirement for improved dimensional accuracy to achieve ever tightening tolerances in manufactured parts increases the need for high precision machine tools. Machine tool accuracy is affected by various errors from which thermal errors have been identified as one of the largest contributors. These are primarily caused by heat generated by the machine as it operates and exogenous influences, mainly in the form of varying environmental temperature, that result in deformation of the machine structure.
There is a complex interaction between the structural components having different heat sources, thermal time constants and thermal expansions and therefore the combined effect on tool position accuracy is often non-linear and difficult to correct easily. There has been considerable research effort to model this behaviour, usually based on temperature information, to compensate the induced errors. The methods and techniques have proved their capabilities with excellent thermal prediction and compensation results but they often require significant effort for effective implementation constraints for complexity, robustness, cost and time consumption.
One of the most significant resources required is thermal testing on the machine and can be the main obstacle for the implementation of many of such methods for industries where production machine availability cannot be compromised.
This research provides a method where the machine downtime can be reduced significantly using offline simulation techniques for extended and complex real world machine operations. In this research FEA is used to simulate the thermal behaviour of the entire structure of a small milling machine using Abaqus/CAE Standard FEA software.
In order to ensure accurate simulations, heat source parameters need to be obtained for which an efficient methodology was created to calculate body heat flux values from a short test. Additionally, a study was conducted to understand the heat flow mechanism across structural joints requiring Thermal Contact Conductance (TCC) values. This research contributes experimentally obtained, and therefore accurate, TCC values for structural interface conditions compatible with CNC machine tool joints not previously available. This was followed by the investigation of the thermal behaviour of the machine due to both internal heat and external environmental fluctuations.
A broad range of operating and static stability tests were conducted to validate the FEA modelling strategy for simulating the thermal behaviour of the machine for internal heating and environmental temperature fluctuations. The simulated and experimental movement of the tool matched by more than 60% in all cases; and by more than 70% in most cases. The most significant cost benefits from this project may result from understanding behaviour during the long and very long term simulations that are impractical or unfeasible to complete experimentally. This information facilitates capability assessment and model development.
Within this research, simple linear models compatible with existing compensation capabilities in modern NC systems was targeted. Extracted FEA data is used to identify temperature-displacement sensitive areas within the full machine structure. The identification method locates structural nodes whose temperature change correlates with error at the tool to effectively install temperature sensors permanently at those positions for simple linearly correlated thermal error compensation.
|Item Type:||Thesis (Doctoral)|
|Subjects:||T Technology > T Technology (General)
T Technology > TJ Mechanical engineering and machinery
|Schools:||School of Computing and Engineering|
|Depositing User:||Carol Doyle|
|Date Deposited:||26 Jul 2011 11:21|
|Last Modified:||25 Aug 2015 09:14|
Downloads per month over past year
Repository Staff Only: item control page