Conclusiones en GC, PI y AI

A similar procedure was followed for simulating the model using the average initial temperature for the entire model as predefined field and consideration of the settling zone.

6.4.1 Setup of initial conditions and time span selection

The average of the initial temperature magnitudes (22.5°C) was applied as a ‘Predefined Field’ prior to the creation of the two separate steps. For the first step, twelve and half hours of the measured data was used for the settling zone as before followed by the creation of the second step. Figure 6-12 shows the thermal state of the machine after the first step. Initial surface temperature magnitudes were compared and shown in Table 6-2 which again revealed to be within ±0.2°C range.

Figure 6-12: Thermal state of the machine after first step (winter test)

Measurement

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Structure Measured temperature (°C) Simulated temperature (°C)

Spindle Boss surface 21.688 21.65

Column surface 21 20.90

Base surface 21.938 21.90

Table 6-2: Comparison of measured and simulated initial surface temperatures (winter test) 6.4.2 Winter test correlations

The simulated results showed similar profile behaviour of the machine compared with the measured profile.. The results revealed very good correlations of 63% for the Y movement profiles (Figure 6-13) and 67% for the Z movement profiles (Figure 6-14). The residual errors were less than 7µm in Y and less than 12µm in Z.

Figure 6-13: Correlation between the measured and simulated Y axis movement with settling zone removed

Figure 6-14: Correlation between the measured and simulated Z axis movement with settling zone removed

Both environmental experiments were found well correlated with the simulated FEA results which have validated the fact that environmental fluctuation causes thermal

152 distortions in machine structure which deteriorates the overall thermal accuracy of machine tools throughout the year. It was identified that vertical temperature gradients in a shop floor vary with height which can be critical to large or tall machines. A procedure is suggested to track vertical temperature gradients by measuring ambient temperatures at a vertical distance of at least 500mm apart so that detailed sink temperatures can be obtained and applied to individual components of the machine for an increased FEA modelling accuracy. Similarly due to vertical temperature gradients it was impossible to accurately apply initial temperatures to individual structural elements of the machine for FEA. To solve this problem, a new method of two-step simulation was developed in this chapter where the first step considers the settling zone for the machine model allowing it to achieve a thermal state and thermal memory. This is followed by the creation of second step to simulate the machine for the environmental behaviour potentially over long periods of time with a high degree of accuracy. This method has eliminated the ambiguity of applying initial temperatures to the individual machine structure for modelling.

6.5 Summary of the chapter

It has been observed that the accuracy of a machine tool can be adversely affected by environmental temperature variations. Experimental and simulated movements between the tool and workpiece during summer and winter environmental tests, each lasting 3 days, have correlated well.

Temperature data obtained from the ambient temperature sensors was applied to the FEA simulations as time variable sink temperatures. Initial temperatures of the machine structure in the simulation do not match the initial conditions of the actual machine due to vertical temperature gradients and long term thermal memory. This creates an error in the simulation that can be removed by creating an initial simulation step that allows the determination of settling zone for the machine model.

In this research the machine was tested while static in order to get displacement data for validation, however temperature measurement can be implemented easily on a production machine tool by placing additional ambient temperature sensors to record during machining operations; which means no machine downtime is required during the measurements, and the data obtained from the machine vicinity and that particular shop

153 floor could be added to thermal compensation models. This will increase the accuracy of the machine tool by eliminating the deformation uncertainty that varying environments impose on the machine structure during long term machining operations.

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CHAPTER - 7

7 FEA NODAL MANIPULATIONS

Previous chapters have presented simulated machine tool behaviour, when subjected to internal and environmental heat sources that correlate well with experimental results. This method therefore provides a platform to use FEA modelling as an offline tool to determine not only machine behaviour, but also help with the development of compensation models. In this chapter it is used for determining the location of nodes whose displacements are sensitive to a temperature change. This is the most common form of modelling method available in modern controllers. Typically they are limited to spindle growth compensation using a linear relationship between a motor or bearing sensor and expansion. Other modelling methods are discussed in Chapter 2.

This chapter details a method and software for the offline assessment of the FEA data and help determine the temperature-displacement sensitive nodes based on search parameters and their physical locations within the FEA model. These will contribute for the development and enhancement of new and existing thermal error compensation methods respectively by updating them with the location information. The information can be used to retrofit sensors for compensation; however there can be practical limitations to their attachment. It will also help at the machine design and build stages by advising where to install temperature sensors within the machine structure.