Tool shape. Instead of monitoring the reasons

Tool centre point (TCP) dislocation due to thermal errors
has influenced the production of machines
for a long time. Due to the rise in
demand for high precision workpieces, research on thermal error compensation methods
has been geared up in industries and research institutes. Researchers
highlighted thermal errors accounts for 70% of total errors (tool wear, geometrical, positional, kinematical errors) in
a machine. Through optimization of design
and manufacturing technology, structural
improvement of the machine tool can be
possible which will minimize these errors. However, the solely physical limitation will not eliminate all thermal errors.

To have an idea about thermal characteristics is
complex. In general, there are six sources of thermal effect for machine tool system 1, (i) Heat produces in cutting
process; (ii) Heat produced by machine (iii) Heating or cooling effect due to cooling systems (iv) Heating or
cooling effect due to room (v) influence
of people (vi) Thermal memory from the last
environment.  These error sources reach
out each other through thermal phenomena (conduction, convection, radiation) result
in non-uniform temperature field which is main
source of thermal displacement of machine
tool. Providing temperature-controlled
environment requires high capital investments and running costs, which are good
to some extent but not in larger context. That’s why over the course of time
methodologies have been developed to compensate thermally induce errors.

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Since the last decade’s various methodologies have been
presented and implemented, to identify, predict and compensate the overall
effect of thermal distribution in a machine tool. J.vryroubal 2 presented an approach which uses the multi-regression equation, comprises of standard
temperature measurement of machine tool and new temperature measurement of spindle
cooling liquid, to compensate thermal
errors. Another method was found by Marco Gherlone
3 along with four others by reconstruction of displacement field of a
structure from surface measured strains to predict, regulate and observe the
structure behaviour. Foss and Haugse 4 used the piecewise continuous function
for predicting deform shape. Instead of monitoring the reasons for deformation
(Temperature, loads, accelerations) and converting them into displacement
through a suitable model, the deformation-strain method was used.

successes include adaptive neuro-fuzzy inference system to design two thermal
prediction models:by splitting the data space into rectangular sub-spaces (grid
model) and by exercising the fuzzy clustering method(FCM model) 5. A vector-angle-cosine
hybrid model which uses three models as reference multilinear regression model,a
natural exponential model and Finite element method 6. Compensation can also
be possible by using thermal error mode analysis for sensor location and vigorous
method, measuring noise and environmental changes7.

Gebhardt classified compensation into physical and phenomenological model. physical
model basically discretization of machine structure into key elements. Elements
exhibit part of structure and physical properties like conduction, convection,
mass etc.Through Uniform temperature distribution deformation is to be
calculated. Edge of physical model are small modelling effort and few measurements
are required. Downside physical model are alignment of model and measurement
(model matching:- density, heat transfer). of  In phenomenological model,proportionality
constant and time constants of values from recorded test are used as model
parameters.Precedence of phenomenological model only measurement are needed and
no requirement of physical model. A hiccup, the experiment has to be performed numerous
time, which is time-consuming8.

 Significant  factor influencing the accuracy in thermal
error modelling is the temperature
variables, which can be concluded by choosing suitable locations for the
temperature sensors.Fundamentally, stations and quantity of sensors are
determined based on engineering experience, more often sensors are placed right
next to heat source 9, or choosing them by using statistical approaches 10,and
decomposition methods 11. In general, it’s not practical to pre-select the optimum
locations for sensors and their required quantity because its a lot depends
upon which working environment machine is being used.