1. Development of a control system for Vibration Assisted Grinding
Heisum Ewad, Supervisors: Chen X, Yu D and Batako ADL Advanced Manufacturing Technology Research Laboratory, GERI, Liverpool John Moores University

2. Outlines Introduction to Grinding Project Aims &Objectives
Problem Outline
Control System
Experimental Setup
Data Acquisition
System Identification
System Modelling
Controller
System Calibration
Conclusion
Plan for Further Work

3. Introduction to Grinding
is an operation in which material is removed from the workpiece by a powered wheel.
Types of grinding
Centerless grinding
Cylindrical grinding
Internal grinding
Surface grinding

4. Introduction to Grinding
Grinding is a popular method of material removal
Construction
Aviation & Automotive
Optical lenses
Giant telescope mirrors

5. Problem Outline
One of the main problems in grinding is the growing vibration (chatter) between wheel and workpiece during the process. Regenerative vibration, a type of chatter that starts as a slip-slide interaction between wheel and workpiece.
However, a possible method to prevent this type of chatter is to apply a periodic disengagement of the wheel from the workpiece and periodic variation of the work- speed. These vibrations can be applied to the work-piece by devices such as piezo-electric actuators.

6. Why Vibration In Grinding?
Reduction of cutting forces.
Better coolant delivery over the entire contact zone.
Better heat removal from the grinding zone.
Oscillation allows the grains to cut with more than one edge.
Oscillation reduces the load per grain therefore, reduces the wheel wear.
Better surface finish due to lapping / polishing effect
Shelf-Sharpening Process of the Wheel

7. Project Aim & Objectives
Aims
Design, model, build and control a two dimensional piezo actuator for the grinding/machining operation.
Objectives
Initial Development of a controller for one axis
Implement control strategy within Matlab & Labview
Design two dimensional controller for two axis in Matlab
Design a Phase shifter to achieve oriented elliptical oscillation
Implement control strategy within Matlab & Labview to control the jigs
Experimental work

8. Control System : Theory
The goal of controller is to make the system performs in a pre-defined and desired manner.
Control systems are basically classified as:
Open Loop and Closed Loop
Open-loop control system
Closed-loop control system

9. Control System : Theory
of PID
PID stands for Proportional, Integral
and Derivative. Controllers are designed
to eliminate the need for continuous
operation attention.
KP to decrease the peak time.
KI to eliminate the steady-state error.
KD to reduce the overshoot and settling time.
Pout: Proportional output
I out: Integral output
D out: Derivative output
e: Error
τ: Time in the past contributing to the integral response

10. Modelling of the System
System motion
A mathematical was derived from the actual oscillating jig designed for the preliminary investigation. The equations of motion of the system are as follows:

11. Control System: Closed-Loop
Preliminary Results
Control System: Closed-Loop
Controller Response

12. Experimental Setup
System Set up in Closed Loop Control

13. Data Logging In Labview
Front panel of data Acquisition In Labview

14. Controller Design Flow Chart

15. Parametric System Identification
The main procedure to parametric identification are displayed in the flow chart in the next Slides. It starts with designing the experimental setup, then collecting the I/O data and pre-processing.
A model structure is defined according to the selected design.
The selected model structure parameters are then estimated and the model is validated.
An iteration is required to go back to modify the methods and techniques during the identification until an acceptable model obtained.

16. System Identification
Flow Chart illustrating the system Identification Process

17. Full system test
The first element of this design of the experimental setup is the selection of the input and output signals according to the purpose of the system identification and control process. The setup should allow the designer to repeat a certain perturbation signal if necessary and to record the I/O data with a sensible time rate.
Data Result From Labview Data Result From Labview

18. Modelling Strategies
Modelling strategies are classified into three categories according to the approaches used:
White box Model
Gray box Model
Black box Model
Why I used the black box?
This approach is wholly dependent on the use of I/O data collected from the physical system in real experiments.
System identification, in this case, is the behavioural approach for developing a model without requirement of physical understanding of the process.
The outcome of the system identification process is a model representing the original dynamic system.
For the black box model, the structure is defined first before calculating the model parameters.

19. Data Result
By using the Black Box system identification and selecting the ARX (Auto Regressive eXogenous) structure, which deals with linear systems.
Model Candidate System Validation

20. Controller
The parameters Space Method allows defining the Value of the Ki and Kp
For the PI controller the maximum value is selected from the parameter space to ensure that the entire response is represented.

21. Equipments – Abwood 5025 machine
Machine Specification
Spindle motor power
1.5KW
Spindle speed
5000 RPM
Cross traverse of head
Resolution
260 mm
10 µm
Longitudinal traverse
530 mm
Vertical traverse of head
350 mm
1 µm
Automatic feed
Pneumatic control x, y, Z Axis
Maximum wheel size
400mm*25mm

22. Calibration Result Dynamometer: Measuring Range: 10 kN Z axis X axis
Y axis

23. Calibration Result Displacement(mm)
Dial test Indicator
For the calibration process, the sensor was clamped in a support with the sensor tip in contact with the work holder.
Different gauges were placed between the sensor and the work holder and the output voltage was recorded.
The relationship between the output voltage and the sensor’s displacement was established.
Sensor gauge
Slip gauge

24. Actual Experimental Configuration

25. Conclusions Literature review has been carried out but still on-going
A mathematical model of the system motion was derived
The 3axis kistler dynamometer has been calibrated for force measurement
The displacement sensor has been calibrated.
The Data Acquisition system was built in Lab view
Initial data from the stand alone rig were recorded using Lab view
An initial closed loop control for one axis was developed in Matlab/simulink and required fine tuning in actual grinding
The initial value for the P & I values were idenfied but need fine tuning using actual grinding data

26. Work Plan for 3 months
Grinding tests with single axis oscillation of the workpiece.
Vibration
No vibration
Collecting the data from experimental work for controller fine tuning.
Extended experiment for controlled oscillation in grinding.

27. Thank you Heisum Ewad H.M.Ewad@2011.ljmu.ac.uk