Literature Review Development of Online Tool Conditioning Monitoring System By:Vikender Panwar

1. Literature Review Development of Online Tool Conditioning Monitoring System By:VikenderPanwar MEng-Mechanical Engineering(260548815) Submitted to: Prof HelmiAttia Mechanical Engineering Department McGill University

2. Tool Failure • When a tool ceases to function satisfactorily, it is termed as Tool Failure

3. Tool Wear • Inevitable • Termed as Positive as well as Negative process • Tool Wear - Product of combination of load factors on cutting edge Initial Stage Accelerated Tool Wear Steady State • Four major load factors : • A-Mechanical • B-Thermal • C-Chemical • D-Abrasive Typical Wear Zones of Tool

4. Types of Wear Mechanisms

5. Classification of Wear on basis of Wear Mechanisms • Flank Wear • Crater Wear • Crater Wear • Notch Wear • Thermal Cracking • Mechanical fatigue • Chipping of cutting edge • Plastic deformation • Notch Wear • BUE formation

6. Types of Manufacturing Monitoring Components of Monitoring

9. Characteristics of Sensors for TCM • Measurement as close to cutting point should be possible • No reduction in static and dynamic stiffness of tool • No restriction of working space and cutting parameters • Wear and maintenance-free ,easily changed , low costs • Resistant to dirt-chips and mechanical, thermal and electromagnetic influences • Function independent of tool or work piece

10. Types of Sensors

11. Force Sensors

12. Sensors Continued…

13. Types of Signal Processing

14. Signal Processing Continued… Time-Frequency Time-Frequency

15. Decision Making Strategies Online Monitoring Offline Monitoring

16. Artificial Neural Networks • Characteristics • Parallel , distributed information processing • High degree of connectivity among basic units • Connections are modifiable on experience • Unsupervised learning • Based on local information learning • General O/P of ANN is written as Y=f(∑wixi) • Basic Structure of ANN Synaptic Weights O/P ∑ f Y Activation Function Inputs = w1x1,w2x2,w3x3……..wnxn I/Ps

17. Artificial Neural Networks Continued… • Main objective in ANN is to update weights in such a way that they converges to desired output values • Weight update Rules • Gradient Descent Rule • Newton’s Method • Quasi-Newton’s Method • Conjugate Gradient Method • Problems with Back-Propagation Algorithm • Actual system have so many minima’s • Reaching global minima is not guaranteed in Back Propagation Global Minima

18. Fuzzy Logic & Fuzzy Logic Controllers • Fuzzy means precision to imprecision • Sometimes, logic of impreciseness is much powerful than precise computation methods • Various types of Membership functions :Gamma function , s-function, Triangular function,∏-function and Gaussian function Command Signal PID O/P PLANT Error Conventional PID Controller System • Two types of FLC: Mamdani-type and T-S Model • In Mamdani, consequent part takes the control action while the T-S Model employ function of input fuzzy linguistic variable as consequent of rules Command Signal FLC O/P PLANT Error FLC Controller System

19. Starting Point Complete Architecture of Fuzzy Logic Control

20. Fuzzy Logic Controllers Continued.. • Advantages • Flexible , intuitive base design • Convenient user interface • Easy Computation • Consistency, redundancy and completeness can be checked in rule-bases • Combine regulation algorithms and logical reasoning • Disadvantages • Time-consuming process • Cannot outperform PID controllers • Performance-Robustness trade-off is not taken into account • Cartesian product is the most usual way of setting up antecedents , the most memory-intensive and inefficient process • Compatibility with current industrial file formats

21. Neural-Fuzzy Networks • Overcome the shortcomings of both Neural Networks and Fuzzy Systems • Neural Nets have better learning capability while Fuzzy Systems have good interpretation ability • Hybrid approach allows to improve the decision making by combining the advantages of both Neural Nets and Fuzzy Systems

22. Types of Neural-Fuzzy Networks ------ FS FS 1 2 NN NN ++++ FS 3 FS NN NN 4

23. Intelligent Control & Intelligent Sensors • Why Intelligent Control? – • Takes care of uncertainties • Adaptive to change in environment • Distributed in nature • Based on Artificial Intelligence • Intelligent Sensors • More functionality than conventional sensors • Based on self-decision making as opposed to pre-determined commands • Able to utilize experience Killer Bee UAV, Credits:Swift Engineering • Eligibility Criterion for Intelligent Sensors • Self-calibration • Signal processing • Decision making • Fusion ability • Learning capability Robotic Arm uses Intelligent Control

24. Gap Analysis of Literature • No specific distinction was made between Tool Wear and Tool Failure • Majority of experiments/investigations were done on turning and drilling. Very less on milling operation • Many of the practical sensors mentioned in literature are good for academic purposes not for industrial use • Tool Condition Monitoring still needs to adapt with AI methods • Blackboard Systems is another AI method which has not been utilized in TCM • Much work has been done on Neural or Fuzzy alone not so much on Hybrid Systems