Final Year Thesis

i 0542281 Sachin S. Kulkarni

Milling Cutter Testing: Tool Life Project No: MMM116

B.Eng Mechanical and Manufacturing Engineering

Sachin S. Kulkarni Student No: 0542281

April 2008

ii 0542281 Sachin S. Kulkarni

I hereby declare:

that except where reference has clearly been made to work by others, all the

work presented in this report is my own work;

that it has not previously been submitted for assessment; and

that I have not knowingly allowed any of it to be copied by another student.

I understand that deceiving or attempting to deceive examiners by passing off the

work of another as my own is plagiarism. I also understand that plagiarising the work

of another or knowingly allowing another student to plagiarise from my work is

against the University regulations and that doing so will result in loss of marks and

possible disciplinary proceedings against me.

Signed Sachin S. Kulkarni

Date 18/04/2008

iii 0542281 Sachin S. Kulkarni

Abstract

In today’s competitive manufacturing environment it is more vital than ever before to

have maximum resource utilization to achieve cost advantages over competitors. In

the metal processing industry effective cutting tool utilization is one of the key

parameters to achieving favourable machining economics. It is therefore essential to

have robust and reliable tool monitoring systems. The work presented here is an

investigation into a low cost reliable tool monitoring technique using a

microcontroller.

In this project cutting force data obtained from a series of experiments leads to the

conclusion that the analysis of cutting forces is not only complicated but can also be

misleading at times. Further investigation confirms the impracticality of the existing

system in industrial applications and high installation and running costs.

The project investigates spindle motor current analysis method of tool monitoring.

This method is less complicated when compared to the cutting forces approach. It can

therefore be implemented as a low cost package on much wider scale. This system

proposed can be upgraded or down graded based on the level of process precision and

quality.

The project also indicates that a dsPIC30F6014 may be capable of performing all the

tasks expected of a monitoring system. It is robust, cost effective and has high levels

of computational capability thus making it suitable for wider industrial applications.

iv 0542281 Sachin S. Kulkarni

Acknowledgements

I would like to take this opportunity to thank:

Mr Paul Prickett for his support, guidance, advice and patience at very juncture of this

project.

Mr Raees Siddiqui for giving me a ‘crash course’ in dsPIC30F6014 and for his wealth

of knowledge and technical expertise.

Mr. Roger Grosvenor for introducing me to the world of PIC Microcontrollers.

Finally, my parents for their love and affection and supporting me morally and

financially throughout my education.

v 0542281 Sachin S. Kulkarni

Contents

Declaration ii

Summary iii

Acknowledgements iv

Contents v

List of Figures vii

1. Introduction 1

1.1 Project Background 1

1.2 Project Aims 2

2. Monitoring the Milling Process 3

2.1 Introduction to Milling 3

2.2 Types of Milling and Milling Cuter 3

2.3 Tool Wear 6

2.4 Tool Life 7

2.5 Tool Life Monitoring 7

3. IPMM Approach 10

3.1 Outline of IPMM Approach 10

4. Cutting Force Based Experiments 12

4.1 Experimental Setup 12

4.2 Experimental Procedure 13

4.3 Results and Analysis 15

5. Spindle Load Based Experiments 18

5.1 Experimental Procedure 18

5.2 Experimental Setup 18

5.3 Results and Analysis 19

vi 0542281 Sachin S. Kulkarni

6. dsPIC Based Data Acquisition and Analysis Experiments 23

6.1 Introduction to dsPIC Family of Microcontrollers 23

6.2 Methodology 25

6.3 Data Acquisition and Analysis Algorithm and flowchart 28

6.4 dsPIC ADC Lab Experiments 30

6.4.1 Experimental Setup 30

6.4.2 dsPIC Code 30

6.4.3 Results and Analysis 34

6.5 Mathematical Correlation between Spindle Load and Tool Wear 37

7. Communication Network and Information Management 39

7.1 Monitoring Module and Central Server Communication 40

7.2 Information Management System 41

8. Conclusion 44

9. Further Work 45

References 46

Appendix 1: Typical output file from Dynoware Software 48

Appendix 2: Typical Output file from Spindle Load Signal 49

Appendix 3: dsPIC30F6014 ADCON register data sheet 50

vii 0542281 Sachin S. Kulkarni

List of Figures

Figure 2.1 End Milling, Slab Milling and Face Milling 4

Figure 2.2 Up Milling and Down Milling 4

Figure 2.3 Typical 4 Flute End Mill 5

Figure 3.1 Relationship between Cutting Force and Spindle Load 10

Figure 3.2 Work done by the cutter 11

Figure 4.1 Milling cutters used in the experiment 12

Figure 4.2 Milling centre and data acquisition system 13

Figure 4.3 Cutting Forces plot for 20 seconds of machining data 16

Figure 4.4 Cutting Forces plot for 5 seconds of machining data 16

Figure 5.1 Typical Spindle Load Plot for a Milling Test 20

Figure 5.2 Filtered Spindle Load Signal 21

Figure 6.1 Input and output signals to the dsPIC A/D converter 26

Figure 6.2 Correction technique to eliminate spindle load component 27

Figure 6.3 dsPIC control and operation algorithm 29

Figure 6.4 Output as obtained from dsPIC A/D Converter 35

Figure 6.5 Magnified A/D converter output plot 35

Figure 6.6 Analogue Spindle Motor Load Current from CNC Controller 36

Figure 6.7 Digital Equivalent of the Spindle load signal 36

Figure 6.8 Typical Spindle Load Plot 37

Figure 7.1 IPMM Architecture with the Tool Monitoring Node 39

Figure 7.2 Communication between Monitoring Module and other elements 40

Figure 7.3 Tool Life Database on Central Server 42

Figure 7.4 Front End HMI Screen on the Central Server (Screen 1) 43

Figure 7.5 Tool Life Information with Tool Utilization Chart 43

viii 0542281 Sachin S. Kulkarni

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1 0542281 Sachin S. Kulkarni

1. Introduction

1.1 Project Background

Lean Manufacturing has been widely accepted as the standard which many

manufacturers aim for. The emphasis on reduction of waste and implementation of

‘Just in Time’ systems result in demand for higher levels of control and predictability

of the manufacturing process. Essential insights into the key elements of the

manufacturing process are needed to facilitate the engineers to make timely and

accurate decisions.

In the metal processing industry it is becoming increasingly essential to be able to

detect the tool failure as soon as it occurs to avoid the financial losses and also waste

of time, material and energy associated with the continued use of damaged tool. In an

event of tool breakage, the work piece under machining is in danger of being

damaged. This could also put the operator in danger of physical injury due to flying

debris from the broken tool or component. In order to tackle this problem research is

being undertaken in both academic and industrial circles to monitor the tool life and

predict the end of its useful life and/or the potential failure point of the tool being

used. However, this research has failed to produce solutions which are universal in

nature. This can be attributed to the fact that most of the research in academia and

industry has been mutually exclusive. The complicated physics of the cutting forces

involved in the cutting process are often modelled within laboratory environment to

support tool monitoring with a degree of success. However the resulting systems have

failed to impress the industry.


This project aims to consider the milling process and investigate methods of

monitoring the tool life of the milling cutter based on various cutting parameters and

the information obtained from the CNC machine controller.

1.2 Project Aims

This project is to complement the current research activity being carried out at the

Intelligent Process Monitoring and Management (IPMM) Research Group at Cardiff

University. The project investigates the feasibility of a low cost Microcontroller based

tool monitoring technique that can be effectively used in the industry.

The objectives of the project are:

Ü Understanding the Milling Process, Concept of Tool Wear.

Ü Research into Tool Monitoring Techniques and Systems available in Industry.

Ü Review of the research at IPMM Research Group, Cardiff University

Ü Conduct milling experiments to obtain cutting forces and spindle current

profiles.

Ü Compare and contrast the cutting force based monitoring approach with

spindle motor load based monitoring approach.

Ü Investigate the feasibility of PIC based tool monitoring technique.

Ü Integration of the PIC based system as one of the nodes in the IPMM

Architecture.


2. Monitoring the Milling Process

2.1 Introduction to Milling

Milling is a metal cutting process that involves a work piece which is fed to rotating

cutter at a preset feed rate and with the spindle head rotating at a fixed speed [1]. The

relative motion between the rotating tool and the workpiece results in the cutting

operation[2]. The multiple cutting edges on the milling cutter facilitate rapid material

removal and reduced the frequency of regrind ing the tool [1, 3]. The machined

surface can be flat or can have the profile of the milling cutter. More complex and

intricate profiles can be machined through a combined effect of the cutter profile and

simultaneous interpolation of more than one axes of the CNC machine employing a

state of the art control system.

2.2 Types of Milling and Milling Cutter details

The milling processes are generally classified based on the type of milling cutter used

or the final profile obtained. For instance figure 2.1 shows ‘End Milling’, ‘Slab

Milling’, ‘Face Milling’. Alternatively, classification is also possible on the basis of

the axis along which the milling cutter is oriented viz. Vertical Milling and Horizontal

Milling. In figure 2.1, Slab milling is classified as horizontal milling and all other

milling processes are classed as vertical milling. It is also possible to use more than

one cutter on a horizontal milling machine. This process is called straddle milling and

is widely used for high volume machining with little or no variation.


Figure 2.1 End Milling, Slab Milling and Face Milling, figure reproduced from Introduction to

Basic Milling (1995) [1]

Besides the classification mentioned above the methods used when undertaking

milling processes are classified as UP Milling and DOWN Milling. This classification

is based on how the cutter enters the work piece.

1. Up Milling: Figure 2.2A shows the UP Milling process. The direction of the

feed motion is opposite to the direction of the cutter rotation [3]. In this case the initial

chip size is small and gradually increases. The power consumption in this type of

milling in less when compared to Down Milling. However the surface finish is

sometimes poor compared to the Down Milling process.

2. Down Milling: Figure 2.2B shows the typical Down Milling process. The

direction of the feed and the cutter rotation is same [3]. The tool takes a bigger chunk

of the metal when it enters the work piece compared to when it exits. However the

tool life is higher in Down Milling as compared to Up Milling [4].

Figure 2.2 Up and Down Milling, figure reproduced from Introduction to Basic

Milling (1995) [1]


Figure 2.3 shows the key elements of a typical End Milling Cutter similar to those

used in the experiments undertaken in this project.

Figure 2.3 Typical 4 Flute End Mill [5]

The tooth and the flute undergo wear which together is classed as tool wear. The tooth

wears due to the high cutting forces resulting from metal fracture in the work piece.

The flute is designed in order to facilitate smooth chip flow resulting from the cutting

operation. This chip flow also results in friction on the flute edges and thus wears the

tool with time.


2.3 Tool Wear

Tool wear will normally involve the gradual deterioration of the cutting edge. This

occurs mainly due to the friction between the cutting tool and the metal being

processed. The tool wear in metal cutting is caused due three main reasons: Adhesive

wear, abrasive wear and diffusion wear [3].

1. Adhesive wear: Due to high levels of friction between the cutter and the

material, an asperity weld junction is formed and consequently fractured due

to the relative motion of the chip and the tool [3]. This results in small

fragments of cutting tool being carried away by the tool [3].

2. Abrasive wear: The hard particles on the chip and the work piece formed as

the result of high working temperatures erode tool fragments during relative

motion [3].

3. Diffusion wear: This occurs due to the changes in the internal molecular lattice

structure of the tool material [3].

Besides the friction various other factors influence the deterioration of the cutting

edge. Some of these factors are:

1. Thermal wear: The friction in the cutting process leads to high operating

temperatures. This induces thermal wear on the cutting tool. Thermal wear can be

controlled by using an effective coolant system.

2. Accelerated Tool Wear: This can occur due to hard spots in the material being

machined. Although the time the spent in the cutting operation may be the same, the

level of tool wear is different due to the hard spots.


2.4 Tool Life

Tool life is a key economic factor in metal cutting and the right choice of machining

parameters like speed, feed, and depth of cut can provide improved tool life and thus

reduce the overall machining costs [3]. “Tool life is the actual life of the tool when the

cutting is satisfactory. It is defined as the time to failure, volume of material cut

before failure or in mass production it is the number of parts produced before

failure.”[6]. The level of

acceptable wear on a cutting edge is based on the operation for which the tool is used.

For instance a higher level of wear is acceptable in a roughing operation as compared

to a finishing operation. This makes it difficult to adopt a universal standard of

acceptable tool wear and thus it is equally difficult to standardise the tool life for a

given tool. For cost effective manufacture it is important to monitor the tool wear and

thus tool life for optimum tool utilization.

2.5 Tool Life Monitoring

Methods and techniques for effective tool life monitoring have been developed in

both industry and academia. These can be broadly classified into two categories [7]:

1. Direct Monitoring: In this type of monitoring the actual wear on the tool is

measured against preset standards. This means the tool needs to be taken off

the machine and the process needs to be stopped. This is therefore not

desirable in an industrial application demanding high levels of productivity.

2. Indirect Monitoring: This method involves obtaining and analysing various

key machine parameters to assess the current tool life situation. The machining

parameters could be the cutting forces on the tool, the spindle load current,


acoustic emissions from the tool which can also be used to asses the wear on

the tool. A combination of two or more of these parameters can be used

depending on the level of accuracy required.

There are two Indirect Monitoring methods currently used in the industry viz.

1. Tool life in terms of machining time

This is the simpler of the two methods. The tool life is calculated in minutes using

Taylor’s Tool Life Equation. The variable in the CNC controller memory is specified

to hold this value. The value held in this variable is decremented every time a metal

cutting ‘G’ command is used (G01, G02, G03). The pre scalar value for the real time

and the actual tool life can also be specified as a ratio [8]. This system is entirely

independent of the actual work done by the milling cutter. Therefore the accuracy of

the tool life monitoring system is down to the proper selection of the pre scalar value.

For instance, in the SIEMENS Sinumerik 802D system, $TC_MOP2[t,d] is a variable

used to hold the residual tool life for active tool ‘t’ and cutting edge ‘d’.

$A_MONIFACT is a system monitoring clock which can be run faster of slower

based on the operation and experience. [8]

This can be explained with an example as follows [8]:

$A_MONIFACT = 1 means, 1 minute real-time = 1 minute of tool life

$A_MONIFACT = 0.1 means 1 minute real-time = 0.1 minutes of tool life

$A_MONIFACT = 3 means, 1 minute real-time = 3 minutes of tool life

This method relies heavily on the experience of the user and uniformity of the

material being machined. This kind of tool monitoring system is available on the

SIEMENS Sinumerik 802D CNC Controller.


2. Tool Life in terms of work done by the tool.

This method of tool life monitoring is rather complicated and expensive as compared

to the earlier method. The tool life is represented in terms of the maximum work that

could be done by the tool before it is deemed undesirable for further use. The work

done by the tool can be monitored by various machine parameters like the spindle

current, cutting forces on the tool etc. This method is more accurate as it is capable of

obtaining real time machining data and updates the actual tool life and does not

entirely rely on empirical standards. The sophistication of this system makes it

possible to also monitor the tool breakage and alert the operator. The example of this

technique can be found in the FANUC Intelligent control called AI Tool Management

system [9].


3. IPMM Approach

3.1 Outline of IPMM Approach

The IPMM methodology was developed at the Cardiff University’s IPMM research

group. One of the key research interests of the group is to develop a low cost reliable

tool monitoring system. The group aims to use spindle load is the key indicator for the

amount of work done by the tool and thus determine the tool wear. Since tool wear is

the function of the work done by the tool [10].

The relationship between the spindle load and the cutting forces has been established

by means of various experiments. Figure 3.1 shows the relationship between the

Average Total Cutting Force and Average Spindle Load [10].

Fig 3.1 Relationship between Cutting Force and Spindle Load, figure reproduced from

Prickett P.W & Grosvenor R.I (2007) [10]


Furthermore, three cutting tests performed at the IPMM Research group indicate that

for a same machining cycle under conditions of similar cutting parameters, the work

done by the tool is different. This is indicated in figure 3.2 [10].

Figure 3.2 Work done by the cutter for each cutting cycle with same cutting parameters, figure

reproduced from Prickett P.W & Grosvenor R.I (2007) [10]


4. Cutting Force Based Experiments

Previous research has proved that there exists a strong relation between cutting forces

and tool wear [11, 12] The Total Tool Force can be used to compute tool wear and

thus establish the tool life of a milling cutter. Therefore an experiment was conducted

to obtain the cutting forces.

4.1 Experimental Setup

The laboratory setup comprised of a Kondia B500 Milling Vertical Milling Machine

on which all the cutting tests were performed. The machine bed was installed with a

Kistler Dynamometer. A standalone industrial computer running “Dynoware”

software was used to obtain cutting force data from the Kistler dynamometer. Two

milling cutters were used in the experiment both 20mm 4 flute cutters but one had a

2mm edge chipped off on one of the tooth as shown in the fig 4.1. This was to

simulate tool breakage and analyse the changes in the cutting forces.

Figure 4.1 Milling cutters used in the experiment.

2mm chipped off


Figure 4.2 Milling Centre and Data Acquisition Systems

The output data from the Kistler Dynamometer was converted in to MS Excel

spreadsheets by the Dynoware software. The spreadsheet comprised of cutting forces

in x, y and z direction. Appropriate data sampling rate was used to obtain optimum

data points.

4.2 Experimental Procedure

The machine was appropriately setup with the required part programme. The first set

of tests was carried out with the “good tool”. A total of nine tests were performed

with the following machining parameters.


Table 4.1 Machining Parameters

Test No

Depth of Cut / mm (X Axis)

Depth of Cut / mm (Y Axis)

Feed / mm/min

Speed / rpm

1 1 1 100 300 2 1 2 100 300 3 1 3 100 300 4 2 1 100 300 5 2 2 100 300 6 2 3 100 300 7 3 1 100 300 8 3 2 100 300 9 3 3 100 300

These machining parameters were selected based on experience to provide best set of

data. The data acquisition was initiated manually and 20seconds worth of data was

acquired. The x, y and z axis force profile was obtained for each test.

The same set of tests was repeated with the second “broken” tool with 2mm chipped

off one of its cutting tooth. A total of 18 excel spreadsheets with the force data were

obtained by the end of the cutting tests.


4.3 Results and Analysis

Table 4.2 shows a sample of the data from a typical output of the dynamometer used

in the experiment.

Table 4.2 Cutting forces for Depth of Cut 1mm X 1mm on a “good tool”

Time [s] Ch1 Ch2 Ch3 0 47.0581 -49.6216 25.6348

1.00E-03 45.2271 -44.7998 25.0244 2.00E-03 45.2271 -46.3257 26.0925 3.00E-03 51.5747 -43.396 24.4141 4.00E-03 47.3633 -40.4663 26.2451 5.00E-03 43.335 -40.2832 26.8555 6.00E-03 41.3818 -34.1797 25.0244 7.00E-03 37.7808 -27.0996 25.0244 8.00E-03 37.9028 -24.353 23.9563 9.00E-03 31.6772 -21.4233 23.3459 1.00E-02 21.4844 -14.9536 23.6511 1.10E-02 20.752 -6.5918 16.6321 1.20E-02 3.60107 2.99072 11.9019 1.30E-02 -6.83594 6.53076 13.7329 1.40E-02 4.76074 5.24902 10.0708 1.50E-02 6.04248 7.20215 10.0708 1.60E-02 -2.56348 2.74658 12.3596 1.70E-02 1.15967 1.0376 11.1389 1.80E-02 6.53076 4.63867 10.2234 1.90E-02 2.19727 3.41797 12.0544

The data in table4.2 show only the first 20 values obtained from the dynamometer.

Ch1, Ch2 and Ch3 and the cutting forces in the x, y and z directions respectively.

Figure 4.3 shows the plot of cutting forces across the entire 20 seconds of data where

as figure 4.4 shows 1 second worth of data (5 revolutions).


Fig 4.3 Cutting Forces plot for 20 seconds of machining data

Fig 4.4 Cutting Forces plot for 5 seconds of machining data


The graphs above show three plot and two of which (Ch2 and Ch3) are in the negative

force region. This is because of the dynamometer was setup. This information is very

effective for the analysis of unusual events which occur infrequently during cutting.

Therefore this approach is better suited for short term trends. However tool

monitoring demands the analysis of long term trends. This system in not suited for the

long term trends because of the following:

1. High levels of analysis required to compute the resultant cutting force of three

individual force patters obtained from the dynamometer.

2. At optimum sampling rates a vast amount of data is produced and storing

would need larger memory capacity and thus it isn’t cost effective.

Besides, the other shortcomings in using the dynamometer based system are as

follows:

1. The initial setup cost of the dynamometer and the corresponding software is

too high, therefore can not be used extensively.

2. Components to be machined have to be smaller than the dynamometer.

3. Operator intervention is essential to initiate data acquisition.

4. It is inconvenient to use the dynamometer along with the specialized work

holding which may be required in machining complex parts. This also

increases the machine setup time.


5. Spindle Load Based Experiments

IPMM research has previously established that the relationship between the Total

Tool Force and the Spindle Load is linear and it has been proved as a result of

previous research that there exists a strong correlation between the cutting forces and

the tool wear [11]. It is therefore, possible to argue that there exists a relationship

between Spindle load and tool wear. The objective to this experiment is to obtain

Spindle load for a metal cutting operation.

5.1 Experimental Setup

The laboratory setup comprised of a Kondia B500 Milling Vertical Milling Machine

on which all the cutting tests were performed. A standalone PC was used to acquire

the data from the CNC machine controller. A data bus carrying the spindle load signal

within the CNC controller was tapped into to obtain the signal. A 20mm, 4 flute

milling cutter was used for the milling tests. The data was stored in the MS Excel

CSV format. The spreadsheet comprised of spindle load signal, 0V level and +5V

level.

5.2 Experimental Procedure

These tests were conducted concurrently with the tests described in section 4.2 using

the machining parameters shown in table 4.1

A total of 9 excel spreadsheets with the spindle load data were obtained by the end of

the cutting tests.


5.3 Results and Analysis

Table 5.1 shows a typical output of the spindle load signal spreadsheet.

Table 5.1 Typical Spindle load output

Sr. No Cal 5V Cal 0V Spindle Speed Spindle Load 1 1041 24 118 74 2 1064 27 115 85 3 1050 28 120 78 4 1048 28 116 88 5 1053 24 118 76 6 1047 21 114 77 7 1051 26 117 76 8 1046 29 112 77 9 1045 27 126 74 10 1049 26 125 81 11 1046 26 119 75 12 1049 26 150 82 13 1047 26 115 79 14 1053 26 118 69 15 1052 26 117 76 16 1047 26 114 70 17 1045 26 113 73 18 1051 26 117 61 19 1050 26 112 77

Cal 5V and Cal0V show the calibrated values of 5V and 0V level respectively. The

values in the spindle load vary between the calibrated 5V and 0V values. Fig 5.1

shows the plot of Spindle load against time. The 5V on the graph is equivalent to.6kW

of power.


Figure 5.1 Typical Spindle Load Plot for a Milling Test

The plot in fig5.1 shows the only one variable quantity i.e. spindle load and therefore

it is more straightforward to analyse and obtain results as compared to cutting forces

plot obtained from the previous experiment. The plot also shows the change in the

spindle load as the cutter was engaged with the work piece. Fig 5.2 shows the filtered

and magnified plot of the spindle load.


Figure 5.2 Filtered Spindle Load Signal

The spindle load signal during non cutting time in fig 5.2 (“No Cutter Work piece

Contact”) is the power consumption in order to overcome the inertia of the machine

spindle head and set it in motion. The increase in the spindle load is a result of the

cutter coming in contact of the workpiece.

Although this approach reduces the number of signals when compared to the cutting

force approach, it is still subject to the problem of high data volumes. This makes it

essential to have a considerable large memory space. From the tests, it was found that

the each MS Excel CSV file was 1.00 MB and had 16 seconds of data. If this system

were to be used for industrial applications with a machine working for 10 hours, it

would produce approximately 2.40 GB data. Therefore a tool room employing


numerous such systems would have to invest significantly in memory space, thus

pushing up the installation cost.

Besides, the use of the standalone PC reduces the robustness of the system and may

render it unsuitable for the industrial applications. However this system does

overcome the problem of data complexity, the need for human intervention and the

work piece size restrictions compared to the cutting force analysis method.


6. dsPIC® Based Data Acquisition and Analysis Experiments

The amount of data obtained from the spindle load signal can be reduced and

effectively managed by appropriate data analysis systems. A PIC based data analysis

approach was used to reduce the data volumes and accurately approximate the

effective spindle load for the entire machining cycle.

The data acquisition process remains the same as in experiment 2. The only change

however is the use of a PIC device to reduce the data size at source. Besides reducing

the data at the source the PIC is also employed to process and analyse the data

obtained. A dsPIC30F6014 was used to condition the data at source. The dsPIC was

chosen because of its large internal memory, powerful Analogue-to-Digital

conversion capacity, high processing speeds and versatility which would be essential

for further development.

6.1 Introduction to dsPIC® family of Microcontrollers

The dsPIC30F family of microcontroller is designed and manufactured by Microchip

Inc which is a leading provider of Microcontrollers and analogue semiconductors. The

dsPIC30F family of Microcontrollers is the high end version of microcontrollers in

the Microchip product offerings. It has a 24 bit instruction word length and 16bit data

path.

The microcontrollers in the dsPIC30F family can be explained under three

headings[13].


1. CPU Core

The core of the dsPIC30F family is consistent with all other PIC microcontrollers and

has a Harvard Architecture with a modified instruction set and a Digital Signal

Processor (DSP) Engine [13].

2. System Integration

The System integration functions are the tools which help reduce the systems cost and

increases the system reliability and design flexibility. These include [13]:

i. Internal Clock (Oscillator)

ii. Reset

iii. Flash and EEPROM Programming

iv. Device Configuration

v. Watchdog Timers and Power Saver Mode

3. Peripherals

The Peripherals are the tools with in the dsPIC Microcontroller which can be used

with communicate to the external world. The dsPIC can be interfaced to an external

stand alone PC and sensors/transducers. It can also signal conditioning functions on

any input signal. The peripherals of interest in this project include

i. 12bit A/D Converter: Analogue to Digital Converter

ii. UART: PC communication module

iii. Timers: For Synchronised A/D operation


The other peripheral functions within the dsPIC are

iv. I/O Ports

v. Input Capture Module

vi. Output Compare Module

vii. Quadrature Encoder Interface

viii. SPITM Module

ix. I2CTM Module

x. Data Converter Interface

xi. CAN Module

These functions can be implemented depending upon the level of system capability

desired.

The code developed can be ve rified and debugged using an In-Circuit Debugger 2

(ICD2) is a low cost hardware debugger that can be used instead of using expensive

hardware or In Circuit Emulator [14].

6.2 Methodology

The internal memory of the dsPIC was used as a buffer to hold 100 digital values

obtained as a result of the A/D conversion. The summation of these values was stored

in a single dimension array. The contents of this array were then exported to the

remote PC thereby reducing the number of data points and making the system leaner

without reducing the sampling rate for data acquisition hence the accuracy of the

system is not compromised. Figure 6.1 illustrates the typical spindle load signal and

corresponding output as a result of the dsPIC algorithm.


Figure 6.1 Input and output signals to the dsPIC A/D converter

Data Analysis


Fig 6.1 shows the original spindle load signal and the figure 6.2 shows the signal

correction technique used.

The signal was reduced in nine individual blocks. For each block, the analogue signal

was converted to digital values. The size of the block can be decided by the operator

depending on the amount of data required. The summation of digital values in the nth

block was held in the corresponding ? n variable within the result array in the dsPIC.

Value in ?1 was subtracted from each ? n in order to eliminate the component of

spindle load to overcome the friction and inertia of the spindle head. Therefore the

final value in the array would represent the actual load as a result of the cutting

operation as shown in fig6.2

Figure 6.2 Correction technique used to eliminate unwanted spindle load component


The instantaneous value of the spindle load component due to cutting can thus be

calculated as follows:

C ( t ) = L ( t ) – L0

Where,

C(t) is then instantaneous spindle load due to cutting at time t,

L(t) is the instantaneous spindle load

L0 is the spindle load when tool and work piece are not in contact.

6.3 Data Acquisition and Analysis Algorithm and Flowchart

A step by step signal conditioning algorithm was developed as follows:

1) Start

2) Check if the spindle is rotating (Halt program execution till spindle is set in

motion)

3) Store the first value of ADC conversion block (?1) as L0

4) Subtract ?1 from all subsequent values of ? n.

5) Store the ADC values in the array in PIC memory.

6) Check if the array overflows, on array overflow export values to the remote

PC.

7) Terminate signal conditioning once the spindle rotation stops.

8) End

This algorithm was converted into a flowchart as shown in the figure 6.3

(Equation 6.1)


Figure 6.3 dsPIC control and operation algorithm


6.4 dsPIC® Based ADC Lab Experiments

An experiment was conducted in a laboratory based environment to test the basic

Analogue to Digital Conversion module using a dsPIC30f6014. This was done in

order to confirm that a dsPIC had the desired level of computational capability. It was

intended that minor changes could be implemented to achieve the final working

model.

6.4.1 Experimental Setup

The experimental setup comprised of a dsPIC development board with dsPIC30f6014

(microcontroller) and peripheral development environment. A computer running

MATLAB was used to generate a simulated signal as obtained from the spindle load

from the CNC machine controller. The output of the MATLAB signal generator was

the input signal for the dsPIC30f6014. This was used to replicate the spindle load

signal without having to do “live” cutting tests. A second computer running MPLAB

software was used to develop the PIC code and a MPLAB ICD2 was used to control

the operation of the dsPIC30F6014. A signal generator and oscilloscope were used for

trouble shooting. The output of the ADC converter was read using the MPLAB

software and exported to excel.

6.4.2 dsPIC® Code

A PIC code was developed based on the algorithm shown in figure 6.3. This code

converted the analogue signal (Spindle Load Signal) to its digital equivalent. The

code is also capable of calculating the spindle load component due to cutting as

shown in figure6.2 by eliminating the load component to overcome the spindle head

inertia and friction. The final Digital values are stored in the result array


A simple delay loop with a while loop is used to obtain the various timing delays

required for data acquisition and conversion. It would be required to change the delay

time depending on the sampling rate required.

Table 6.1 shows the list of all the variables used in the PIC code and values they hold.

The Algorithm shown in fig 6.3 was converted to a dsPIC30F6014 compatible code

using the MPLAB C30 C Compiler. MPLAB C30 complier simplified code

development as compared to the Assembly Language code development. MPLAB

C30 allowed developing a Microcontroller code in C language using the assembly

language programming techniques. The following C code was developed to operate

the dsPIC30F6014 in A/D mode. The analogue input generated from the function

generator and the equivalent digital output was stored in the ADC result array.

Sr. No Variable Description 1 ADCresult Array holding the A/D result 2 NUM_SAMPLES Number of Digital Values to be held in ADCresult 3 ADCindex Pointer used to address ADCresult 4 Pinconfig Initialize the input signal pin 5 Scanselect Scans the input pins for input signal 6 config1 Holds the initialization values for ADCON0 7 config2 Holds the initialization values for ADCON1 8 config3 Holds the initialization values for ADCON2 9 TOOLLIFE Array Holding the value of Tool Life 10 TEMPTOOLLF Holds intermediate Tool Life value or further analysis

11 ZEROLOAD Holds value for load component to overcome spindle head inertia and friction

12 ADCPTR Variable used in the for Loop


// Basic A/D Converter using dsPIC30F6014 #include<p30f6014.h> // Header File #include<adc12.h> // Header File #define dataarray 0x1820 #define NUM_SAMPLES 3200 unsigned int ADCresult[NUM_SAMPLES]; unsigned int ADCindex; unsigned int PinConfig, Scanselect,config1, config2, config3, TOOLLIFE[32], TEMPTOOLLF, ADCPTR, ZEROLOAD,i,j,x; void ADCINIT(); // Function Prototype void delay(); // Function Prototype int main(void) { ADCINIT(); // ADC Initialization ZEROLOAD=ReadADC12(0); // Load when tool not in cut ADCON1bits.ASAM=0; //Initiate Data Acquisition ADCindex=0; for(j=0;j<32;j++) // Program to hold 32sec data { TOOLLIFE[j]=0; for(ADCPTR=0; ADCPTR<100; ADCPTR++) { i=0; ADCON1bits.SAMP=1; // End Acquisition delay(); //conversion delay ADCON1bits.SAMP=0; // Start Acquisition i=0; while(i<10) { i++; } ADCresult[ADCindex]=ReadADC12(0); TEMPTOOLLF=ADCresult[ADCindex]; ADCindex++; TEMPTOOLLF=TEMPTOOLLF-ZEROLOAD; TOOLLIFE[j]=TOOLLIFE[j]+TEMPTOOLLF; } TOOLLIFE[j]=TOOLLIFE[j]/100; // ADC Result Average } while(1) // Looped Program Execution return 0; } // End of Main


void ADCINIT() { PinConfig = ENABLE_AN10_ANA; Scanselect = SKIP_SCAN_AN0 & SKIP_SCAN_AN1 & SKIP_SCAN_AN2 & SKIP_SCAN_AN3 & SKIP_SCAN_AN4 & SKIP_SCAN_AN5 & SKIP_SCAN_AN6 & SKIP_SCAN_AN7 & SKIP_SCAN_AN8 & SKIP_SCAN_AN9 & SKIP_SCAN_AN11 & SKIP_SCAN_AN12 & SKIP_SCAN_AN13 & SKIP_SCAN_AN14 ; config1=ADC_MODULE_ON & // Initialize ADCON0 ADC_IDLE_CONTINUE & ADC_FORMAT_INTG & ADC_CLK_MANUAL & ADC_AUTO_SAMPLING_OFF; config2=ADC_VREF_AVDD_AVSS & // Initialize ADCON1 ADC_SCAN_OFF & ADC_SAMPLES_PER_INT_1 & ADC_ALT_BUF_OFF & ADC_ALT_INPUT_OFF; config3=ADC_SAMPLE_TIME_0 & // Initialize ADCON2 ADC_CONV_CLK_INTERNAL_RC & ADC_CONV_CLK_Tcy & ENABLE_AN10_ANA & SKIP_SCAN_AN10; SetChanADC12(ADC_CH0_POS_SAMPLEA_AN10 & ADC_CH0_NEG_SAMPLEA_NVREF); OpenADC12(config1, config2, config3, PinConfig, Scanselect); } // End of ADCINIT void delay(void) // Delay Loop { int i=0; while(i<25000) { i++; } }


6.4.3 Results and Analysis

Two signals were used to assess the operation of the ADC and the algorithm. The first

signal was a sinusoidal wave of 2hz frequency and 4V peak to peak voltage generated

from the signal generator. The dsPIC algorithm was programmed to sample 3200 data

points. Table6.2. shows the first 100 values output from the ADC and figure6.4.

shows the plot of all the digital values obtained from the dsPIC ADC.

Table6.2. Digital Values obtained from the ADC


Fig 6.4 Output as obtained from dsPIC A/D Converter

On magnifying the first hundred output values of the ADC, the Successive

Approximation method of ADC used by the dsPIC becomes obvious as shown in

figure6.5

Fig 6.5 Magnified A/D converter output plot


The second experiment was conducted using spindle load signal obtained from one of

the cutting tests. Figure 6.6 shows the spindle load current obtained from the SNS

controller which was simulated using MATLAB in Laboratory environment and

figure 6.7 shows the digital output obtained from the dsPIC30F6014.

Figure 6.6 Analogue Spindle Motor Load Current from CNC Controller

Figure 6.7 Digital Equivalent of the Spindle load signal obtained from the dsPIC


6.5 Mathematical Correlation between Spindle Load and Tool Wear

As stated earlier, it has been proved that there is a relation between the spindle load

and tool wear. The spindle load characteristics in digital format can be obtained as

shown in section 6.4. Consider the spindle plot shown in figure 6.8 for cutting

operation.

Fig 6.8 Typical Spindle Load Plot

Using time based tool life monitoring system, the total reduction in Tool life T

(expressed in minutes) can be expressed as:

T = T – t

Where, ‘t’ is the machining time in this instance. So the new tool life is as given by

the equation 6.2

In reality the tool has undergone accelerated wear due to change in machining

environment denoted by the spike in the load signal and therefore the corrected value

of tool life can be given by

Time

Load

x

a

t

Load Signal

Equation 6.2


T = T – (t + z)

Where, z is a correction value can be mathematically expressed as

z = f (x , a)

z is function of x and a where

x is the time for which the tool undergoes accelerated wear

a is the amplitude of the load signal.

The dsPIC based A/D can be used to compute the total work done in the by the tool

during time ‘t’ which needs to be converted to equivalent time. Research needs to

establish this Mathematical Correlation given in equation 6.4.

Equation 6.3

Equation 6.4


7. Communication Network and Information Management

The work described in this project forms one of the new nodes in the IPMM

Architecture. This proposed new node is as shown in figure 7.1

Fig 7.1 IPMM Architecture with the Tool Monitoring Node [10]

The Tool Monitoring Module can be integrated into the existing dsPIC30F6014

Microcontroller and can thus become a virtual node. This will be particularly

important where the cost of the Tool Monitoring System is required to be as little as

possible. However this would compromise the data handling capacity of the entire


system. Therefore the quality of the machining process expected would determine

weather or not an additional node is required to be implemented.

7.1 Monitoring Module and Central Server Communication

A two way read/write communication is required between the Tool Monitoring

Module and Central Server. The Monitoring Module reads the spindle load data from

the CNC machine controller as shown in figure7.1. Figure7.2 shows the data

communicated between the Central Server and the Tool Monitoring Module.

Fig 7.2 Communication between Monitoring Module and other elements


The central server holds the tool life data for every tool. This tool life information

includes the maximum available tool life for each tool and the current usage level.

This information is relayed to the Monitoring Module when the appropriate tool

number input is selected on User Tool Selector Switch.

The User Tool Selector Switch is a simple multiple way rotary selector switch. The

user needs to select the appropriate switch based on which tool is being used. In a

CNC machine with automatic tool changer and multiple tools on a tool magazine /

carousel, the tool information can be directly obtained from the CNC Tool Library.

7.2 Information Management System

A database on the on the Central Server maintains the current tool life of every tool on

every machine. The tool life values in the database are updated after every cycle by

the Tool Monitoring Module as shown in figure7.2. The data base can also be

designed to store other fields like Date, tool life used on every cycle etc, which could

be used for diagnostic purposes in case of material hardness variations and also to

evaluate economics of machining. Figure7.3 shows a typical database which could in

built either in MS Excel or MS Access or any other advanced database management

packages.


Fig 7.3 Tool Life Database on Central Server

The front end tool serves as a HMI and can be linked up the database. A simple two

screen front end tool developed in MS Word Visual Basic Macro developer is as

shown in the figure7.4 and figure 7.5.

On running the information management system, the dialog box shown in the

figure7.4 would open and prompt the user to input the machine number. Following

which the dialog box to all the tool life information will be displayed.


Besides these basic operations, there could be facility to extract information from the

database in MS Excel spreadsheet.

Fig 7.4 Front End HMI Screen on the Central Server (Screen 1)

Fig7.5 Machine Specific Tool Life Information with Tool Utilization Chart (Screen 2)


8. Conclusion

The experiments undertaken during the project helped establish that the cutting

force data was not suitable for monitoring the tool life because it was complicated

to analyse. The shortcomings in the form of high cost and workpiece size

restrictions meant it was unsuitable for low cost industrial applications.

The spindle motor current data had only one single plot and thus it was less

complicated in comparison to the cutting force approach. The experiments also

showed that there was a noticeable increase in the spindle motor current when the

cutting was initiated, this could be used or monitoring purposes. However there

was still the problem of high data volume with the spindle motor current

approach.

The problem of high data volume was resolved by employing a data compression

system at the source. The laboratory experiments showed that the dsPIC30F6014

was capable of reducing the data vo lume. The dsPIC30F6014 was also found to be

capable of converting analogue signal to digital equivalent at appropriate sampling

rates for accurate and efficient monitoring.

It would be possible to integrate the tool monitoring system into the IPMM

architecture. Investigation was also led into possible integration scheme and

information management system to efficiently transfer data from the central

machine server to the tool monitoring module.


9. Further Work

The dsPIC code presented in this literature is only a basic A/D converter module.

Further work needs to be done in order to obtain accurate tool life information.

Following improvements are required to enhance the effective operation of the

algorithm:

Ü Delay using while loop need to be replaced with the timer based delay using

one of the internal timers within the dsPIC30F6014. This will facilitate better

control on the data acquisition and conversion and thus improve accuracy

Ü The code needs to be further developed on communicate with the standalone

PC / Central Server. This can be established using the UART peripheral

functions available in the dsPIC30F6014.

Ü Real-time communication between the dsPIC30F6014 and the Central server

needs to be established which does not exist in the current code.

Ü The code need to be upgraded to obtain sampling rate information from the

user.

The mathematical correlation between the spindle load and the tool wear needs to be

established.

For more accurate tool life monitoring systems, the spindle motor current signal can

be used in association with the Acoustic Emissions to reflect a better image of the

actual tool wear. This would give a realistic indication of the work done by the tool in

cutting operation [15].


References

1. Fletcher, P. 1995. An Introduction to Basic Milling. Watford: EnTra

Publications.

2. Rufe, P.D. 2002. Fundamentals of Manufacturing. Dearborn: Society of

Manufacturing Engineers.

3. Boothroyd, G & Knight, W.A. 1989. Fundamentals of Machining and

Machine Tools.2nd ed. New York: Marcel Dekker, Inc.

4. Alauddin, M. El Baradie, M.A. Hashmi, M.S.J. 1995. Tool- life testing in the

end milling of Inconel 718. Journal of Materials Processing Technology

Vol.55, pp321-330.

5. Kennametal Inc. HEC4-Flute - Conventional Lengths Finishing End Mill

http://www.kennametal.com/en/e-

catalog/ProductDisplay.jhtml?XMLArg=3675.xml&id=3675&level=&pid=50

52283&navAction=push&item=category%3A3675

[Accessed: 20 February 2008]

6. Dorf, Richard. C & Kusiak, Andrew. 1994. Handbook of Design

Manufacturing and Automation. New York: John Wiley & Sons.

7. Kalpakjian, S. 1928. Manufacturing Processes for Engineering Materials. 5th

edition. New Jersey: Pearson Education Inc.

8. SIEMENS Sinumerik 802D Milling Programming and Operating Manual,

pp.279-280

9. Fanuc Ltd. FANUC Robodrill a- i series Intelligent Control.

http://www.fanuc.co.jp/en/product/robodrill/intelligence/intelligence.html#tool

[Accessed:15 December 2007]


10. Prickett, P.W., Grosvenor, R.I. 2007. A Microcontroller-based milling process

monitoring and management system. Proc. IMechE Vol.221, Part B: J.

Engineering Manufacture, pp357-362

11. Choudhry, S.K., Rath, S, In process wear estimation in milling using cutting

force method. Journal of Materials Processing Technology, Vol. 99, no. 1-3,

2000, pp113-119.

12. Oraby. S, E., Hayhurst. D.R., Development of models fo r tool wear and force

relationship in metal cutting. International Journal of Machine Tools and

Manufacture. no.33. 1991

13. dsPIC30F Family Reference Manual. Microchip Inc.

http://www.microchip.com/stellent/idcplg?IdcService=SS_GET_PAGE&node

Id=1999&ty=&dty=&section=&NextRow=&ssUserText=dsPIC30F%20manu

al

[Accessed: 10 March 2008]

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http://www.microchip.com/stellent/idcplg?IdcService=SS_GET_PAGE&node

Id=1999&ty=&dty=&section=&NextRow=&ssUserText=ICD

[Accessed: 14 March 2008]

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Machine Tools and Manufacture. Vol. 39. no 10. 1999. pp 1525-1538.


Appendix 1 – Typical output file from Dynoware

File Type: Path: C:\Kistler\DynoWare\Data\Projects0708\ Filename: test2311-1.dwd Config ID: test2311-1.cfg Setup ID: 0 Manipulated: 0 Filename 1: Filename 2: Date: Time: Sampling rate [Hz]: 1000 Measuring time [s]: 20. Delay time [s]: 0. Cycle time [s]: 0. Cycles: 9 Samples per channel: 20001 Cycle interval: 0 Channel enabled: 1 1 1 Cycle No: 3 Time [s] Ch1 Ch2 Ch3 0. -0.244141 2.92969 -3.20435 1.e-003 3.17383 2.38037 -4.42505 2.e-003 2.62451 0.976563 -3.96729 3.e-003 3.90625 -6.10352e-002 -4.11987 4.e-003 6.46973 0.610352 -4.42505 5.e-003 5.98145 1.28174 -3.66211 6.e-003 5.79834 1.64795 -3.35693 7.e-003 7.01904 2.68555 -3.35693 8.e-003 5.7373 3.17383 -3.20435 9.e-003 4.57764 2.92969 -3.96729 1.e-002 5.12695 3.11279 -4.11987 1.1e-002 3.90625 2.92969 -4.11987 19.997 22.2778 -9.64355 -10.0708 19.998 10.0708 -4.69971 -8.39233 19.999 -1.89209 2.80762 -6.71387 20. 6.10352e-002 5.06592 -6.2561


Appendix 2 – Typical output file from Spindle Load Signal

Frequency (Hz) 2000

Cal 5V Cal 0V Spindle Speed

Spindle Load

1053 26 109 78 1050 26 113 67 1050 27 112 74 1050 26 110 68 1050 26 112 74 1047 27 107 75 1052 26 110 71 1046 27 114 65 1049 26 110 76 1047 27 106 67 1047 27 111 78 1047 26 110 70 1045 26 112 77 1050 25 113 72 1048 26 108 70 1045 27 117 67 1047 26 109 71 1050 26 111 78 1052 26 111 80 1049 26 112 76 1047 26 116 69 1046 26 107 81 1045 27 118 73 1049 26 117 73 1048 26 114 73

1048 26 109 74 1048 26 109 62 1047 26 99 81 1045 26 109 101 1048 26 103 91 1050 26 100 83 1046 26 109 80 1051 26 102 84


Appendix 3 – dsPIC® ADCON register datasheet

Final Year Thesis

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