RESEARCH REPORT Project No. 14

AN INVESTIGATION OF FACTORS AFFECTING THE MACHINABILITY OF DUCTILE CAST IRONS

MATERIALS PROCESSING RESEARCH GROUP, SCHOOLS OF MECHANICAL ENGINEERING AND CHEMICAL ENGINEERING (METALLURGY PROGRAM)

THE GEORGIA INSTITUTE OF TECHNOLOGY ATLANTA, GEORGIA

/ DUCTILE IRON A

A STUDY SPONSORED BY THE DUCTILE IRON SOCIETY FOR USE BY MEMBERS ONLY.

OCTOBER 1987

SUMMARY STATEMENT By: Lyle R. Jenkins Robert J. Warrick

BACKGROUND: 1. Cupola melt with sandwich treatment. Users of ductile iron castings who have more than 2. Coreless induction melt with sandwich one supplier have been aware of wide differences treatment. in machinability of the products from different 3. Coreless induction melt with in-mold treat- suppliers as well as changes in machinability of ment. castings from the same producer day to day and piece to piece. Cost reduction is achieved by machinability improvement and consistency which is a requirement for automated machining lines and high production. Hardness is not a general indicator of machinability, but serves to qualify castings within each type of process. All too often castings with similar hardness measurements show significant differences in machinability.

PURPOSE: The Ductile Iron Society recognized the need to determine the effect of casting microstructure on machinability and funded a research project at Georgia Institute of Technology. The effects of surface or sub-surface discontinuities, such as chilled edges, slag inclusions, and burned-on sand are well recognized and not considered in this study. The overall purpose is to improve the machinability and consistent high quality of duc- tile iron.

EXPERIMENTAL APPROACH: A steering committee of ductile iron users and producers experienced in high production machining of ductile iron was organized and met with the Georgia Tech staff to observe and advise the initiation and progress of the project. The committee consisted of:

A. Alagarsamy Grede Foundries J. Berry Georgia Tech. S. Carter ACI PC0 L. Jenkins DIS L. McFarland Caterpillar Tractor Co. K. Millis DIS T. Prucha CMI-NOREN, Inc. D. Rhoda Dana Corporation R.J. Warrick lntermet Corp. D. Weise Caterpillar Tractor Co.

The test specimens were 18 inch long hollow cylinders, 8 inches in diameter and having either 3/4 or l/2 inch thick wall sections. They were pre- machined prior to the start of data collection to eliminate effects of surface variations. balance.

Since the objective was to correlate castings microstructure variations with machinability, fixed machining conditions were used for most of the work. Standard titanium carbide coated, complex carbide tools were used for dry, single point cutting. Cutting conditions were: 800 surface feet per minute; 0.01 in. depth of cut per revolution feed with a negative 10 degrees true rake angle and a positive 10 degrees flank clearance angle. The machinability index was based on the amount of tool wear after 120 seconds of con- tinuous dry cutting. These cutting conditions were selected after extensive investigation of conditions necessary to avoid excessive edge build-up, provide a clear wear scar without fracture, provide consistently reproduceable results, and take the tools into second stage (steady state) wear - for both ferritic and pearlitic castings - within 2 minutes. Well into the program, it was found that a third stage of accelerated wear occured above 80% pearlite. From the data col- lected, computer aided regression analysis was per- formed to obtain the desired empirical equations.

CONCLUSIONS: 1. Microstructure is the best indicator to predict

machinability.

2. Tool wear correlates strongly with volume frac- tion pearlite Vlvp and the volume fraction graphite Vlvgr provides some improvement in the overall correlation. The empirical equations for the total wear VIB were found to be:

VIB = 0.10 + 0.35 Vlvp R = 0.89 VIB = 0.16 + 0.375 Vlvp - 0.71 Vlvgr R = 0.91 R-Multiple Correlation

This means that tool wear increases with increas- ing amount of pearlite. Very rapid tool wear oc- curs above 80% pearlite, much of it due to fine pearlite from fast cooling rate. (Shake-out too fast). The reduced tool wear with increased volume of graphite must be treated carefully. In- creased graphite can also reduce fatigue strength and toughness before it shows an affect on ten- sile properties.

and interrupted cuts. The castings ~ e r e ' ~ r o d u c e d 3. Hardness (BHN) is not a reliable method to deter- to cover the range of microstructures typically mine machinability. Microstructure constituents found in grades 80-55-06 and 65-45-12. Phase I which reduce machinability and increase tool cylinders were produced by three member foun- wear are not always detected by the Brinell dries using different production methods, namely: harness test. The low volume pearlite increases

hardness, but may not increase tool wear as much as non-metallic inclusions and intercellular segregates.

I. Abrasion appears to be the dominant wear mechanism on the flank of the tools, both during running in and the steady state wear stages of the tool life cycle. The main abrasive inclusions in ducti le iron castings are compounds of magnesium, silicon, and aluminum which occur in the cell boundaries and as inclusions throughout the grains of ferrite and pearlite.

5. Significant variations in castings microstructures were found to occur from either surface to the center core of both the 3/4 and 11/2 inch wall cylinders. Such variations must be considered in evaluationg the effect of microstructure on the machinability of castings. The microstructure near the surface of both the outside and the in- side cored surfaces may be low pearlite content and low hardness. Because they solidify faster, they can contain less intercellular segregation than the slow cooled heavy section in the center and show better machinability. The slow-cooled center of a heavy section can contain more pearlite, more segregation in the cell boundaries with or without carbides, and have poor machinability. Tool wear, in general, was less in the surface areas than in the center.

6. No significant difference in tool wear was detected as a result of variations in silicon con- tent of castings from 2.1 % to 2.86%. The change in ferrite hardness, as a result of silicon content, did not have a detectable effect on machinability.

7. Major variations in tool life were found between lots of cutters ordered from the same supplier to the same specification. Both the foundries and users need to be aware of potential life variations in cutters purchased to the same specification.

8. Best machinability in ductile iron is attained by:

a. Sufficient carbon to produce a uniform distribution of graphite nodules with no micro-porosity.

b. Castings which have sufficient cooling time to permit intentional pearlite to partially spheroidize.

c. Using clean charge materials in melting and separating insoluble materials (slag off pro- perly) before treatment to avoid inclusions.

d. Controlling nucleation and inoculation to minimize intercellular segregation during solidification of heavy sections and light sec- tions. Intercellular segregations can have car- bides and precipitates or no carbides and still have precipitates.

e. Correcting the iron by removing the effects of holding time, excessive oxidation, and super- heating temperatures.

f. It has been shown in the literature, and from experience, that the elimination of in- tercellular porosity, carbides, inclusions, and intra-granular inclusions significantly im- proves toughness and fatigue strength.

9. The results of Phase II are not published in this report because the irons used were not produc- tion irons and the results can be confusing. They have been reported and are on file at DIS. A copy can be obtained by contacting DIS headquarters.

TOOL WEAR MECHANISMS. Tool wear mechanisms are discussed in the body of this report. Additional detail is presented in the Georgia Institute of Technology PhD Thesis of F. Salame-Lama. Dr. Salame-Lama was the principal stu- dent investigator for this DIS project and used data generated from the DIS machinability test castings for part of the analysis of tool wear mechanisms presented in his thesis.

I. INTRODUCTION Machinability research has, in general, often been of a qualitative rather than a quantitative nature, the prin- cipal reason being the very large number of variables present in actual machining operations. These variables may be classified into the following categories:

(a) Machine Set-up variables; such as cutting speed, feed, depth of cut, and tool geometry.

(b) Casting material variables; such as major phases present with their volume fractions and mor- phology, and inclusions with their type, quantity, and distribution.

Interactive effects between the machine set-up variables and casting materials variables may also be present. For example, at high cutting speeds some in- clusions of the more refractory type such as silicates may be very detrimental to tool life. However, at low speed they may not be as harmful.

From the above, one may readily appreciate why few truly quantitative results have been accumulated in the field of machinabiltiy. In a recent review of previous research, much work was seen to be of a qualitative nature (1).

The present project objective was to obtain a machinability index for ductile irons as a function of microstructure. This aim was later broadened into ob- taining an estimate and a probability distribution func- tion of tool wear under specified cutting conditions, with the eventual possibility of having the results ex- tended to other cutting conditions.

Another aim of this work has been, at the same time, to study the effect of different treatment processes in- volved in the production of ductile iron on machinabili- ty. Consequently, material produced by three member foundry organizations using different meltingltreat- ment combinations were involved in this investigation.

The main effect of treatments that would be expected is on type, quantity, and distribution of inclusions which may significantly affect machinability. Reference to the literature review may be made for af- fects of inclusions on machinability (1,2).

It. EXPERIMENTAL APPROACH The direction taken in the present research was of an experimental nature and involved tool wear tests utiliz- ing standardized ductile iron castings.

Certain preliminary work was performed to establish a suitable experimental set-up. In so doing, several potential problems entered into the picture, principally due to the formation of build-up on the flank side of the tool (as shown in Figure No. 1) which frequently covered the wear scar and prevented the quantitative assessment of tool wear. This is discussed further in Appendix A.1. As a result of this work, cutting condi- tions and tool materials were selected that essentially solved the flank build-uplwear scar problem.

The particular approach adopted for this project was to obtain data of tool wear, and the microstructure associated with it, under specified cutting conditions, i.e., tool wear was the dependent variable correspond- ing to the independent microstructural variables. From this data, computer-aided regression analysis was per- formed to obtain the desired empirical equations.

The experiments (see Appendix A.l) consisted of turn- ing thick-walled ductile iron cylinders supplied by three foundries utilizing different production techni- ques, namely:

CODE CS - cupola melted with sandwich treatment meth-

od of nodularization. ES - coreless induction furnace melted with sand-

wich treatment of nodularization. El - coreless induction furnace melted with in-

mold method of nodularization.

Two wall thicknesses, 3~ and 1 l/2 inch, (shown in Figures 2 and 3) were studied. The cylinders used covered a wide range of microstructural matrices from predominantly ferritic to pearlitic. Table I shows the data for the chemical anaylsis, hardness, tool wear, and volume percent pearlite.

The cutting tool used was: Carboloy TNMG 544 E-GR 516, a titanium carbide coated, complex-carbide tool. The cutting conditions used were as follows:

True rake angle . . . . . . . . . . . . . . . . . . - 10 degrees Flank clearance angle. . . . . . . . . . . . . + 10 degrees

Cutting speed . . . . . . . . . . . . .800 sfpm (264 smpm) Depth of cut . . . . . . . . . . . . . . . . . . . .0.1 in. (2.5 mm) Feed . . . . . . . . . . . . . . . . .0.01 in.lrev. (0.25 mmlrev) Cutting lubrication.. . . . . . . . . . . . . . . . . . . . . . .Dry Cutting time . . . . . .I20 secs. (continuous cutting)

The tool holder used was Carboloy PTBNR-85-5, which provides a 5 degree negative rake and a 5 degree flank clearance angle. It was ground at Georgia Tech to pro- vide a 10 degree flank clearance angle and a 10 degree negative rake. The average flank wear land VIB was adopted as a quantative measure of tool wear (This is shown in Figure No. 4). VIB was found by utilizing a pro- cedure involving a tool maker's microscope and an im- age analysis machine (See Appendix A.2 on tool wear). The cylinders were premachined to provide balance for high speed turning. Each cut was undertaken at a known depth in a cylinder and was associated with the microstructure of that cylinder at that depth. The microstructure evaluations were obtained by perform- ing quantitative metallographic analysis on two through-thickness specimens from cylinders and subsequent plots were made for the microstructural variables VIVg, VIV@, and Nlg versus depth for every cylinder. (See Appendix A.3 on QM analysis)

VlVg =volume fraction of graphite VIV =volume fraction of ferrite Nlg = nodule count of graphite

About seventy-five data points were collected in this manner and used in the regression analysis. From this analysis the following empirical equation was obtain- ed: (See Appendix A.4 on Regression Analysis)

VIB = 0.155 + 0.3VIVp - 0.5VIVg Multiple correlation . . . . . . . . . . . . . . . . . . . .0.9051 Standard Error of Estimate. . . . . . . . . . . . . . .0.0355 F Ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .133.75

The residuals behaved as a normally distributed ran- dom variable.

Ill. RESULTS OBTAINED A. The development of a procedure for quantitative

machinability studies in general, and ductile iron in particular.

B. The determination of a quantitative machinability indexlmicrostructure relation.

C. The determination of an equation that presents tool wear as a normally distributed random variable with a known variance and estimated mean, under the selected cutting conditions. (This latter may be the most important result; since a true optimization of machining processes in a large manufacturing concern requires not only the estimate of mean tool life but also the over-all variations in tool life). All factors of tool life must be looked at, not just the mean.

IV. DISCUSSION AND CONCLUDING REMARKS

It is clear that the results of this work agree basically with what is already known in general terms: tool wear increases with increasing volume fraction of pearlite and decreases with increasing volume fraction of graphite. However, a quatitative expression has been obtained for the specific cutting conditions used.

Since the volume fraction of graphite in ductile irons does not vary as much as the volume fraction of pearlite, which may vary from zero to 100 percent, it is relevant to study wear as a function of volume fraction of pearlite. A plot of tool wear (VIB) versus volume frac- tion of pearlite (VIVp) is presented (See Figure No. 5). The volume fraction of pearlite has the highest correla- tion with tool wear (0.8972), It is also clear from Figure No. 5 that the volume fraction of pearlite is the single most important variable affecting tool wear with a linear relationship up to about 80 percent volume frac- tion pearlite for the selected cutting conditions leading to 0.016 in. (0.4 mm) of average flank wear. Above 80 volume percent pearlite, there is significantly higher tool wear. Two possible explanations have been presented for this phenomenon:

1. Above a certain volume percent pearlite, the re- maining ferrite is not sufficient to form any build- up that would subsequently act as'a protection for the tool. Hence, accelerated wear is seen to occur. The threshold for volume percent ferrite probably is about 10 percent for the condition considered.

2. The machining of ductile irons with 80 percent volume of pearlite, using the previously mention- ed cutting conditions, would be expected to lead to a tool life of 120 secs. The machining of an iron with larger volume percent pearlite values would thus result in using the tool beyond its expected life, at which time much higher thrust forces are seen to act on the flank edge. Thus, accelerated wear is liable to occur rapidly.

Reference to Figures 6 and 7, which plot cutting and thrust forces during a tool life experiment, in- dicates that beyond a certain point in the testing, a change in behavior of the thrust force occurs and a steady increase results; an occurrence which invariably led to stalling of the lathe during the present experiments.

The volume percent pearlite is the most significant fac- tor affecting tool wear because it becomes the hardest major microstructural component and conditions which promote the formation of pearlite may also result in other forms of combined carbon which are detrimental to tool life. Insufficient cooling time resulting in fine pearlite is a common source of ex- cessive tool wear. It may be detected by Brinell hard- ness testing.

presented for tool wear. Such information could well be used for a time optimization of the machining pro- cess concerned.

In this connection it is important to note finally that the work reported presents a basis for obtaining similar results for different cutting conditions, tool material or possibly other work material which could eventually be used in practice.

V. RECOMMENDATION FOR FUTURE WORK 1. Study the effect of nodularization procedure on

machinability.

2. Develop empirical equations that include the cut- ting conditions as variables. This may be achiev- ed by running experiments similar to those per- formed in this work, with variable cutting condi- tions.

3. Study quantitatively the effect of inclusions and provide an "inclusions" factor in the empirical equation. This would require developing a techni- que for the quantitative assessment of inclu- sions.

4. Investigating flank build-up and its effect on tool life and surface finish.

VI. CONTRIBUTING PERSONNEL Project Consultants:

Professor E. E. Underwood - Quantitative metall- ography.

Professor A.A. Radwan - Machining conditions, tooling and machina- bility aspects.

Instructor C.W. Meyer - Machinability testing and assessment.

Data Collection:

Graduate Students:

F. Salame-Lama A. Saigal (preliminary work)

Undergraduate Students:

Mark Kurzenhauser Jim Bowen Wajdi Abu Khreibeh Catherine Adams John Jordan Steve Sheetz

Machinability Tests:

Graduate Students:

F. Salame-Lama A. Saigal (preliminary work)

Undergraduate Students:

Mark Kurzenhauser John Jordan Jim Bowen

An important feature of the present results is that both an estimate and a probability distribution are

Statistical Analysis: VII. REFERENCES Graduate Student: 1. J.T. Berry, F. Salame-Lama, and A. Saigal, "A

Review of the Literature of the Machinabilityof F. Salame-Lama Cast Irons". Prepared for the Ductile Iron Society,

Report Preparation: October 1981. F. Salame-Lama 2. J.T Berry, F. Salame-Lama, and A. Saigal, "The J.T. Berry Machinability of Cast Irons - A Review". Paper

Report Review: presented at International Conference on the In-

C.W. Meyers fluence of Metallurgy of the Machinability of

A.A. Radwan Engineering Materials, American Society for E.E. Underwood Metals, September 1982, Chicago.

Manuscript Preparation: 3. A. El-Hakeem, T. El-Gammal, W. Konig, Y. Kabil,

T. Parise and M. El-Salamone, "The Effect of Mg Treatment on the Machinability of Spheroidal Graphite Cast

Coordination and Supply of Test Pieces: Iron". Proc. First Cairo Univ. Conf. on Mech., R.J. Warrick (Lynchburg Foundry and Chairman Design and Production, Paper Tech-1, 1979, pp.

of monitoring committee) 1-12.

Test Casting Suppliers:

Grede Foundries, Inc. Lynchburg Foundry Company Wagner Castings Company

Bu~l t -up

edge

Figure 1 - Scanning Electron Micrograph of Sec- tioned Carbide Tool, Showing Rake and Flank Face build-up. The build-up covers respectively the crater (on rake-face or upper part of the tool) and the flank-wear (on the flank or lower part of the tool). x250

Figure 2 - 1.5 inch cylinder.

Figure 3 - 0.75 inch cylinder.

7

Flank b- VB' Wear l a n d

Figure 4 - Section through tool showing flank wear land. Shad- ed area is work volume.

I I I I 1 I

1 5.0 30 45 60 75 90

VOLUME PERCENT PEARLITE

Figure 5 - Flank wear vs. volume percent pearlite for phase I cylinders.

Figure 6 - Cutting force vs. time 800 sfpm.

10 secs. -

Figure 7 - Cutting force vs. time.

10 s e c s . N

&Force l b s .

Cutting force Y

Cylinder "A" , ES

Cutting speed: 1000 sfpm Thrust force -4

1 20 l b s .

-

Feed : 0.01 inchlrev.

Depth of cut: 0 . 1 inch

Tool: TNMG-5 44E- GR5 16

9

11 Time s e c s .

APPENDIX A.l EXPERIMENTAL FACILITY

The tool life tests were conducted on a 10HP Spring- field lathe. Tool forces were measured with a Kiag Piezo-electric transducer connected to a Kistler charge amplifier and a Hewlett-Packard two-axis recorder. Hollow ductile cast iron cylinders were supplied by Lynchburg Foundry, Wagner Castings, and Grede Foundries.

Set-up castings were produced in the Lynchburg Research Foundry during the gating and risering development phase of this project. Four 3/4 inch (19 mm) wall thickness castings were received in January 1981 for set-up purposes. The melt charge consisted of 60% F-1 Sorel pig, 3% steel, and the remainder ductile iron scrap plus ferro-alloys. The metal charge was melted in a 1500 Ib. coreless induction furnace. Magnesium treatment was by the pour-over method.

Four 11/2 inch (38 mm) wall castings were shipped in May 1981. The charge consisted of 78% F-1 Sorel pig, 4% steel, and ductile iron scrap plus ferro-alloys. The metal charge was melted in a 1500 Ib. coreless induc- tion furnace. Magnesium treatment was by the pour- over method. These castings showed some shrinkage and were not of typical production quality and hence were not used for full-scale machinability testing, but were used for further intial set-up, flank build-up studies and cutting force studies.

Production Castings. 1. Five castings with inch thick walls and four

castings with 1 l/2 inch thick walls, all being 8 inches in diameter and 18 inches long, were shipped in November 1981. The charge was melted in a coreless induction furnace and magnesium treatment was by the in-mold method (Code El). The castings were of pro- duction quality and accordingly were used in the machinability studies. The castings were numbered 2(B), 4(B), 6(B), and 8(B) where (B) corresponds to 1 l/2

inch wall thickness. (See Table A.l)

2. Four 11/2 inch wall thickness castings were ship- ped March 1982. These were poured on a production line with normal production iron. The melting process is coreless induction furnace and the sandwich method was used for the magnesium treatment (Code ES). (See Table 14.1)

NOTE: All cylinders were cast and machined as shown in Figures A.l-1 and A.l-2. Test runs were made on a continuous basis and according to cutting conditions mentioned previously.

For the machinability data used in obtaining the empir- cal equation, a two-minute cut was performed at a known depth and with a new cutting edge for every data point. This was done to permit associated tool wear for a certain cut with the microstructure at that location.

A limited number of force scans were also undertaken from 200 to 1000 sfpm (60-300 smpm). In some ex- periments the same cutting edge was used for the scan from 200 to 1000 sfmp (60-300 smpm), i.e., repeating the scan in reverse order to permit reducing the effect of tool wear on cutting forces while conserv- ing cutting tools. Other force scan experiments were performed using a new cutting edge for every speed. No significant difference in cutting forces were observ- ed between the two procedures.

3. Six 3~ inch and four 1% inch thick wall castings were shipped March 1982. They were cast from cupola melted iron which was magnesium treated by the sand- wich method (Code CS). Three 3/4 inch wall castings had about 0.3% copper present. Two 11/2 inch wall castings had about 0.6% copper present (See Table A.l)

TABLE A.l

CODE CASTING NO. WALL THICKNESS CHEMICAL ANALYSIS

(Wt. %)

El 2B1 4B 1.50 in. 3.71 2.21 0.32 0.055 0.28 0.042 0.032 0.29 0.036 0.020 0.031 0.007

El 6B1 8B 1.50 in. 3.78 2.31 0.32 0.055 0.53 0.042 0.032 0.28 0.036 0.021 0.031 0.007

ES B 1.50 in. 3.56 2.61 0.31 0.022 0.15 0.049 0.01 5 ND ND 0.032 ND 0.005

ES C 1.50 in. 3.68 2.83 0.31 0.031 0.095 0.040 0.01 3 ND ND 0.030 ND 0.007

BHN

% PEARLITE VlVP TOOL WEAR VIB

CODE CASTING NO. WALL THICKNESS CHEMICAL ANALYSIS C

(Wt. %) S i Mn Cr Cu

Mg P Ni Mo A l Ti S

CS W 0.75 in. 3.51 2.59 0.54 0.060 0.31 0.042 0.217 ND ND 0.028 0.010 0.009

CS X 0.75 in. 3.49 2.47 0.53 0.059 0.15 0.041 0.022 ND ND 0.027 0.01 0 0.008

CS Y 1.50 in. 3.42 2.50 0.54 0.060 0.63 0.034 0.022 ND ND 0.027 0.010 0.008

CS z 0.75 in. 3.49 2.47 0.53 0.059 0.15 0.041 0.022 ND ND 0.027 0.01 0 0.008

BHN

% PEARLITE VlVP TOOL WEAR VIB

Figure A.l-1 3/4 inch wall cylinder

18"

114" -=

1 12" - '-7

-- - lr-

Cast n o t c h on t h i s end

7 112" A

P - - -

8" OD

-- -----

--

\ 118" x 45' chamfer

125 microinches 125 microinches

i n c h Figure A.l-2

w a l l c y l i n d e r

APPENDIX A.2 TOOL WEAR ANALYSIS

The average depth of the flank wear land was used as a quantitative measure of wear. The reason for this was based upon the fact that flank wear appears to be one of the principal causes of tool failure when machining ductile cast irons with carbide tools. This was further justified in a set of tool life experiments performed at different speeds. Here it was observed that a sudden increase in both flank wear and thrust force occurred after about 0.3 to 0.4 mm of flank depth. In view of the catastrophic nature of the wear observed, it was con- cluded that flank wear was the controlling factor in tool failure.

Quantitative assessment of tool wear was originally performed by analyzing scanning electron microscope micrographs of the tool. Although this method was deemed reliable, another method was eventually adopted that was quicker and that can be more readily performed in other (industrial) laboratories. This se- cond approach involved obtaining a magnified contour of the flank wear land using a tool-maker's microscope. The resulting plot is then analyzed on an automatic image analysis machine to give the area and average depth of the flank wear land. This method prov- ed to be efficient and reliable in obtaining quantitative values of tool wear. A typical plot of the wear contour is presented in Figure A.2-1.

Figure A.2-1 Typical wear contour plot.

All. units in 111000 inch.

APPENDIX A.3 significant variation in graphite volume fraction was QUANTITATIVE METALLOGRAPHY detected either through the wall or along the length.

Quantitative microstructural (QM) analysis was per- Specimens at the end of the machine surface, towards formed using optical and automatic equipment. the chuck end, can be used to analyze the microstruc-

Two through-wall samples were taken from the ture of the cylinder casting.

castings that were analyzed, as shown in Figure The QM data collection involved duplicate operators in A.3-l(a). order to ensure repeatability of results. Volume frac-

It was not possible to take specimens from the area that was previously machined away in tool-life testing and hence it was necessary to study microstructural variations in an unmachined casting. This was per- formed in the preliminary studies; casting number 6 supplied by Lynchburg Foundry was selected for this purpose.

The specimens were chosen from the locations as shown in Figure A.3-l(b). Specimen 640 was selected to study the microstructure through the wall of the cylinder, the remaining specimens cut for the study of microstructure along the machining surface. Volume fractions of graphite (Vlvg)O, ferrite (Vlva), as a function of depth for specimen 640 were obtained. Average values for Vlvg and Viva were also obtained for the re- maining specimens.

A statistical analysis of variance (ANOVA) of the data was performed to determine whether there was a significant variation in Vlvg or Vlva through the depth of the sample and along the machining surface at the different locations. As a result of the analysis, it was concluded that there is a significant variation of the volume fractions of ferrite and pearlite through the thickness of the wall of the cylinder, and a less signifi- cant variation along the length of the casting. No

tions of ferrite and graphite were obtained by point counts of mounted metallographic specimens using an optical microscope. Nodule counts were also done using a manual optical microscopy method. Graphite volume fraction and nodule count data were addi- tionally obtained using a Bausch and Lomb Feature Analysis system.

Pearlite volume fraction was obtained by subtraction: Vlvp = 1 - (Vlvg + Vlva).

Other microstructural parameters could be obtained from the raw data and used in obtaining an empirical machinability equation, however it is always necessary from a statistical standpoint to limit the number of in- depended variables to obtain meaningful results that may be generalized. Accordingly, the parameters that were selected as most significant were: Vlvg, Vlva, Vlvp, and NlAg (NIAg is the nodule count of graphite). Results of the regression analysis justified this choice.

Figures A.3-2 through A.3-11 show plots of microstruc- tural parameters through the wall thickness for the cylinders used in obtaining regression equation.

Figures A.3-12 through ~.3:19 show the microstruc- tures of specimens cut from the production castings used in this study.

, Machined away material / ,Specimen 1

/ /'

Cope section /'

/

Figure A.3-l(a) Location of specimens selected for QM analysis.

Parting line

Drag section

Figure A.3-l(b) Location of specimens used form microstructural variability study.

Figure A.3-2 - Microstructural Parameters for Cylinder El 2(B) (NL = 3.1 mm-l)

I..

' ' ' ' l l l t l l l n + I I I t ~ O Depth

0.0 10.0 2 0 . 0 3 0 . 3 413.0 in m

Figure A.3-3 - Microstructural Parameters for Cylinder El 4(B) (NL = 3.53 mm - I )

Figure A.3-4 - Microstructural Parameters for Cylinder El 6(B) (N L = 3.53 mm - l)

Figure A.3-5 - Microstructural Parameters for Cylinder El 8(B) (N L = 3.53 mm - I)

Figure A.3-7 - Microstructural Parameters for Cylinder E S C (NL = 1.93 mm-l)

* Vvp 1 UB

.. .......... .......... .... ... I N y r nl

). ........., w - - '" b. .

!= L

.:, a o

El. CI 10.0 2 0 . \:I 3 0 . a -18.0 inrnm.

Figure A.3-8 - Microstructural Parameters for Cylinder CW-W (NL = 1.95 mm -I)

Figure A.3-9 - Microstructural Parameters for Cylinder CS-X (NL = 1.93 mm -I)

- - 1~ . 41 10.13 2 12 . 111 3 13 . 5 4D. Q in mm.

Figure A.3-10 - Microstructural Parameters for Cylinder CS-Y (NL = 1.97 mm-l)

Drp th

in mm.

Figure A.3-11 - Microstructural Parameters for Cylinder CS-Z (NL = 1.7 mm-l)

Unetched 100x

2% Nital 500x

Etched 2% Nital 100x

Code: Casting No: Thickness: Depth: Vol. Pearlite: Vol. Tool Wear: BHN Figure No:

El 2(B)

1.5 in. 21 mrn vvp.54 Vb.31

179 A.3-12

Vol. Pearlite: Unetched 50x Vol. Tool Weal

vvp.21 Vb.255 Unetched lOOx

2% Nital 100x 2% Nital 500x

Unetched 50x Vol. Tool We Vb.11 Unetched lOOx 5.7 mm

2% Nital 50x 2% Nital 500x

Unetched 50x Thickness: Vol. Pearlite: Vol. Tool We ar:

.75 in. Unetched 100x vvp.91 Vb.66

4% Picral 100x 4% Picral 500x

Code: Casting No: Thickness: Vol. Pearlite: Vol. Tool Wear: Depth: BHN Figure No:

CS X

1.5 in. Vvp.46 Vb.347 10 mm

187 A.3-17

Unetched 50x

Unetched 50x Vol. Pearlite: Vol. Tool Weal

Vvp.88 4% Picral 50x Vb.58

4% Picral 200x 4% Picral 500x

2% Nital 50x

Code: Casting No: Thickness: Vol. Pearlite: Vol. Tool Wear: Depth: BHN Figure No:

CS z

.75 in. vvp.25 Vb.12 5 mm

179 A.3-19

Unetched 50x

2% Nital 500x

APPENDIX A.4 REGRESSION ANALYSIS

number of independent variables of the regression equation.

The independent variables considered were VlVg, VIVa, 2. The multiple correlation between tool wear and NIAg, VIVp, and transformations of these variables: regression variables was acceptable with R = (VIVS)~, (VIVa)Z, (VIVP)~, (VIVa)3, (VIVp)3, (V1Va)-I, (V1Vp)-', 0.9051, and the Adjusted R-Square: 0.8132. (VIV~)", (V1Vg)O 3.

3. The standard error of the estimate of 0.0355 was The programs used were: considered low enough.

BMDPIR: Multiple Linear Regression. 4. The residuals, i.e., the errors between the ex- BMDP2R: Stepwise Regression. perimental value for tool wear and the predicted -

BPDP R: All possible subset regression. value from the empirical equation, behaved as a Gaussian random variable as shown in Figure A.4-3,

The above programs are from the Biomedical Corn- Normal Probabilitv Plot of Residuals. From the puter Programs P-series, developed at UCLA. The three figure, it can be observed that the points represent- above mentioned programs were used to optimized the ing residuals conform to the diagonal line that regression and select the most significant indepen- represent perfectly Gaussian residuals. The dent variables. Many equations were obtained, two of significance of this is that the deviation is mainly which will be presented. die to the inherent randomness in tool wear and

The data included in the regression analysis were operator error.

those that were run at the following conditions:

cutting speed: 800 sfpm (240 smpm) depth of cut: 0.10 inc. (2.5 mm) feed: 0.01 in.lrev. (0.25 mmlrev.) continous cutting time: 120 secs. cutting tool: TNMC-544E-GR516

The following equations are valid for these specified cutting conditions.

Equation (1): VIB = 0.172 + 0.296VIVp - 0.657VIVg + 0.37(VIVa)

Multiple correlation R = 0.9377 Standard error of estimate = 0.0419 F ratio = 162.577

See Figure A.4-1.

The above equation was based on all the data obtain- ed; however, the preliminary studies showed that after average tool wear land reaches a depth of about 0.4 mm, accelerated wear occurs. As it is desired to predict tool wear up to failure, another equation was obtained that predicts tool wear up to a VIB 0.4. The resulting equation:

Equation (2): VIB = 0.155 + 0.3VIVp - 0.5VIVg

Multiple correlation R = 0.9051 Standard error of estimate = 0.0355 F ratio = 133.75

See Figure A.4-2.

The advantage of equation (2) is that it has a smaller standard error for VIB less than 0.4 mm.

Equation (1) was selected as the best representation of tool wear versus microstructure for the following reasons:

1. It included two independent variables VlVp and VlVg (correlation between the two variables was -0.2717), as it is always desired to minimized the

Figure A.4-1 Expected vs. Experimental Tool Wear Plot

V =0.155+0.3V -Vvg lrltlt iple R - 0.505YP Standard error of estimate: 0.0355 F ratio: 133.75

v~ experimental

Figure A.4-2 Expected vs. Experimental Tool Wear Plot

0.035 VB = 0.172 + 0.296 Vvp - 0.657 V + - Multiple R - 0.9377 Vg Standard error of estimate: 0.0419

v~ F ratio: 162.577

experimental

FIG. A.4-3 .................................... ........................................ - - - - -.

Normal Probab5lity Plot of Residuals

APPENDIX B THE QUANTITATIVE ANALYSIS OF SLIDING

CONTACT WEAR AND AS RELATED TO TOOL WEAR Previous workers in the machinability field have generally studied a single facet of the tool-workpiece interaction, such as the influence of cutting condi- tions, microstructure, heat treatment, or inclusions content, etc., upon tool wear (5). Additionally, much of the work reported has been undertaken on low carbon steels rather than ductile irons. In this work, however, it was realized that cutting tool wear is a special case of severe sliding contact wear, where the true area of contact (AIT) approaches the apparent area of contact (AIA), as a result of the very high contact pressures and temperatures.

Sliding contact wear studies have shown that quan- titative analysis of the subject may be performed within the framework of Archard's (7) equation:

(B.1) VIL = K,WIH where:

V is the wear volume L is the sliding distance W is the normal load application H is the hardness of the wearing material in the vic-

inity of the contact

and where the non-dimensional quantity KI1 is a func- tion of the physical parameters influencing the wear process (see Figure B.1). Accordingly, the interpreta- tion of KI1 differs for the various mechanistic theories of wear. Equation (B.1) was developed for constant ap- parent area of contact applications. In tool wear, however, contact area is a function of the amount of wear, and hence varies during the life cycle of the tool. In developing a relationship equivalent to (B.1) that ap- plies to tool wear, the instantaneous wear rate VIL cor- responding to a given area of flank contact should be considered. Referring to Figure B.1,

V = % hVBb = % VB2bsina

OIL dVldL - bsina VB dVBldL AIA _= bV B

OIA E 1IAIA dVldL = dVBldL sina

Additionally the term W of Equation (B.1) is replaced by q = WlAlA (nominal pressure on the contact surface). Therefore the equivalent of Equation (B.1) that is ap- plicable to tool-wear on the flank is:

dVBldL = ( ~ : l s i n a ) ~ l ~

(B.2) dVBldL = K;~IH

Futhermore, for constant workpiece velocity:

dL = vlcdt

Equation (B.2) or (8.3) may be considered as the equivalents of (B.1) in cutting tool wear. Equation (8.3) may be rewritten as:

(B.4) (dVBldt*Hlq) = K

Therefore, three basic quantitites to be considered in tool wear analaysis are:

dVBldt: flank wear rate H: tool hardness q: normal contact stresses of the flank.

The quantity VB and hence dVBldt can be readily measured. For a given tool composition H is a function of temperature, graphs of which may be obtained from the literature. The value of q cannot be directly obtain- ed, for reasons that are discussed in more detail elsewhere (5). However, q is a complex function of material properties and cutting conditions.

LOAD, W

Disk

Figure (B.l) ARCHARD'S EQUATION VIL = K, WIH

V:Volume of worn material L: Sliding distance K,:Wear coefficient W:Contact load H:Hardness of wearing material (pin)

GLOSSARY Tri bology*:

The study of the science and technology of in- teracting surfaces in relative motion. It includes studies of lubrication, friction, and wear between the surfaces.

Hypothesis Testing: The various statistical testing methods used in this study are described in detail in any standard statistical textbook, for example:

W.W. Hines and D.C. Montgomery "Probability and Statistics in Engineering and Management Science" J. Wiley, 1972.

*After Quinn T.F.J. "The Application of Modern Phys i ca l Techn iques t o Tribology",: Van Nostrand Reinhold, 1971.

REFERENCES 1. Berry, J.T., Salame-Lama, F., and Saigal, A., "The

Machinability of Cast lrons - A Review", Prepared for the Ductile Iron Society, October 1981.

2. Pierce, R., "Machinability of Ductile Cast Irons: 'Pearlite Micro-hardness Study"', Final Report ME 4901 2, Winter 1983, Georgia lnsti tute of Technology.

3. Heine, H.T., "Machinability and Microstructure", American Foundryman, January 1954.

4. Field, M. and Stansbury, E.E., "Effect of Microstructure on Machinability of Cast lrons - I", ASMME Transactions, August 1947.

5. Salame-Lama, F.A., "Tool Wear Mechanisms in Single Point Cutting of Ductile Iron", Ph.D. Thesis, Georgia Institute of Technology, School of Mechanical Engineering, March 1984.

6. Shaw, M.C., "Study of Machined Surfaces", Proc. Seminar on Metal Cutting, OECD, September 1966.

7. Archard, J.F., "Contact and Rubbing of Flat Sur- faces", Journal of Applied Physics, Vol. 23, 1953.