Transcript 17~chapter_15_manufa..

Exploring Engineering
Chapter 15
Manufacturing Engineering
We Cover Three Shop Topics
Shop Safety
Basic Metal Cutting
Operations
Speeds and
Feeds
Applications of statistical methods
 A statistical
method of analyzing
defective manufactured products will be
introduced.
 Six
Sigma methods will be touched upon.
To Be Safe - Think!
Basic Safety Rules
Wear eye protection
at all times
No loose-fitting
clothing or jewelry
Do not work alone
Ask Someone If You Are Have
Questions
= ?
Traditional Machining
Operations
Turning
Milling
Drilling
Typical Drill Press
On Lathes, Part Rotates
On Milling Machines The
Cutter Rotates
Two Types Of Milling
Cutter
Part
Conventional
Milling
Cutter
Part
Climb Milling
Climb Milling
Cutter
Part
Climb Milling
Both the cutter and
the lead screw move
the table in the same
direction
Speeds And Feeds Are Like
Biting And Chewing
Speed: how fast the cutting tool (or part)
spins
Feed Rate: how hast the part is advanced
into the part
Manufacturers Have
Recommended Cutting
Speeds
Calculating The Cutting RPM
Same equation for drill press, mill, and lathe
3.82 * CS
RPM 
Dia
Where RPM = revolutions per minute for the cutting tool (mill
and drill) or work piece (lathe)
CS = cutting speed in surface feet per minute
Dia = diameter in inches for the cutting tool (mill and
drill) or work piece (lathe)
The Feed Rate For Milling
FEED  ( RPM ) * (chip _ load ) * (# _ of _ cutter _ teeth)
Chip load values are
found in tables (check
Machineries
Handbook)
We will use 0.003 IPT
for high speed steel
cutting steel
The Feed Rate For Turning
(Lathe)
Variability & Six Sigma
 No
manufacturedl
part is exactly like
another.
everyone in this
class measured
the length of new
a pencil with a
suitable ruler, the
derived lengths
would randomly
vary by small
amounts.
Frequency of result
 If
Histogram of measurements
referred to mean
5
4
3
2
1
0
-1.6 -1.2 -0.8 -0.4
0
0.4
Mean - actual, mm
0.8
1.2
Variability & Six Sigma

Extend to many points and in the limit of
large measurements the data become
continuous:

Plot ordinate as “frequency” (fraction of total
measurements) vs. Z
Z
  xi

in which
x

 is the mean,  
N
i
i
xi is the i th measuremen t
 is the standard deviation,  2 
2




x

i
N
i
Variability & Six Sigma
Plot with abscissa Z and ordinate
p(Z)

Z
p( Z ) 
e
z2
2
Variability & Six Sigma
 When
plotted this way the area under
the curve from - to + is 1.00 (i.e.,
100% of the samples)
area from -1 Z  1 contains 68% of
the data
 The area from -2 Z  2 contains 95% of
the data
 The area from -3 Z  3 contains 99.7% of
the data
 The
Example 1:
 You
make 1,000 rods of mean length
10.0 cm, standard deviation 0.1 cm.
 How many are within a specified range
of between 9.8 and 10.2 cm?
= ±0.2/0.1 = ±2. Thus 95% of the rods, or
950 are within spec and 50 are not serviceable
since out of spec.
Z
Example 2:

You make 1,000 rods of mean length 10.0
cm, standard deviation 0.1 cm.
 How many are within a specified range of
between 9.85 and 10.15 cm?


Z = ±0.15/0.1 = ±1.5. In the general case get the area
under the normal curve using Normsdist(Z) in Excel.
Normsdist(1.5) yields 0.933 and 67 rods will fail
 Less failures than in Example 1 since the window
of acceptance is wider.
Variability & Six Sigma
 Variability
has deep
consequences
example: Are
these two noisy
means equivalent?
Widget variable
 For
120.0
100.0
80.0
60.0
40.0
20.0
o
Average
Std dev
107.2
0.4
x
0.0
95.6
4.1
-20
80
30
Expt. #
• Hint: Add ±boundaries for 2 - if they overlap
Then the data cannot be statistically distinguished
with 95% confidence
Six Sigma
 A quality
control program introduced
into the US by Motorola to reduce the
number of rejects and thus improve the
quality of their products.
Z
= 6 is the stated target; with some slight
of hand it translates to 3.4 defects per
million samples)
Summary

Manufacturing engineering is covered in
Chapter 15



Machining, cutting, welding, extrusion, pultrusion
are all ways of manufacturing different products
Derivation of formulae relating to cutting rates for
drilling, milling, and lathe work, are derived.
Statistical analysis leads to better process control
and lest rejected widgets being out of
specifications.