manufacturing process performanceweb.mit.edu/2.810/www/files/lectures/lec4-process...4 Manufacturing...
Transcript of manufacturing process performanceweb.mit.edu/2.810/www/files/lectures/lec4-process...4 Manufacturing...
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Manufacturing Process
Performance
2.810
T. GutowskiSee Refs and extra slides at end
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State of the Teams:
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• Please form teams with 4-6 members
• We can help if you are unsure how to
proceed
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Comparing Mfg Processes
Ref University Loughborough, UK
Conventional
casting
3D Printed
Guidelines: redesign for process, design for same objectives
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Manufacturing process
performance attributes
• Cost - mostly direct costs, DFM, software
• Rate and Time – process rate & system rate
• Quality/Variation – physics & statistical control
• Energy/ CO2 – processes & systems
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Manufacturing process
performance attributes
• Cost - DFM Boothroyd, Oct 2 Lewandowski
• Rate and Time – by process & for systems
Oct 30 Gershwin
• Quality/Variation – Oct 28 Hardt
• Energy/ CO2 – Oct 2, Nov 13
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Profit = Sales - Costs
Ostwald
Direct
costs: “touch”
labor, direct
materials
& tooling…
Indirect
costs: common
activities that
support many
products
Our focus: the cost to make a product
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Components of Direct Cost:
Direct Recurring Costs (Variable C = VN):
•Materials, including Buy to Fly, auxiliary…
•Labor, usually “touch”, but…
•Energy, usually small for parts
•Equipment (Prices as a “rental”)
Direct Non-recurring costs (Fixed C = F):
•Tooling, special equipment..
BMW
TRM tool
ORNL/
Cincinnati
BAAM
Machine
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Unit cost: C/N =F/N + V
Serial processes takelonger, larger variable costsSpecialty mat’l add to variablecosts
Parallel processes require tooling,larger fixed costs, but short cycle time
N*
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Estimate breakeven
N*
Waterjet
Extrusion
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Direct Cost comparison;
breakeven part volume
Extrusion
• Fixed = extrusion die
• Variable =
(30grams/part)
• Material = aluminum
billet, scrap
• Labor = set-up & run
• Machine = set-up, run,
maintenance
Water jet
• Fixed = programing
• Variable = (30g part, 90
secs/part)
• Materials = aluminum
bar, waste, abrasive
• Labor = some
monitoring needed
• Machine = includes
machine, financing,
installation, maintenance
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Aluminum Extrusion
• Extrusion die = $5,000
• Extrusion rate, less than 1 second to extrude one part
• Equipment rate ~ $40/hr
• Labor rate ~ $50/hr
• Materials billet aluminum,
some start up loss, kerf, say 30g + 1g waste => $0.31
• Setup/cleanup times dominates
Equip and Labor
• Sawing, post processing
• V = $0.31 + $0.09
• Cost = $5,000 + $0.40N
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Nest Parts for Waterjet, waste
~30%
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Waterjet cutting
• Variable Costs:
• Aluminum bar stock ~ $ 5/lb (~$10/kg)
• 30 gram part + waste = 40g/part = $0.40/ part
• Run time ~ 90 sec
• Abrasive ~ $0.30/part
• Machine cost (~$100k/5years/1shift (1000h/y)
+ maintenance) ~ $25/hr
• Labor cost ~ $35/hr
• Cost = $2.2N
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Waterjet cutting
• Variable Cost Breakdown:
Variable Cost Value
Aluminum $0.4
Abrasive $0.3
Labor $0.875
Machine $0.625
Total $2.2
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Estimated breakeven
N* ~ 3,000 parts
Breakeven cost $2.20/part
Waterjet
Extrusion
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Big Blue Saw quote
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Additive Mfg Vs Injection Mold
Ref. Cassandra Telenko et al
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Components of Indirect Cost:
Indirect Costs
•Support activities that can be charged “evenly” to all products
i.e. “overhead” may include things like; design, programming,
maintenance, quality control, purchasing, marketing, sales,
general and administrative expenses…
e.g.Composites parts
may require more
inspection than
metal parts
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Indirect costs..
• Become more important for higher
levels of automation,
• Become more difficult to allocate as the
number of products and variation grows.
• Use “Activity Based Costing” and other
tools
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Cincinnati MAXIM FMS Cell
(5) Machines
(24) Pallets
(3) Shifts
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System H; (2,000/35,000)
System 2;
(20/1,500)
System 3; (200/ 10,600)Part Types/ Total Produced
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Cost Models• Time & Motion; F.W.Taylor, Ostwald, Polgar
• DFM and DFA: Boothroyd, Dewhurst & Knight (Parametric
Models)
• Software & On-Line Quotes
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DFM guidelines are helpful,
they identify important
features that drive cost:
Examples-
• Handling and insertion
difficulty for manual assembly
• Injection mold tooling
complexity
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Example: Time Estimation for
Manual Assembly• Handling
pick up
orient
• Insertion
location (obstructed view? Self locating?)
hold down and resistance
securing method
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Symmetry
Handling Issues
0 180 180 90 360 360
0 0 90 180 0 360
Size Fragile/Sharp
Nest/TangleSlippery/Flexible
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B-D Manual handling chart
Handling difficulties: nest, tangleslippery, sharp…
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Insertion Issues
Obstructed Access/View
Alignment
Insertion Force
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B-D Manual insertion chart
screws
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1 – screw(2) (steel) not easy to align
2 – cover(steel) not easy to align – assembly worker’s fingers must be used to align edges
3 – spring(steel) (closed ends) subject to continuous cycling and
must be spring steel
4 – piston stop(plastic) edge is chamfered for ease of alignment5 – piston(aluminum) obstructed
access for insertion of spindle into bottom of bore
6 – main block(plastic) depth of bore is 28mm with small through hole for piston spindle
Pneumatic Piston Sub-Assembly
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B-D Manual handling chart
piston
screws cover
spring
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B-D Manual insertion chart A
coverpiston
spring
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B-D Manual insertion chart B
screws
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Pneumatic Piston Sub-Assembly
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Boothroyd/Dewhurst Design Rules
1. Reduce part count and part types
2. Strive to eliminate adjustments
3. Design parts to be self-aligning and self-locating
4. Ensure adequate access and unrestricted vision
5. Ensure the ease of handling of parts from bulk
6. Minimize the need for reorientations during assembly
7. Design parts that cannot be installed incorrectly
8. Maximize part symmetry if possible or make parts obviously asymmetrical
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Rules to reduce part count
1. During operation of the product, does the part move relative to all other parts already assembled?
Only gross motion should be considered – small motions that can be accommodated by elastic hinges, for example, are not sufficient for a positive answer
2. Must the part be of a different material than or be isolated from all other parts already assembled?
Only fundamental reasons concerned with material properties are acceptable
3. Must the part be separate from all other parts already assembled because otherwise necessary assembly or disassembly of other separate parts would be impossible?
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1 – snap on cover and stop (plastic)
2 – spring(steel)
3 – piston (aluminum)
4 – main block (plastic)
Redesign: Pneumatic Piston Sub-Assembly
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Re-design
Redesigns need to be thoroughly checked that they
do not compromise the functioning of the device!
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Process Rate Limits
• Machining – limits on (MRR), large forces…
• Injection Molding – die development, heat
transfer
• Sheet metal forming – die development, part
complexity of CNC machines, multiple presses
• Assembly – time between steps, out of tolerance
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Nominal Mfg Process Rates
Vary by more than 8 orders
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See extra slides at end
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The consequences of slow
machines
• Bottlenecks
• Large capital investment
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Metal Additive Part
String ribbon process for PV
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Production Rate
• Process rate - physics
• System rate – waiting for needed resource
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Flow efficiency
= process time/total
time in the system
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System Time & Rate
• Oct 30, Dr. Stan Gershwin
– Little’s Law,
– unreliable machines,
– Buffers (zero, infinite and finite)
– M/M/1 queue
– Simulations
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Quality Issues at the Process
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Satisfied customer and meeting target specification,
later TPS and hidden problems
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Where you find the problem
matters
• Product recalls
• Customer complaints
• Warranty claims
• Dealer returns
• In-house inspection
• Assembly
• Observed at the process
Increasing
CostRepair and upgrade of the Hubble Telescope
Fix problems close to home!
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Quality Loss Function
48Ref M.Phadke
After G Taguchi
Deviations from
The target value
should be penalized
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Inspecting in Quality
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Ref M. Phadke
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50Kalpakjian and Schmid 7th ed
Nominal Process Variation(process in conrol)
Surface RoughnessDimensional Tolerance
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Process variation/tolerance
What are the
most important
variables?
Kalpakjian
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Process variation/tolerance
elevated temperature
machining
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Process variation
size
temperature
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Quality Control Actions
Y = (+, U)
Ref Hardt, 2001
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How to control variation
output variationdisturbance
sensitivitycontrol action
ref. D. Hardt
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Real Time Control
output variationdisturbance
sensitivitycontrol action
Covered in 2.830, D. Hardt
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Designed Experiments
output variationdisturbance
sensitivitycontrol action
Output
feature
Controllable parameter
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Statistical Process Control
output variationdisturbance
sensitivitycontrol action
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Mean drift, and variance of the
output Y
Mean on target, but
large variation due to
many random effects
Mean drift has
assignable cause,
tight grouping
means small variation
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Comparing the variation with
the specifications
Goals: 6s < (USL-LSL)
and mean centered
out of spec
partsout of spec
parts
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If UCL-LCL = 6s
and the process mean
is in the center, then
The out of compliance
parts are given by
2(0.500-(3s)) =
2(0.500-0.4987) =
0.0026 or 0.26% or 2600ppm
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Some propose a process capability index Cp that
compares the tolerance interval USL-LSL vs the
process variation 6s.
Upper Specification Limit
Lower Specification Limit
Target
Cp % out ppm
⅔ 4.55 45,500
1 0.26 2600
1 ⅓ .0063 63
2 .00034 3.4
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Mean Drift
Upper Specification Limit
Lower Specification Limit
Target
0.135%
13.6
+2.1
+0.135
15.835
+0.135
15.97%
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2.810 Manufacturing
Processes and
Systems - Process
Control64 Slide taken from D. Hardt
“Not In-Control”
i
i+1
i+2
...
The Parent
Distribution
Changes with Time
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Statistical Control Methods
Strategy:
1. Monitor process when under control
2. Determine Centerline & Control Limits
3. Data outside of UCL/LCL indicates change
4. Investigate and eliminate causes of change
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SPC chart showing
“Not-in-Control” condition
• X bar chart
Lower Control Limit
Sampling period
Upper Control Limit
Centerline
Read 1.5-1 Hogg & Ledolter, “Control Charts”
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Histogram for CNC Turning
From Dave Hardt
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Sampling Frequency
Factors that determine the appropriate
sampling frequency:
– Stability of process
– Potential loss
– Cost of sampling inspection
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What causes variation in dimensions?
• Machine variation
– e.g. change in settings, environment, equipment
• Material variation
– e.g.suppliers, substitutes, mixtures..
• Operator variation
– Jim instead of Joe, or Alice instead of Mary
• Method variation
– Late Friday afternoon, Mary always does it this
way…
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“x-bar charts”Mean of the means
• Mean
Where, n = sample size
k = number of samples
A2 = constant from Table C.1
R = defined next slideAlso see alternative method
Presented by Dave Hardt
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“R-charts” Range = high - low
Where, n = sample size
k = number of samples
D3 , D4 = constants from Table C.1
• Standard Deviation can be estimated from R
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Estimate of standard deviation from range
ref. P. Lyonnet
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Robustness Vs Sensitivity
• Mean shift by 1s requires on average
44 data points to observe
• Alternatives
– ±2s; two consecutive observations
– ±1s; three consecutive observations
– other charts (e.g. “Cusum”)
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Source Identification; Ishikawa
Cause and Effect DiagramMan Machine
Material Method
Effect
Finding the cause of a disturbance is the most difficult part of quality control. There are only aids to help you with this problem solving exercise like the Ishikawa Diagrams which helps you cover all categories, and the “5 Whys” which helps you go to the root cause.
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SPC - Statistical Process Control in Injection
Molding and Extrusion By Chris Rauwendaal
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References
• Boothroyd & Dewhurst DFA (manual)
• Control Charts (sect 1.5-1), Hogg & Ledolter
• also see your text, topics are listed in the index. For
the 7th edition: SPC section 36.8, Cost section 40.10,
and Energy sections 40.5 &40.6 these topics are also
listed in other places in the text
• “hockey stick diagram” for energy and rate, Gutowski
et al, See “ electrical energy used in manufacturing
processes” p 1586-1588 your text