ROBOTICS An Introduction

1
Flexible Manufacturing Systems (FMS)by Ed
Red an automated, mid-volume, mid-variety,
central computer-controlled manufacturing system
Nanua Singh, Computer-Integrated Design and
Manufacturing, John Wiley Sons,
1996 References 1. Nanua Singh,
Computer-Integrated Design and Manufacturing,
John Wiley Sons, 1996 2. Mikell Groover,
Automated Production Systems and
Computer-Integrated Manufacturing,
Prentice-Hall, 2nd edition, 2001
2

• Objectives
• To review modern flexible manufacturing systems
  (FMS) - Group technology (GT) - Manufacturing
  cells - Automated part handling equipment
  (AGVs, etc.) - Control software - Analysis
  models
• To consider application conditions (student
  presentations)
• To test understanding of the material presented

3

• FMS characteristics
• A manufacturing cell used to implement group
  technology (GT)
• Independent machines performing multiple
  operations and having automated tool interchange
  capabilities
• Automated material-handling between stations
  (move parts between machines and fixturing
  stations)
• Hierarchical computer control architectures
• Often include CMM, inspection and part washing
  devices

4
GT requirement Parts can be grouped into part
families!
Similar manufacturing process requirements
(manufacturing attributes), but with different
design attributes
Turned, drilled, milled..
Cylindrical, hole, thread, chamfer, tolerance,
dimension..
5
GT requirement Production machines can be
arranged into cells!
Group technology layout
Process type plant layout dashed lines indicates
departments!
6
GT part classification and coding

• Parts distinguished (classified) by design
  attributes and manufacturing attributes.
• Part differentiated by coding methods for

• design retrieval
• automated process planning
• machine cell design

Basic structure of Opitz coding system
7
GT Opitz form code
8
GT example

• For the part shown determine the form code in
  the Opitz parts classification andcoding
  system..
• Solution
• With reference to Figure 15.6, the five-digit
  code is developed as follows
• Length-to-diameter ratio, L/D 1.5
  Digit 1 1
• External shape stepped on both ends with screw
  thread on one end Digit 2 5
• Internal shape part contains a through-hole
  Digit 3 1
• Plane surface machining none
  Digit 4 O
• Auxiliary holes, gear teeth, etc. none
  Digit 5 O
• The partss form code in the Opitz system is
  15100

9
FMS Highly automated GT manufacturing cell,
consisting of a group of processing workstations,
interconnected by an automated material handling
and storage system, and controlled by a
distributed computer system (Groover defn) What
does flexible mean? 1. Can identify and operate
on different part/product styles 2. Quick
changeover of process/operating instructions 3.
Quick changeover of physical setup FMS
operations 1. Processing operations, or 2.
Assembly operations
10
FMS automated part handling
Conveyor
AGV
AS/RS
11

• FMS type - Distinguish by number of machines
• Single machine cell can operate in batch mode
  (sequentially process parts of a single style in
  defined lot sizes) or flexible mode (process
  different part styles and adapt to different
  production schedules)
• No error recovery if machine breaks down since
  production will stop
• Flexible machine cell (FMC) consists of 2-3
  machines plus part handling equipment and limited
  part storage.simultaneous production of
  different parts and error recovery.
• Flexible manufacturing system consists of 4 or
  more workstations connected by common part
  handling system and distributed computer system.
  Other stations may support the activities, such
  as a coordinate measuring machine (CMM) or
  washing station. .simultaneous production of
  different parts and error recovery.

12

• FMS layouts
• In-line layout
• Loop layout (secondary part handling systems)
• Ladder layout
• Open field layout

13

• FMS computer control system
• Workstation control
• Supervisory control among workstations
  (workstation coordination)
• Production control (part rate and mix)
• Traffic control (manage part delivery systems)
• Shuttle control (part handling between machine
  and primary handling system)
• Workpiece monitoring (status of various systems)
• Tool control (location and tool life)
• Performance monitoring and reporting (report
  operational data)
• Diagnostics (identify sources of error,
  preventive maintenance)

14

• FMS design issues
• Workstation types
• Variations in process routings and FMS layout
  (increasing product variety move you from in-line
  layouts to open field layouts)
• Material handling system
• Work in process (WIP) and storage capacity (FMS
  storage capacity must be compatible with WIP)
• Tooling (numbers and types of tools at each
  station, tool duplication)
• Workpiece monitoring (status of various systems)
• Pallet fixtures (numbers in system, flexibility)

15

• FMS operational issues
• Scheduling (master production schedule) and
  dispatching (launching of parts into the system)
• Machine loading
• Part routing
• Part grouping
• Tool management
• Pallet and fixture allocation

16

• FMS quantitative analysis
• Four models
• Deterministic models (dont include operating
  characteristics, including queues, that may
  degrade performance, thus are a little
  optimistic)
• Queueing models
• Discrete event simulation (simulation)
• Heuristic approaches

17

• FMS bottleneck model
• Bottleneck output of a production system has an
  upper limit, given an upper bounds on the product
  mix flowing through the system
• Introduce the bottleneck model to provide initial
  FMS parameter estimates
• Introduce terminology and symbols
• Demonstrate on examples

18
FMS terminology and symbols Part mixpj
fraction of system output that is of style j P
total number of part styles made in FMS in given
time period
Workstations and servers (workstation that can
duplicate process capabilities of another
workstation )n number of workstations si
number of servers at each station i (i 1,2,n,
and we include the load/unload station
as an FMS workstation)
19
FMS terminology and symbols Process routing
for each part or product, defines operational
sequence, assigned workstations, and associated
process times, including loading and unloading
times tijk processing time for a part/product
in a given server, not including waiting
time, where i station i j part/product
j k particular operation in process routing
sequence of operations
20
FMS terminology and symbols Work handling system
material handling system is considered a
special workstation and designate it as station n
1 then sn1 number of carriers (servers) in
handling system (conveyors, carts,
AGVs, etc.) Transport time tn1 mean
transport time required to move a part from
one workstation to the next station in the
process routing
21
FMS terminology and symbols Operation frequency
expected number of times a given operation in
the process routing is performed for each work
unit, e.g, an inspection of a dimension fijk
operation frequency for operation k for part j at
station i This parameter (fijk) is usually one
since each operation is usually performed once on
a different workstation! Exceptions might exist
for part inspection stations. Note that there are
many zero values since not all parts and
operations go through every machine.
22
FMS quantitative modelsAverage workload (Li)
mean operational time of station i per part,
calculated as (units are in min.) Li Sj Sk
tijk fijk pj Workload of the handling system is
the mean transport time (tn1) multiplied by the
average number of transports to complete part
process. Average number of transports (nt) is
the mean number of operations in the process
routing minus 1 nt Si Sj Sk fijkpj
1 difficult interpretation! Workload of handling
system is Ln1 nt tn1
i station ij part/product j (process
routing) k operation in routing sequence
23
FMS example determine nt Simple system has
machining station and load/unload station. If
system processes single part, determine nt. One
part (j 1) so p1 1.0 fi1k 1.0 3 routing
operations load part at 1-gt route to station 2
for machining-gt return to station 1 for
unloading Then nt 1(1.0) 1(1.0) 1(1.0)
- 1 2 load machine at 2
unload
24
FMS quantitative models FMS production is
usually constrained by a bottleneck station
(consider the handling station also), which is
the station i with the highest workload per
server as measured by Li/si. Designate i b the
bottleneck station and calculate the maximum
production rate from Rmax sb/Lb (number of
parts per time for station b) Note This is valid
even for parts not passing through the bottleneck
station because the part mix ratios are fixed and
limited by the bottleneck station. Individual
production rates are Rj pj sb/Lb
25
FMS quantitative models Mean workstation
utilization is the proportion of time that
stations are active as determined from Ui
Rmax Li/si ( Ub 1) The average
station utilization is U Si Ui/(n1) The
overall FMS utilization is weighted by the number
of servers at each station (not including
handling stations) Us Si siUi/ Si si Number
of busy servers at other than the bottleneck
station determined from Bi Rmax Li
26

• FMS example (from Groover)
• An FMS with 4 stations is designed so that
  station 1 is load/unload, station 2 performs
  milling operations with 3 servers, station 3
  performs drilling operations with 2 servers,
  while station 4 performs part inspection on part
  samples. The part handling system has a mean
  transport time of 3.5 min and 2 carriers. The FMS
  produces parts A, B, C, and D with part mix
  fractions and routings shown in the table.
• Determine
• FMS max production rate
• Production rate of each part
• Each station utilization
• Overall FMS utilization

27
FMS example solution First, determine bottleneck
station by calculating workloads L1
(42)(1.0)(0.1 0.2 0.3 0.4) 6.0 min. L2
(20)(1.0)(0.1) 25(1.0)(0.2) (30)(1.0)(0.4)
19.0 min. Similarly, L3 14.4 min. L4
4.0 min. nt (4.5 - 1)(0.1) (5.2 - 1)(0.2)
(3.5 -1)(0.3) (3.333 - 1)(0.4) 2.783 L5
(2.873)(3.5) 10.06 min. part handling
station! Now calculate Li/si to identify
bottleneck L1/s1 6.0/1 6.0 L2/s2
19.0/3 6.333 L3/s3 14.4/2 7.2 the
bottleneck! Rmax 2/14.4 0.1389 pc/min. (8.333


pc/hr) L4/s4 4.0/1 4.0
L5/s5 10.06/2
5.03
28
FMS example solution Production rate for each
part RA 8.333(0.1) 0.8333 pc/hr. RB
8.333(0.2) 1.667 pc/hr. RC 8.333(0.3)
2.500 pc/hr. RD 8.333(0.4) 3.333
pc/hr. Station utilization U1
(6.0/1)(0.1389) 0.8333 (83.33) U2
(19.0/3)(0.1389) 0.879 U3 (14.4/2)(0.1389)
1.0 U4 (4.0/1)(0.1389) 0.555 U5
(10.06/2)(0.1389) 0.699 Overall FMS
utilization (exclude part handling) U1
1(0.833) 3(0.879) 2(1.0) 1(0.555)/7
0.861 (86.1)
29
FMS follow-on example (from Groover) Determine
the production rate of part D that will increase
the utilization of station 2 to 100. Note that
this is possible since part D does not go through
station 3, the bottleneck station, and station 2
is under utilized. Solution Set U2 100 and
solve U2 1.0 L2(0.1389)/3 to get L2 21.6
min. as compared to 19.0 min. previously. Parts
A, B and D are processed by station 2. Parts A
and B are constrained in their production rate by
the other stations, but not part D which is only
processed by station 2. We first determine the
portion of the station 2 workload taken up by A
and B L2(by AB) 20(0.1)(1.0) 25(0.2)(1.0)
7.0 min.
30
FMS follow-on example At 100 utilization the
workload for part D increases to 21.6 7.0
14.6 min., where it was 19.0 7.0 12.0 min. at
87.9 utilization. The production rate for part D
is now increased to 14.6(3.333)/12.0 4.055
pc/hr. Note that increasing the throughput for
part D will change the part mix ratios previously
presented.
31
Optimizing operations allocation in an FMS with
negligible setup
Sound familiar?
Two criteria - production of parts with minimum
cost - production of parts at max production
rate Define K part types having demand dk (k
1,......K) M machine types each having
capacity bm (m 1,.....M) Jk operations
performed on part type k (j 1,.......Jk) ckjm
unit processing cost to perform jth operation
on kth part on mth machine else,
set the cost to infinity (set high) tkjm
unit processing time to perform jth operation on
kth part on mth machine else, set
the time to infinity (set high)
32
Optimizing operations allocation in an FMS with
negligible setup
Define flexibility factor, akljm Assume
operations can be performed on alternative
machines. Part can be manufactured along a number
of routes. For example, if a part has three
operations and if the first, second, and third
operations can be performed as - operation 1
on two machines - operation 2 on three
machines - operation 3 on two machines then a
set of alternative process plans (l Î L, where L
is the total number of alternative plans) would
include 2 x 3 x 2 12 possible processing
routes. Define akljm 1 if in plan l the
jth operation on the kth part is performed on
the mth machine else, set the factor to 0
33
Optimizing operations allocation in an FMS with
negligible setup
Minimum cost to manufacture all parts Minimize
Z1 Skljm akljm ckjm Xkl Linear
programming where Z1 is the objective function
and Xkl is a decision variable representing the
number of units of part k to be processed using
plan l. Constraints Demand for parts must be
met Sl Xkl ³ dk " k Can not exceed machine
capacity Sklj akljm tkjm Xkl bm "
m Positive number of units produced Xkl ³ 0
" k, l
34
Optimizing operations allocation in an FMS with
negligible setup
Maximize throughput (minimize total process time
for parts) Minimize objective function Z2
Skljm akljm tkjm Xkl Constraints Demand for
parts must be met Sl Xkl ³ dk " k Can not
exceed machine capacity Sklj akljm tkjm Xkl
bm " m Positive number of units produced Xkl
³ 0 " k, l
35
Optimizing operations allocation in an FMS with
negligible setup
Balance workload on machines (minimize the
maximum of the process times) Minimize
objective function Z3 maximum Skljm akljm tkjm
Xkl Constraints Minimized max gt other
workloads Z3 - Skljm akljm tkjm Xkl ³ 0
" m Demand for parts must be met Sl Xkl ³
dk " k Can not exceed machine capacity Sklj
akljm tkjm Xkl bm " m Positive number of
units produced Xkl ³ 0 " k, l
36
Linear programming - example
Consider the manufacture of 5 part types on 4
machine types, each part requiring several
operations. Table 12.18 list the pertinent data.
Develop a production plan for 1) min cost model
2) max throughput (min processing time) and 3)
workload balancing.
37
Linear programming - example
The 3 models were solved using LINDO, a linear
programming package, with the results shown in
Table 12.19. The table shows that parts can be
produced through a number of alternative process
plans. Another table (next slide) can be
generated to show the machine loading for various
operations allocation strategies.
38
Linear programming - example
Note that all three models result in 100
utilization of machines m2 and m3, making these
bottleneck machines. Consider machine m1. Its
resource utilization for the 3 models are 2400,
2400, and 2045 units of time, respectively. This
information is useful for production scheduling
and also for preventive maintenance.
To calculate these values simply multiply all the
operations on each machine (each part through the
machine is an operation) by the time required for
each operation as given in Table 12.18.
39
FMS
What have we learned?