EMF T V `
1.5 Setup Merging and Monitoring
containing runtime execution status back to the SI-FB and finally to the Execution Control module. The SI-FB is of vital importance for machining-process monitoring and dynamic rescheduling in case of machine failure.
EO_ESR EO_ESS EI_MSR
EI_ESS EI_ESR
Service Interface FB
FB_EXE
Internal Algorithm ALG_ES_REQ ALG_ES_SEND ALG_MS_REQ ALG_MS_SEND ALG_US_SEND
EO_MSS
US MS US
EI_MSS
MS
FB_EXE EO_MSR
EI_USS EO_USS
Figure 1.13. Service interface function block
FB_EXE
EI_ESR
MT EMT EI_INI
EI_UPD EI_RUN
EO_INI EO_RUNRDY
EO_ESS
FB_EXE EMT
MT
Step MF-FB
F25
EO_INI EO_P1 EI_P EI_INI
ES-FB
ROUTE EO_DONE
EO_P2
Chamfer MF-FB
F23
Sunk Hole MF-FB
F26 EI_ESR
MAC_ID OPER CC_UPD ROUTE
F24
EO_ESS EI_MSR
EI_ESS SI-FB
FB_EXE ALG_ES_REQ
ALG_ES_SEND ALG_MS_REQ ALG_MS_SEND ALG_US_SEND
EO_MSS
US MS US
EI_MSS
MS
FB_EXE EO_MSR
EI_USS EO_USS
Execution Control Machining Selection
FB Dispatching Process Monitoring
Figure 1.14. An SI-FB linking to a composite function block for process monitoring
1.5.1 Setup Merging
As mentioned in Section 1.4.2, a DPP-generated sequence plan is 3-axis based. In the case that a 4-/5-axis machine is selected, proper setup merging is required for the best utilisation of the machine. According to the five EMF-based reasoning rules, a 3-axis-based generic setup plan of a test part (shown in Figure 1.15(a)) with 26 machining features can be generated. It consists of 5 setups, each of which contains a set of partially sequenced machining features, as shown in Figure 1.15(b). The light grey areas are setups and the dark grey areas indicate the feature groups sharing the same tools. Each 3-axis-based setup can be represented by a unique unit vector u indicating its tool-access direction (TAD).
F1
F2 F8
F9 F4
F6
F5
F20 F3
F11
F17 F10
F15
F13 F18 F19
F21 F22 F14 F16
F25 F23
F26 F24 F12
F7
F2
F10 F9 F15
F14
F16
F17 F13
F12 F11
F22 F21
F19 F18
Setup-1
F7
F8
Setup-4
Setup-5 Setup-3
Setup-2
F23 F24
F25 F26
F20
F1 F3
F4
F5
F6
A //
Reference Feature
(a) A test part with 26 machining features (b) Partially sequenced machining features Figure 1.15. A test part with 5 setups after applying EMF-based reasoning rules In the case that a 5-axis machine tool {X,Y,Z,A (around X),B (around Y)} is selected, more than one setup of the test part may have the chance to be machined in onebase setup through setup merging. The setup merging examines whether other setups can be included in the base setup by checking the unit vector u of each setup against the tool-orientation space (TOS) of the selected machine. The procedure is straightforward by following two steps and their iterations, i.e. (1) aligning the locating direction of a base setup to the spindle axis Z, and (2) searching for a position that includes a maximum number of 3-axis-based setups by rotating the part around the locating direction (or spindle axis Z). This merging process is repeated for all setups until a minimum number of 5-axis-based setups can be reached. Since the first step can be done easily using matrix transformation, we only provide more details on the second step.
Figure 1.16(a) shows a typical scenario, where a base setup has been aligned with -Z axis and another 3-axis-based setup with a tool-access direction ui (xi,yi,zi) is under consideration. The goal is to rotate the vector ui (or the test part) around Z and at the same time determine a mergable range (or ranges) within 2S, that ui can fit in the TOS of the machine. The TOS is represented as a spherical surface patch denoted by EFGH in Figure 1.16(a).
Ji
Y
-Z X
O
ui(xi, yi, zi) Ci
G F
E H
)A
IA
)B
IB
)A
IA IB )B
Ti
(a) Searching for setup mergability in TOS
S 2
8
Ji 7
Ji 6
Ji 5
Ji 4
Ji 3
Ji 2
Ji 1
Ji
Mergability
1
0 Ji
(b) Mergable range of a setup with TAD ui
Figure 1.16. Setup merging for a 5-axis machine
As shown in Figure 1.16(a), the spherical coordinates of ui are (1,Ji,Ti). By rotating ui around Z, a circle Ci is obtained.
°¯
°®
i i
i i i
i i i
z y x
T J T
J T cos
sin sin
cos sin
(1.16)
where, Ti is a constant and Ji [0, 2S]. The Ci may intersect with the spherical surface patch EFGH defined by
EF: IA )A, IB[)B,)B] (1.17)
FG: IB )B, IA[)A,)A] (1.18)
GH:IA )A, IB[)B,)B] (1.19)
HE: IB )B, IA[)A,)A] (1.20)
where, [)A,)A] and [)B,)B] are the motion ranges of axes A and B, respectively.
ForIA )A and IB[)B,)B],
2 2
)) tan(
) (cos(
1
)) (cos(
B A
z A
I )
)
,IB[)B,)B] (1.21)
If zi zmin, the segment EF:{IA )A,IB[)B,)B]} and the circle Ci has no intersection. If zi0 and
zmax
zi ! , the segment EF and circle Ci intersect over the entire range of [0, 2S]. Otherwise, if zi0 and
max
min z z
z i ,EF and Ci
intersect with each other along the edge of the TOS. Figure 1.16(b) gives the mergable range of the case shown in Figure 1.16(a). This mergable range can be calculated for every 3-axis-based setup. A pose (position and orientation) of the test part that provides the most overlapping mergable range determines a 5-axis-based setup. Figure 1.17 depicts one case of setup merging of the test part after the generic sequence plan in Figure 1.15(b) has been combined for a 5-axis machine.
F10 F9 F15
F14
F16
F17 F13
F12 F11
F22 F21
F19 F18
Setup-1
F7
F8 Setup-2
F23 F24
F25 F26
F20
F1 F3
A //
Reference Feature F2
F5
F6 F4 -Z
X Y
A B
Figure 1.17. Results of setup merging for a 5-axis machine
1.5.2 Detailed Operation Planning
After a merged composite function block (e.g. Setup-2 in Figure 1.17) has been dispatched to its dedicated machine, detailed operation planning is performed. The algorithm ALG_INI in each MF-FB can choose a cutter, determine a set of cutting parameters, plan tool path, and generate optional G-code for conventional machines.
A knowledge base that contains suggested tools and tool-path patterns for each MF- FB is used to facilitate operation planning. Although the runtime initialisation runs transparently in a controller, a user interface is implemented to visualise the process and to verify the concept, as shown in Figure 1.18. The machining sequence X and setup Y are derived based on critical datum references, manufacturing constraints, and the EMF-based reasoning rules, while other detailed machining data Z–\ are derived by individual MF-FBs. The data in Z and [ is used to cut corresponding machining features. The G-code \ of a setup (setup-5 merged into base Setup-2 in this case) is generated for the selected machine, by assembling blocks of G-code of each machining feature in the order of the defined sequence. Note that the function of G-code generation is designed into function blocks to best utilise legacy machines. It is triggered when the process plan is dispatched to a non-OAC (open architecture controller)-based machine.
Machining sequence Setup 5
Cutter data Cutting parameters Optional NC code generation 1
2
3 4 5
Figure 1.18. Detailed machining data derived by function block embedded algorithms
1.5.3 Function Block Execution Control and Monitoring
Unlike conventional CAPP systems, our function block enabled DPP approach can provide two-way information flow. The monitoring information from bottom up adds value to adaptive process planning and is of vital importance for shop-floor execution control and dynamic scheduling. The current DPP implementation enables both remote monitoring through the Execution Control module and local monitoring beside a machine using the Operation Planning module. TCP/IP through sockets is used for data communication. Figure 1.19 demonstrates one scenario for remote monitoring of another test part. Once a request is sent to the composite function block that runs on a specific machine, its runtime status including current cutting conditions and job completion rate (%) will be sent back to the requester.
DPP Execution Control
CNC Machine Ethernet
Figure 1.19. Real-time function block execution monitoring
Finally, the two sample parts machined using DPP-generated process plans are shown in Figure 1.20. It is worthy of mention that rather than “how to do” defined by ISO-6983 in terms of G-code, the function blocks in DPP only define “what to do”, which is independent of machines. The detail instructions on “how to do” a job, however, is up to the embedded algorithms to determine at runtime.
Figure 1.20. Machined test parts for DPP concept validation