Kobe University Repository : Thesis
Contents
1. gt 02 04 1 2 1 0 1 0 1 010 1 00080 0 2 11 000 1 770 0 0 00 0 00080000000 000100 0 1 70 000 1 100 s a Input output model2. 0 1 1 1 0 1 1730 7 7390 1 11 1 1 1 1 0 160 1 000 0 0 200000000000 1 1 1 0 gt 0810 1 gt 30 878770 10 0 0078770 000 A Koestler 0000000000 0 1 1 1 0 1 1 100800000
3. 5120000000000000000000000000000 99 1 1 53 procedure rocedure is suggested for done for upper upper level level elem pp goto step 5 no move target to first operation in batch goto step 1 exchange job order move target to job jobs exchanged notified in batch goto step 1 goto step 1 5 12 Flowchart of recursive propagation method for the hierarchical manufacturing system part 4 Cell A operation Machine A1 E selected Machine A2 EN by Cell B Machine A3 8 Cell B Machine B1 Machine B2 Machine B3 Cell C operation Machine C1 fm selected Machine C2 by Machine B3 Mach
4. 01608 30 Database Action toward Outside Machining Operation to Outside Compensation etc Database Action toward Inside Modification and Edition of Database Execution Interpretation Decision making Intelligent machine tool 3 2 Fundamental information flow 0 8170 0 1 1 0 7710 0 1 1 0 20 00 00 4 0 lt 00100783 0 1 0 0 0 00 1 1 0
5. b Cell mill a FERRE eo 11 de AU ET c Shop oo 2 SI fe er deed d Factory an det e on 6808 88 ex TT Ms 1 ES 8 2 EX e Industry 5 1 Hierarchical structure in a manufacturing system 88 51
6. 100 0 1 1 1 1808 805 000 6 522 08 0 1 1 1 1 1 8 0 0 1 1 87730 b Schedule in a factory 5 2 Job operation and scheduling in factory 52 00 9 1 5 20008 008 8 1 5 2066 1 1 0 0 070 2 b 1 1 0 1 0 1 0 16 0 1 1 160 m Co Aa
7. 1 1 11 1 1 1 1 1 1 0 8310 1 1 4 10 118 11 1 60 040 7308 a Centralized manufacturing system b Decentralized manufacturing system 4 2 Structure of manufacturing system 1 1 1 14 4 60 0 0 1 1 61 machine time a Initial state machine time b Modification of stating time of the first operation by machine machi
8. 0 1 1 10 1 000 0 1760 0 1 1 1 1 730 0 808087010 0 1 11 00 88 EY LEKOL 0 0 00 000000 0 1 0 180787770170 51 0 1 11 1 1 4 760 0 1 1 761 1 8 0 0 1 1 0 0 1 080808 0
9. L3 L3 E3 00 00 00 0000000 0 00 00 0 0000 0 8008770 1 0 00000000 0 111000 77 7230 0000000 1 OO OO 10 08 0000000 08880 0 00 0000 000 000000 1 7 1 gt 8770 0 000 Oooo EE Jo EN EI 00 00 00 LU 0 00000 000 080 0070 0 0 00 000000 0 00000070 0 U EST ES EST EST ESI 7 EST ESSE a LESE EI
10. 78 710 0 8888080080 00 108808187360 0 11 4 0 1 1 0 1 30 0 1 0 801 8 0 1 11 1 4 00070 0 1 1 1 706 Computer 0 1 1 1 Ei r3 x E 12 O EJ E Persona UB 0 Oo
11. 1 1 1 7 10 1 20 1 1 1 0 0 1 1 1111114 8 4 4 70 1 1 1 1 0 0 0 1 100 0 0 1 1 1 0 4 0 1 1 0 0 1 1 1 070 0 1 1 1 1 8 4 10 0 1 1 1 7730 0 1 1 1 1 1 gt 000080730 1 1 1 70 1 1 gt 0 0 1 1 0 1 011 0 000 707 0 0 U fexibility OOO robustness LL O 000000000 1 1 1 4 0 0000 1 0 0 1 1 1
12. 0 0 00000 0 80 00000 0 6 000 770730 0000000070 400 01 00000 00 1 0 710 0000000 000 0 1 0 00000 70 00000000 0 00030 000 11 0 000 1 117108 nm DOUDOU OOOO DOUD 2 RET 27 ADEF IVE 3 4 VAFLELTO LIVERS VAT DERLLCO Lien 5 gt LY gt LTXO Y 6 Y z 8 1 1 Structure of the present thesis 2 E Bal 2 1 0 0000000000 00000000 00 0 0000000000000 0 0 00000000000000000000 00 000000000000000000 00000000000000000000000000000000 000 000000 00000000000000000 000 0000000000000000 0000 00 0000000000000000000 0000 00 00000000000 0000000000
13. 0 1 1 0 1 1 1 4 7760 0 1 1 1 000 1 01 810 18180 98 1 1 1 0 0 1 1 1 0 1 1 1 4 0 0 1 1 8 88 0 000000 0 000000000 1 1 0 1 100878 gt 0 1 1 gt 000 0 3 0 1 0 1 1 0 1 1 1 4 30 0 1 110 70730 1 0000807 730 1 5 1 820000000000 10 3 21 1 1 Oooo 1 078881810006
14. U450000000000000000000000000000000000000000
15. 0 18787787730 propagation 10 00 Du DD 0 1 1 1 1 8 710 0 1 1 0 0 1 1 0 1 1 8 0 1 1 10880 gt 230 0 0 1 1 70 43 2 1 881011277168 1 118 8 80 43 70760 65 DOU step 1 0000000000000000000000000000000000000 0 0 000888 08000 0000000000000000000000000000000 0010118 gt 8 8808000 7808 00000000000000000000000000 8 0 000 0000000 000 000000000000000000000000000 000000 3 00000000000000 20000 4 000000000000 0000000000000000000000 0000000000000000000000000000000000000000 00 0000800800000000000000000000000000000 A000 00000 6 708 repeat Receive message until message Z empty if message NOTICE then 3 B
16. machine machine Nl previous process previous process target operation target operation previousoperation time time a Depending on b Depending on previous operation previous process 4 4 Decision of starting time of operation U 440000000000000000000000000000000000000000 4 4 0 0 1 44 0 040 64 43 10 0008001 4 3 1 1 0 08008 gt 0070030 1 0 0 6 process next operation target operation time 4 5 Influence of change of starting time
17. 0 0 000 0070 EE E EOE EA 3 2 5 2 1 A 1 1 1 1 0 1 0 1 08 302 1 1 1 1 1 0 2 1008 1 0 cooperation domain 0 1101 gt 71 a holon b non holon 2 10 Holon and non holon 0 00000000000000000000000000000000000000000 OOO non hlo 0 080 8 83 6 0 0 0 1 811410 1111 111 1 0 1 1 1 360 0 0 kolarchy DOU 21 0 870 200000000000 740 0 1 1 1 0 0 1 1 1 000 1 1 0 0 1 1 1 7 0 1 1 4 10 L3 L3
18. 1 1 1 00 0 70 1 1 1 2 01 1 8080087106 0 100878730 OOOO 3000000000000000000000000000000000000000000 1 1 0 0 0 0 160 3 6 OO 53 Feed Rate e 20m min o 10m min 2 5m min on E T 0 0 00 120 180 240 300 Time min a Temperature Rise at Z Axis Motor Cover Feed Rate e 20m min a 10m min ML 5m min o 5 o Q E 0 0 60 120 180 240 300 Time min b Temperature Rise at End Bracket of Z Axis Ball Screw
19. 000000 5 1992 1674 10679 C Vol 58 No 549 1 6 1 8 8 C Vol 63 No 616 pp 4402 4409 1997 0 7 1 109 Nod pp 291 298 1989 S 0000 0000 T 1 1 P BE ER DD 0111 C Vol 58 No 551 2276 2281 1992 9 1 8 1 0 0 C Vol 65 No 632 pp1725 1730 1999 0 0 0 0 C Vol 66 No 647 pp2449 2455 2000 11 Y Koren U Heisel F Jobane T Moriwaki G Pritschow G Ulsoy Reconfigurable Manufac turing Systems Annals of the CIRP Vol 48 2 1999 107 6 1 1 1 881 19 0 0 1 0 1 0000 1111 5 00706 2000 0 0000000 00 0800080 0 11 7 0
20. 2 41 1967 0 kolon 1 0000000 H3 4000000000000 0 1 1 00 1 777730 0 1 6 1 1 1 1 11 1 65 7 0 0 0 0 00000000000000 bolarchy 0 00 080 70 00000000 0 0 00000008 0 0 1 0 4 11 1 0 1 1 70 0 1 4 70 10 lulu 000000 EEE EST E O 1 0 008770 00 00000 gt gt 08
21. 25 000000000000 7 2 11 holarchy 1 1 1 11 0 758 000 1 760 hi 710 A8 00770 38 71 0 0 1 70 h 1 11 lt i lt n 2 1 0 07380 1 0 hen 2 2 2 5 3 0 1 1 D 0 1 0 1 11 0 0000 1 70680 1 0 00 00000 70 8 730 8080807 1 0 77808 1 0 020 18 0 8 1 1 1 1 n OOO 1 080017 706 Are 0000 0 CHO 880 72730 0 1 11 1 1 000 1 AND Ce DII Si n 2 3 0 1 0 Donono 1 1 760 0 C t Ane t 2 4 0 1 gt 70730 0 08 00 8070 0
22. 4200000000000 Step 100 44000 1 U Step 60000 0000 27180 0 327 01011011011 7758 10 Step 1 1 0 far ns At At Ato Send REPORT Send At goto Step 1 0 1 1 1 8 0 1 1 1 0 1 1 1 7 00 0 Step 1 10 Step 0 Ce goto Step 2 step 6 1 10 1 Atz 0 ae 7 DM At At Ato Stop 43 1 1 67 4 3 3 1 1 7 1 7 1 1 1 1 0 1 10
23. 1110009000 000 1 EHI 1 0 0 0 0 0 ET EST EST 6E ESL E EST 2 gra Li 0 040 0 A candidate J marked job machine t release time starting time of J of J 4 13 Search for suitable order of job Step 1 1730 Step 2 000000000000000000000000000 s o0000000 s oo0000000000000000000000000000000000 0000 3 04130000000000000000000000000000000 0000 1 10 DO 1 0 01 1 0 0 0000000000 0 0 08 00000000 10 00070 0 4 0 0 e wn
24. 00010 30 24 0 7760 0 1 008070 321 00 10 0 000000000 0 1 4 4 0 0 1 7 0 1 10778805 UO pre process O O O in process O O U U U post process 1 30 0 01001010000 1 1 10 0 0 1 1 1 0 07070 0 1 1 0 000 1 Database Knowledge Base lt Software gt Schedule CAD Machine Tool etc CAD Machine Tool etc Product Shape a Decision Making Evaluation Final shape Specification E Roughness Material Shape Tolerance Release Date Due Date Knowledge Knowledge nformation acquisition p Modification rforce temperature 4 Command 0 0 rdisplacement EE tool condition etc lt Hardware gt Actuator Machine Tool Collision Machine Tool Storage etc Avoidance JL Storage etc Material Machining Field Product In process item
25. 1 0800878730 0 0000 U 3000 000000000 70 0 0088772 070 1 1 1 1 7 70 0 0 7 060 1 2 0 DOU 708 0 4000000000 70 4 1 0000 0 1 1 1
26. EST ac fo onn EI asa Do EAE E3 E3 r3 E3 Ed E3 EJ E3 Ed L3 EEE LI 2 OU 0 1 1 0 1 0 000 1 1 170 0 1 778170 0 1 1 0 1 73 1 2
27. 1 1 1 3 3 0 1 1 0 1 1 18888 0 0
28. 14 020 10 0 1 1 0 0 1 11 708 0 108708 730 0 0 0 2 4 3 000000000000000 0 1 808 10 0 01 1 1 1 0 0 0 0 7 0 0 1080 000000080773 7 0 1 1 1 00 1 1 7080 1 Enterprise holon 00 DH lt gt DB 7 2 3 Factory holon A Factory holon B 2 8 Structure of holonic manufacturing system proposed 25 1 1 158 15 0
29. 3 27 0 1 81 0 8 D uut 35 80730 51 Spindle Motor Workpiece Direction of Depth of Cut Feed Direction 3 26 Point of temperature measurement 11805 0 0 1 70 0 1 1 1 30 0 0 3 28 1 1 1 0 1 1 1 4 00 00010 D uuu U 3 29 000000000 1 9 0 0 1 1 1 0077 0 1 1100801870 30 52 3 5 Experimental condition goog 000000000 1008 0o00000 00 0 3 101111 0 10010010111 8808 0 1 1 7 001 01 1 1 110 50730 000 1 0000 00 3 6 OO 0 1 7 0 1 1 1 0 0 1 1 0 gt
30. 0 1 1 710 0 L3 530 710 0 a Arrival time em Release time ES C_N of materials of products Arrival time 7 Arrival time of materials Operation for factory Operations for machines factory rocessing 5 3 Relationship between schedule for machines and job for factory U 5 4 10 1 1 1 0 1 1 1 070 1 1 1 1 1 machine target machine target operation operation previous previous process operation time time a Depending on b Depending on previous process previous operation 5 4 Decision of starting time of operation 90 51 07017730 target operation cell A 2 machine A1 22 machine A2 2 machine A3 EN 7 cell B previous process machine B1 BI machine B2 02 m machine B3 Mn time a Depending on previous job processed by
31. 8877 6 1771 0 11 1 0 11817 4 1 70 0000 0 0 D uu 0 111 1 10 0 1 1 0 0 70760 0 00 4 000000000008770 0 0 0 EJ E3 EH EJ EJ EJ L3 Ed E3 L3 104 129 0 9 ETT EST 40 1 5531 ESTEE a EST EL EET 1 T EST EST E S EESTI TET ES ES SESTO
32. 4x3Q 0 0000000 0000 00080 0000 1 0000000007777730 0000000 70 000 1 1 1 1 0 e e ET EST ESI es EA L3 E3 C3 E3 L3 L3 0 1 07770330 r3 L3 L3 L3 L3 42 00 08 421 1 00 0 0 1 1 uuu 10 0 0 1 4 D D job D D U product D D D U U process D D D OO machine 0 1 U D operation DB HB D B 000 1 go 00 GO 1 100 0 s 00340000000 starting time f 70000000 finishing time p 00400000 processing time release time 8 0000000 1 0 ALGOL Pidgi
33. 020 8 0 FA Factory Automation NC Numerical Control 1 4 4 0 110 0 00 0 1 1 1 1 0 lt 2 3 2 0000000000 U SCM 0 1 10 00 010 11 0 240 10 nOOOOn 7 V J1 y gt gt gt gt gt am Bussiness manager Material flow System resource gt Information flow C Subcontractor 7 Storage 2 4 Toyota style manufacturing system 7770 1 1 4 1 1
34. 11 1 0 1 1 08 4 0 0 1 1 0 D uu 4 5 DO 1 10 0 17887380 0 1 1 1 1 0 1 1 0 1 0 D uuu 83 4 5 0 0 10 0870170 00 0 0 000000 6 LI Oo N DODOD DOUDOU 00 1 1 1 0 0000 1 0 0 1 0 Om noo Fes sea Ez Oo
35. 1 00 72310 1 CMO 61 1 111 318 gt 70000000000000 Oooo r3 z 2 34 0 11 0 00 88 00 0 009000000000008000000898 0 88088208404 80 00000000000 0 1 1 1 710 ni 1 System resource
36. 1 1 1 1 0 108 O60 Ul 1 1 1 0 0 1 1 ull 0 1 1 1 000 1 50 20000000 0 1 1 181 18 0 0 1 4000000000000 0 0770
37. 1 1 18 Step 2000000000000000 Step 2 Q00 0000000000000000 0000000000000000000 Hi P 1 while gt n do FoF maz r k fk Af Fok fk fok k k 1 fi fyi or Af 0 then Afr 0 goto Step 3 else goto Step 6 0 77 43 4 073108 300 200 0100010000 000070 460000000000000 ADU 0 0 1 1 8 871 1 1 108001878008778 0 000 1 5 Ooo 4 6 6 0 8 0 1 1 1 CU 4 6 00 0 4 50 0 0000808878 0 88 7 707807 6 OOO 1 1 BON 70 0 1 1 000 1
38. 1 107707 0 0 1 7 7 1 1 0 0 1 1 0 1 11 4 60 1 1 0 1 1 1 0 1 1 0000 0 1 0 1 1 0 160 8 1 4 0 1087077707 730 0 1 1 0 0 1 1 1 8 10 0 1 1 000 1 9 0 1 1 000 0000000 1 1 1 1 0 2 1 4 3 1 3 740 000 71 0 1 1 0 71 machine time 8 Report time ta ta as a result caused by noti fication N2 machine time h Return reported time ta ta as a result caused by notification N1 machine time i Adjust starting time of second operation machine time j Adjust starting time of third operation according to finishing time
39. EST EE E 3 3 a 102 O50 70 Enterpri ee aa Truck 1 L EN Truck 2 3 m Factory 1 TT Lift 1 0 1 1 1 Lift 2 1 Shop 1 El a 00 AGV 1 1 8881 DIE NH NE 888 8 8881881 81 1 8 0111 CIN a 8088 88 AGV 2 JUIN Tong 5 1 1018 18 NEN RUM 18888 CUN FU 81 11 BH 188 AGV 3 BENED 1811 8 E MEDI CEU IT 8 81 ums Bains mm Machine 1 wis man E LT Machine 2 8 8 m mu mum 88 2 EH onn S Machine 3 8 5107 8 an In gg EN HN 801 um Machine 4 WM 1 EE 8 m 8 E 17 yu mH Bs Wi 18 Machine 5 jim 8 Houn mE HN rT m Machine 6 W NN 8 EHI s mu min PS 1 0 Shop 2 E LI I a Shop 3 m LI gt Factory 2 C C Shop 4 555521 fE L a AGV 4 8088181 00 7 7 1110 171 LTT 7 Machine 7 8 m wi
40. 3 17 8100800081104 0 2 1 1 10 00 770 OU 3 40 1 1 1 0 3 1 080 0008000 0 1 4 1 730 EK E EN Es 1 OO 3 4 Estimated state of tool wear Application to unknown data The data corresponds to the experimental results shown in Fig 3 17 D depth sec AA depth 5 15 25 35 45 5 15 25 35 45 1 creator wear creator wear W pa MW Dh C2 wow 2 3 3 3 3 3 3 3 3 3 N N N N WWW WR e e e 02 C29 C2 N N N N N Note Numbers 1 2 and 3 corresponds to initial middle and final state of tool wear respectively 35 1 47 3 5 1 1 1 77710 0 1 CPU central processing unit 0 0 0 1 1 1 1 1 11 02 0 1 1 0 0000 0 10 0 1 1 2910 000 11 0 Dono
41. 11 70 0 1 1 1 08 00 1 9 Oooo 0 0 10 0 10 0 1 87740 0 1 0808070 EST 30000 0 1 0 1 1 1 1 4 2000008 0 0 10 0 8870 000 770 1 11 8087777777330 0 1 1 1 0 0 10 707777310 00000 0 90 OO 21 a ET E ESE E E 0 0 0 0 0 0 1 4000 0 1 1 1 0 0 0 1 gt 0 1 1 0 10
42. 1 11 6 315000000000000000000 AED DO 0 3 16 000000000 1 1 0 4000000 0 0 8 0 0 1 5 0 500 O Macimum depth of crater wear Mean flank wear A e T co T CD e gt o 0 Mean flank wear um e eo T N o eo T T Maximum depth pf crater wear a o T o gt 30 gt 10 20 Cutting time min 3 15 Examples of wear of coated tool Cutting speed 200 m min Depth of cut 2 0 mm Feed rate 0 38 mm rev 3 160 0 0 0 1 1 2 1 1 90 6em OU 0 1 1 110877 gt 70 01 1 1 1 70 8 0 1 1 1 8 1 1 1600 0 60000088080 317000000 1 1 1 1 01 1 1 1 1 70 1
43. 1 10 0 3 2 L1 E L1 EJ L1 EQ L1 L1 L1 L1 E oO L1 0000 8777340 1 1 1 0 1 11 00000800000000 1
44. 1 1 88 1 B B D p d Ud 8 011 1 1 1 1 0 1 1 160 Start Bits End Bits Compensation Data Machining Program Compensation Data Shift of Z axis 8bytes um Shift of X axis 8bytes um Machining Program NC Commands 0 1424 bytes Override of Spindle Speed 2bytes Override of Feed Rate 2bytes Structure Code of NC Lathe 2bytes Size of NC Commands 2bytes reserved by system for future extension 3 23 Details of information computer to machine tool 3 24 1111111111 1 1 07710 1 1 1 1 0 1 U TCP IP Transmission Control pro tocol Internet Protocol B 1 1 932511 11111 1 gt 1 1 11 8000 L L 1 1
45. 04 30 0000 000 70 5 3 5 3 1 0 1 4 1 1 0 0 0 00 0 1 00871770 0 0 1 gt 30 Modification of Notification of condition for change from operation previous operating machine Notification of change to next operating machine Report about modification to previous operating machine Add results of modification to reported information Report about modification from next operating machine 5 7 Information exchange among machines in a layer at the same level 53 1 1 1 95 5 3 2 0 1 1 0 1 000 1 100 0 7 1 1 0 70 008107 1 0
46. 1 1 4 1 0 0 1 1 1 1489 7 0 000 1 0 0 254 158 00000000000000008000000000000000 DOU OOOO Ou 0 gt 8088 00 0000 00 1 2020 DU unu 0 0 0 8 7060 0 1 2 12 8 0870 1 1 0 0 1080700877 4 84 10 0 1 4 7 6 nm Lr rr EE A ES EL ETEN L3 L3 L3 EJ E3 ao 25 0 1 158 19 a Holon including non holons b Non holon composed of holons 2 12 Relationship between holon and non holon 1 6 0 0 0 0 0 0
47. 1 11 1 4 0 0 1 1 1 0 0 1 1 4 2 5 5 10 0800 9 1 1 0 0 1 1 0 1 0 788 0511060 000 1 1 0 1 1 1 6 1 1 1 1 1 0 20 020 0 0 11008 730 2 5 6 0 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 11 0 D uuu 2 6 OO 0 1 8 8776 0 1 1 0 0 0 0 1 1 4 0 1 1 0000000081 0 00000 1 4 0 Aa LI 1 1 1 0
48. 1 1 1 1 1 1 1 0 8 9 0 0000 0 gt 0000031 1 1 0 1 1 0 1 0 10 Notification from upper level ES to ES level T o Notification Xp ito lower level Report eport from A upper level d 5 8 Information exchange with elements in upper and lower level layers 5 3 3 160 1 70 1 1 0 0 1 4 1 0 1 81 1 808077 70 1 0 81778 23 96 51 0010800707782 0 message n is received yes Y yes goto step 5 from ye lower level 0 record job notified no no no goto step 0 e ran move target 006 66 to job notified no goto step 1 yes from record broadcast upper level event X goto step 0 Jen from yes lower level goto step 3 goto step 2 e from y same level
49. 08 0 OOOO 2000 6 0 0 00000770 1 1 2 1 1 1 710 11 4 80 1 6 6 24am 000000 20 24 7 10 but 8 0 70 00 U 3 OU 0000 000 D uuu 30 42 0 11 3 19 10 78060 000 011 7 0 2 0 10 1 5 L 0 8 o g 06 1 8 1 0 z 2 x 0 4 05r 02 L 0 10 20 30 0 10 20 30 time min time min a Mean d Skewness 1 0 05 5 0 04 4 E amp 0 03 8 3 0 amp c 8 002r 2 M 0 01 17 0 10 20 30 0 10 20 30 time min time min b Variance e Skewness 2 0 5 25 04 20 t gt 5 03 5 15 5 L 2 L 02 x 10 0 o 2 01 F E 0 10 20 30 0 10 20 30 time min time min c Coefficent of Var f Kurtosis Cutting speed 200
50. 1 1 1 70 0 1 00 0 7 6 1 0 0 1 1 OO ABO 0000000 3 3 1 710 0 AE Acoustic Emission 1111 4 17 5 OO 10100 6 4 6 114 4 4 111000 1 1 2 1 1 1 080 1 1 080870 1 1 71 OO 4 E30 0 1 11 OOO 4 0 00 1 0 76 00 00 00 00 00 3 1 7608 29 ugggaugnagagagunagagugaunagngagaaggagug 1 20 1 1 110 AED 0 1 1 0 0 1 0 3 5 1 1801080002231
51. 7100 1 1 1 11 8110070 0 uuu 35 2 7 0 0 1 1 4 1 0 1 1 0 49 0 1 35 Size of NC status 2bytes Status of NC Lathe 8 bytes Sequence No 4 bytes Override of Feed Rate 2bytes 5 Override of Spindle Speed 2bytes Spindle Speed 2bytes rpm Detected Position of X axis 4bytes um Destination of X axis 4bytes um Offset of Origin 4bytes uum Spindle Torque 2bytes Offset of Tool Position 4bytes um Inner Counter 2bytes Effective Shift value 8bytes um Program file name 20 bytes X Axis Torque 2bytes 111 Axis Torque 2bytes 5 Effective Shift value 8bytes um Offset of Tool Position 4bytes um Offset of Origin 4bytes um Destination of Z axis 4bytes um Detected Position of Z axis 4bytes um reserved by system for future extension 3 24 Details of information machine tool to computer 4 NC Program b Status Information EWS Cutter Position Turning Machine Spindle Speed Estimation of eius Deformation Feed rate an Calculation of Cutter Path to be Modified Sequence Number of 7 Output of Sensors 7 P d Temperature Sensor Modification of Cutter Path O 3 25 Control schema to compensate machining error 01 1 1 1
52. 0 11 1 5 0 1007078 87730 0 1 11 00 0 70 000 0 1 gt 0 1 1 4 0 1 8 0 D uu Changer 0 11000 0 200100188 80870 5 2 3 DOUDOU 0 1 7 10 5 1 0 8 730 i 08008710 08 8 7080 e 0070 7 00 00 0 00 168 Je OOO 70 fs 710 0 0 7 prs 310 c O00 000 0 6 ana 524 e eP 1 i n gt 0 817802 1 5 92 1 gt D uuu e e 5 2 0 1 nns 0 J JIP 1 lt N gt 1 8 gt ns 5 3 fr
53. DOU 0000000000000000000000000000 833 7 or 10 03 ol L L 1 OOO 1 1 1 31100000 3 00000000000000 33 3 11 Processing in unit and sigmoid function 0 11 1 0 0 D uuu 3 12 Relationship between weighted output and input 31200000000 10000000000 0 0000080880 w 0 0 0 00 m Lo L 1 L 1 4 1 OOOO 1000000 000 2000000 s00000 3 0 70 00010 30 34 34 7720 1 1 1 0 1 1 0 000 7 1 DODOUD E ln or 3 1 k 1 0 1 1 1 06 181 00 Av 00000 Aw 00000000000 0 0 0 0 10 OE Ow ji 6 0000000000000 OE eer 3 3 0 3 3 6 1100 Awji ry 3 2 Awl ob ji T Oui tlok 3 4 0101 67 00000000000 1
54. max fje J gt 0 5 4 max rye py J 0 1 1 1 1 JOGO OOO 1 1 Tia eed 5 5 0 00 0 10000000 1 J O0000 67s00000000000 gt 0 000000000000 00077065 525 00710 1000 008 00 0000000000 00870 5 6 b 0 00 000000000 000000 56 4 gt 1 6 0 10000000 0000 4 0 0 0 70 00 000000000000 000 0 10 8 00 0000000000000 0 0 0 00000000 00 0000000000 1 1 7 00 000000000000 4 16 0 1 1 1 8 800 52 00 10 93 Cell A Machine A1 Machine A2 Machine A3 Cell B Machine B1 Machine 2 Machine 85 7 a Selection of jobs by cell Cell A Machine A1 Machine A2 Machine A3 Cell B Machine B1 Machine B2 Machine B3 Cell A Machine A1 Machine A2 Machine A3 Cell B Machine B1 Machine B2 Machine B3 Time c Fixation of finishing time of job 5
55. 1 0 1 1 1 1 1 1 1 1 71 1 1 1 1 1111 23 1 1 9 0000 70 0 1 1 8 0 1 8 0 0 SCM Supply Chain management 000000000 POS Point of Sale 0 atene 0 7 0 1 88 0 1100 gt 7 DOD 2 3 3 DODDOUDOD 0 1 1 30 0 1 0 0 11 188 0706 gt
56. EWS CNC Sensors Interpolator Servo Servo 2 X Turning machine 3 3 Allocation of EWS and CNC lathe Newer Superordinate Controller CNC Sensors Turning machine 3 4 Logical structure of CNC lathe 1 1 1 0 0 0 0 00000 0 0 1 0 4 0 33000000 0 88 888 8 7710 0 0 1 2 00010 30 28 0 10 gt gt 730 0 1 0 1 1 0 00000 1 1 1 U 3 4 008 1 1 0070 70 1 8 110 1 1 1 0730 000 1 0 1 1 30
57. 1 1 70 2 4 0 888718 gt 07230 1 0000 1 4 0 25 8 00 251 1 110 00 Market Cooperation domain in which Customer and Enterprise negotiate Sales Division Manufacturing Division 3 i Design Division Machining Cell 2 9 Example of hierarchy of manufacturing system 16 020 gt 0 customer 0 OO enterprise O U 00 800070 000000 facory 1 U sales divisa 1 4 71
58. 1 1 0 708 0 1 1 1 0 570 0 gt 110800 87070 77878707 8730 0 1 gt 0 70 1 00 1 1 7 710 00 07 0 1 1 Active Information Acquisition Monitor of Status by Sensors Passive Information Acquisition Receipt of Task from Outside 26 Operation from Outside DODUD uuu DODUD DODOD 1141141114 0 DODOU 1141110111 DODOD utu utu DODOD DOUDOU uuu DODOD uuu 3 2 2 uU uuu DODUD uuu 0 0 uuu DOUDOU 27 0 32 Network
59. 1010 30 44 0 1 1 1 000000070 3 3 Estimated state of tool wear Application to unknown data The data corresponds to the experimental results shown in Fig 3 17 Mn depth sec pon rann depth 5 15 25 35 45 5 15 25 35 45 i creator wear creator wear pa w ww W ww N N N N WW kr RRR wn N N N N N WR oo 2 OW NW WD wer 3 1 3 1 2 2 2 3 3 2 3 3 3 3 N N N N N N N N N N WW e e e N N N N N N N N N Note Numbers 1 2 and 3 corresponds to initial middle and final state of tool wear respectively 11 1 1 1 80106 1 1 0800087 1 1 1 71 1 1 1 1 0 1 0 1 0 310 lt 1 10 1 1 1 1
60. D FA Factory Automa 89 00009800000800000800000 0000 00 00880000008000 008000 0000000000000 000000 0 0 OO armory practice 00000 00080000 088000 000000 0 008000 00000000080000 D0000000000000000000000000000000000000000000 D000CCCOCOO0O000000000000000000000000000000000 00oooooooooooooooooooooooooooooooooooooooooa 00000000 0000000000000000000000000000000000000000000 0000000000000000000000 90 00080000000230000000 0000080800080000080000008000000000 000 00000000000000000000000000 0 00000000 0000000000000000000000 00000 0 0 0000000000 0066888666668666 8 0000 000 088000008000000 60 2300000000000000 000000 000000000000000000000 0 000 0 0 000000000 00008000000800000088000008000 00000000080000 1 0 1 23 mn lesse lt gt gt Bussiness manager mm Material flow System resource gt Information flow Subcontractor Storage 2 3 Ford style manufacturing system 0 1 0 0 7700 0 1 70 DOD
61. 1 1 1 1 1 1 1170 0 1 1 11 1 0 0 0 1 1 1 0 1 4 16 0 planning 1 0 0000000 scheduling O O OOO O O O 0 1 1 1 0 50 0 1 1 1 0 0 000 1 0 11 87727830 0 1 0 1 1 gt 9 0 1 1 02 0 1 1 1 uu 0 1 1 1 0 1 1 700 01181 70 02 1 1 0807 730 0 1 1 1 1 1 70 8 1 1 2000U0000000000000000000000000000000000000000 444 010 0 11101 8 PC per
62. 1 11 1 0 1 777 0 0 1 1 0 agit 00000000 0 08007707778 8 7 230 1 70 D uuu 0 1 1 1 0 1 1 0 0 0 008086808 0 1060 ESI EE ka ET SESS ole Bel ERI EST EI EZY 000 1 1 00 740 000000 000080870 7060 1 1 000
63. 1101 200000000 0 8 09 0 1 1 1 0 0000 0 3 0 00 8 0 1 1 1 0 0 0 1 78071020 000 1 0 0 a a 21 UU 1 0 776 60 0000 1 4 Oyvind Bjorke Manufacturing systems theory geometric approach to connection Tapir publishers 1995 M Eugene Merchant Progress and Problems in the Application of New Optimization Tech nology in Manufacturing 1967 David A Hounshell 00000 00000000000000 O 1800 1932 OOO 000468 0 1 C 104 No 12 pp 303 1984 311 Reference Model for Shop Floor Production Standards Part 1 amp Part2 ISO Technical Report 10314 1990 OOU0 0000 000 CAD D D 1993 1 1 U 1993 U 1994 Mineo Hanai et al New Autonomous Manufacturing System Adapted for Uncertainty in Market Proceedings of the Second International Workshop on Intelligent Manufacturing Systems 1999 pp 15 22 1999 APS 1 8 8 8 Vol 65 No 8 108
64. 70 3 3 2 000000000 1 1 01 80000 31000000000000000000 0000 1 6 1 00 00000 1 0001060 30 3 7 Apparatus of Lathe Specification of experimental equipments 000 00000 0000000 450mm 00000000 800mm 00000000000000 ACG 0000000000 TiC TiB 00000000 Apo 7 000 6 10 000000 U UU CSBNR2525 00000 6 0800 5 0000 60000 5 00000 5 00080 25 25 x 25mm 0 0 0 000 0000000008000 00000 268 00000 00000000 840 3 1 0 00 710 000 10 3 00000000000000 41 3 80 1 1 1 1 1 3900000 1 _ 1 1 0 0 1 1 6 ee Tool holder Spacer T AE Sesor Sesor holder 3 8 tool holder workpiece j tool AE sensor pre amplifier discriminator data recorder A D comverter computer 3 9 AE system 3 3 3 0 888
65. 0 8 70 1 _ 6 8an 9 909 0000 70 0 8878 0 lt 07 0 1 0 00 11 1 2000 01000000080 0070 1 1 9 0 0 730 1 1 000 39 a 0 3 0 2 0 9 0 1 a ANN a nal 0 0 02 0 04 0 06 0 08 0 10 time sec 4 min 30 sec after start of cutting gt 0 3 0 CD L 2 0 1 1 1 1 1 0 0 00 004 0 06 0 08 0 time sec 12 min 30 sec after start of cutting gt 0 3 2 E 1 1 1 0 04 0 06 0 08 0 10 time sec 1 0 0 02 20 min 30 sec after start of cutting 0 3 M SG 0 2 1 1 0 002 0 04 0 06 008 0 time sec 28 min 30 sec after start of cutting am RM 34 0 730 7 0 3 2 0 2 t 0 0 2 0 1 0 0 00 0 04 006 0 08 time sec 30 sec after start of cutting gt 0 3 0 2 0 1 1 1 1 0 0 00 0 04 006 8 time sec 8 min 30 sec after start of cutting gt 0 3 5 0 2 t L 2 0 1 1 1 1 0 002 0 04 0 06 8 time sec 16 min 30 sec after start of cutting gt 0 3 0 2 0 002 0 04 0 06 8 time sec 24 min 30 sec after start of cutting 3 16 Typical examples of RMS value of measured AE signal at various stages
66. 4 1 Schedule in a shop with two machines and three products 1 11 00 1 1 1 1 112 0 0 4 1 2 07800070 1 1 0 11810 11 1 _80 1 1 1 1 1 1 6 401 BLU 710 8 6 11 0 1 1 1 1 1 1 0 tL L L 000000000 42 00 1 1
67. EJ OD m UU DOU DOG 0 7 00 00 0000 70 0000000 1 7 0 0 11 0 000 0 0 8 70 O N 000 0000 1 10730 000 000 80 00000000 0 000000 0 0 1 0730 7080 0 000 0080 20000000 00 60 OOO E3 0O 0 0 p p E 76 4 A T E 44 OOO TT Utilization factor of machines 0 0 50 100 150 200 250 300 350 400 450 0 Iteration of information exchanges 4 14 Convergence process 000000 1 4650 0 0000807010 0 1 1 1 7340 0 0 1 1 0 U 4 141 10000000 0 0 0 0 0 0 0 10 1 0 10 0 00 00080770 00 00 00 00 DOU 500 0
68. 44 OOO 75 600 500 1 Conventional Method X Proposed Method 400 r 300 200 100 r gt Avarage of Information Exchanges 0 5 10 15 20 25 30 3b Number of Products 4 12 Relationship between number of products and amount of information exchanges among machines by improved recursive propagation method U 41l2000000000000000000000000000000000000000 442 11 00 0 0000 0 0 0 0000 770 0 000000 1 7 EJ LI Ooo Ey DOU Uu DOU DU UU DOU 0 0 0 U
69. 2000 01 00000 0000 PearsonQ 000000 1 1 1 0 1 1 0 1 0 mode x 0 OOOO UU 0 40000000 0 4 0 008 41 1 1 1 0 0 4 1 1 000 000 00 0 1 1 0730 DU 0 EE EI L1 E 00000 0070 0000000 0 OO 75 8 1 0 1 000 000 U 3010000 01080000730 10 30 38 3 4 2 0000 0 000 0 0
70. m u Oil Air Pre process gt In process gt Post process 3 1 Logical structure of machine tool 32 1 25 O00000 1 0177 8 gt 1 0 0 1 gb 0 1 0 1 1 0 1 8 8077030 0 8 0 O00000 1 44 8 0 1 1 0 0 1 1 bu 0 1 080 0 1 1 0 1 1 1 6 U U between process
71. 0 040 C 0 10088000 46 b 4 6 OO 8 8 0 BO 0 20000000 00 1 18808077700 DOUD E 0020 0100 3000 OOOO COU 20000000000 000000 B0000000000000 46 8 7876 7 4 00000 08 0 1000000 08 00 0 0 DOU 00 D uu 0118 1 E E ES EINES D D Uu 300 20000000 GO 4 0 0 81 10 0 AO 30808088 7 00
72. Ey O 0 1 7 68 0000 10 84 UU 1 Nils R Sandell Jr Pravin Varaiya Michael Athans and Michael G Safonov Survey of decentralized control methods for large scale systems IEEE Transactions on Automatic Control Vol AC 23 No 2 pp 108 127 1978 C Vol 104 No 12 pp 303 1984 311 John F Muth and Gerald L Thompson Industrial scheduling Prentice Hall Inc Englewood Cliffs New Jersey 1963 0 1971 Jacques Calrier The one machine sequencing problem European Journal of Operation Re search Vol 11 pp 42 47 1982 J Calrier and E Pinson An algorithm for solving the job shop problem Management Science Vol 35 No 2 pp 164 176 1989 Yatna Y MARTAWIRYA 1 11 1 0 000000000 C Vol 58 1 No 5 pp 58 63 1992 1 C Vol 63 No 12 pp 324 331 7 OO00 0000 000 000 000000000000 00000000000000 OOO00 0000000 C 109 No 4 pp 291 298 1989 1 1 1 8 1 D Vol 31 No l p
73. 270 Ane pr Lift 3 Shop A A Factory 2 5 14 Schematic illustration of the hierarchical manufacturing system 0 1 1 0 1 1 0 108 71 0 1 0 1 1 70 0 10800800800730 2001000000770 0 136 0 1 1 1 0 1 8000710 1 1 1 0 E3 L3 L3 E3 OO L3 EJ 5 4 OOO 101 54 DOO 0 1 0 0 1 16 870 1 310000 0 000000000 2 5 30 00000 0 1088087 306 0 0 6
74. 0 000 0000 0 000 0000 0000000000 00 800000000000000000000000000000 0 000000000000000000000 00000000 1 000000000000000000000000000000000000 0000 0000000000000000000 0 000 0 00 000000 0000 0000000000000000000000000 0 7 68 00000 52 0000000000 0 000000 00 0 00000000000 000000 000000000000000000000 00000 000 00 0000 00000 000 000000000000000000 00 00000000000 00000 0000000000000000000 00000 00 0000 00000 0000 000008000000000000 1016600688 7 8 0 000001800088 3 52 00000000000 521 10 6 1 11 1 5 1 68 0 1 1 1 8 1 00 0 factory 0 0 0000 5 1 0 OOOO 5 16 00 1 industry 52 000 1 1 87 0 000 0 1 0 8 0 0 1 1 11 i b e 0 1 a Machine
75. 0 00000 automated warehouse 1 1 1 0 O Numerical controller Hh 0 0000000000000000 1 1 1 18 1 8 8 1 1 1 1 1 100081071 80 1 1 1 1 1 1 1 1 1101 1 0 1 1 1 1
76. 3 1 4 518000000 80 8 7700 00000000 008 00070723808 L3 L3 L3 r3 C3 0000 53 BOO 10000000000 8 4 0 1 1 70 10000070 0 1 1 1 0 4 0 1 1 1 0 1 0 1 1 1 08 000 5 1 Structure of system elements System Elements Enterprise Factoryl Factory2 Factory3 Factory4 170061 2 Factoryl Shop1 Shop2 Shops Lift1 Lift2 Factory2 Shop4 Shop5 Lift3 Shopl Machinel Machine2 Machine3 Machine4 Machined Machine6 AGVI AGV2 AGV3 Shop4 Machine7 Machine8 Machine9 AGV4 Shop5 Machinel0 Machinell Machinel2 AGV5 EST ESI EST ESI ET ES
77. ANE 0 gt mer Kobe University Repository Thesis Bg BUD LV AT LICE 2175 REET hh Thesis or Dissertation 233 ANARE tE X 68 65 COD 5 5900876 XX lt FE amp 1 Create Date 2015 11 06 0 8 0 9 EA Se 0 0000 D ale Dahlen kdk ma BEBE see meets ele ae ellos aK KO 231 232 00 80080888888 8 As AT A 234 00000000000 ae area 235 0000000000000000 2 ts A 2341 00000 0688 itea prae da SAO NT RN NR eae 243 000000000000000 OE GE EN 0 1 NICE cans rat e 251 000000000000 ee 252 Ma a aa era ls rr ER a 253 254 000100000 255 000000000 2 wy 256 0011110 sss 0000 10 8 oath DNE TEUER Se a 321 322 000000000 ED DADE ee e ra kok s 331 AEDNOOUODOUOOUOQO 2 EE Hb
78. EST EZ TEST EST EST 1 EE 1 4E 5 6 OO 105 5 6 OO 0 1 1 4 1 0 0 0 077170 0 1 1 888 0 0 1 1 11 1 0 1000 10770 20 4 1 3 0 1 0 1 8106 0 0 1 1 1 0 1 1 1 1 1 0 0 1 DU uuu 000 1 0 1 0000 0 106 UU 1 John F Muth and Gerald L Thompson Industrial scheduling Prentice Hall Inc Englewood Cliffs New Jersey 1963 197 0000 2 3 J Calrier and E Pinson An algorithm for solving the job shop problem Management Science Vol 35 No 2 pp 164 176 1989 4O000 0000 0U000 000U000000 00000U00 C 104 No 12 pp 303 311 1984 7
79. i structure of system 2 1 Model of system 2200000000000 21 00000000000000 0 1 70 0 gt 2 2 Model of element 0 1 1 0 Di 6 020 0 0 0 8 8 8 1111 1 1 0 70 0 0 8878070 00 0 187700 1 1 1 1 1 1 1 0 2 3 E Merchant 1 88080 8 1730 000000000000 1 0 0 1 1 11 0 0 7770 400 1 1 0 material flow O O O O 0000000 information flow 000000000 L3 L3 OOOH 2 3 1 1 000000000000000000000000
80. no from Jes upper level goto step 4 5 9 Flowchart of recursive propagation method for the hierarchical manufacturing system part 1 6900 notic QO 81 DUDO report D U O 0 token 0 11 1 1 0 0877078 6 87310 53 1 1 97 lower level notify to lower elements level elements exist no goto step 0 calculate finishing time goto step 2 yes ye release date next process notify to same is changed exists level element no no goto step 4 goto step 3 goto step 0 next p move target operation to next operation no goto step 1 goto step 4 5 10 Flowchart of recurs
81. 0 0 1 16 0 C P Y 1 Y Bussiness manager System resource Subcontractor Material flow 1 1 gt Information flow Lew 2 5 Distributed manufacturing system 1 1 1 0 0 0 OOO 2 5000 8001760 distribute 0 0000000000 0 0 0 0 1 1 od 1 020 10 00 10 CIM Computer Integrated Manufacturing OOOOOOOOOOOOOOOOOTO computer uggggaaagguggagggguguagggrFAnmmuuuugagagagagaagnmuuuu CI 1 1 1 0 1 1 1 1 1 1 1 0
82. 55 a 50 ki Utilization factor by recursive propagation 96 50 55 60 65 70 75 80 85 90 95 100 Utilization factor by most operation remaining rule 96 b Comparison with most operation remaining rule 4 15 Computational results of scheduling 0 1 1108700 0 1 1 0 0 8 81710177708 44 OOO 79 1 10 10000 78 0 0 1 0088008000880800 gt 20 1 1 1 4 1 0 1 1 0 080 0 1 08000 gt 730 OOO 15100080000000 8 807700 0 1 1 1 02 0 0 000 1 1 11 60 11 1 0 0 1 1 0 00000 1 1 60 0000 D uuu 0
83. B C time time c Modification of starting h Report of modification time and notification of results the change machine machine time d Modification of starting 1 Final state time and change of target operation machine time A Notification of change gt Report of modification result v Target process V Target operation time e Receipt of report and change of target operation 4 6 Modification of schedule caused by change of starting time of operation on machine A and flow of messages exchanged among machines A B and C 70 040 7308 1 1 8 lulu 000 1 4 800000 010000 000 1 000 0 470000000 0 1 1 0 1 1 2 0 000 1 0 0888 8 100000000000 B 48 00 0 0 4 8 080 0 AD 0000000000000 48 00000000 4 40 0000000008 0870770 1 30 30 1 11 6 1 4 70 0 008800800 1 470000 41070 1 1 70 000 0 0
84. EST Eariest Starting Time 1 U U U EFT Earliest Finishing Time 0000000000 SPT Shortest Process ing Time 0000000000 LPT Longest Processing Time 000000 FCFS First Come First Served DO B D O O U FOR Fewest Operation Remaining j O O U OOOO MOR Most Operation Remaining 0 700000000 dispatching rule 41100000000000 200 1 1 0 xOO0000000000000000000000000000000000000 1 1 4110 1 1 1 0 1 1 730 1 1 80 1 _ 1 1 1 1 1 1 1
85. Feed Rate E 20m min 10m min S 5m min 6 o z E 0 x 1 0 60 120 180 240 300 Time min c Thermal Deformation in Z Direction at Cutting Point 3 27 Examples of temperature rise measured at representative point and thermal deformation in Z direction 10 30 94 80 Expetiment Feed tate 20m min i Estimation o eo FS Deformation U m 0 60 120 180 240 300 Time min 3 28 Comparison between estimation and experiment in Z direction X Workpiece 2 direction tool Feed direction Center of workpiece a without comensation control Feed direction lt lt lt Center of workpiece b with comensation control 3 29 Example of compensation control 55 UU 1 Toshimichi MORIWAKI Eiji SHAMOTO Rei HINO Modeling and Monitoring of Milling Process for Intelligent Control of Machine Tool Proceedings of the 3rd CIRP Workshop on Design and Implementation of Intelligent Manufacturing Systems pp184 192 1996 TOSHIMICHI Moriwaki and REI Hino Application of Neural Network to AE Signal Pro cessing for Automatic Detection of Cutting Tool Life Proceedings of the First International 5 Conference on Automation Technology pp811 818 1990 3 11 1 1 1 1 1 1 1 7 Vol 57 No 7 p
86. LU D uu kai EE P 1591 10000 1000 9 0 70 1 1 4 0 1 Du 000 1 4 DOU EE E EJ E pen fad ea ai oP 0 443 0 11081 0160 0 1 1 7 0 0 11 0 1 1 1 808 8 0 0 1 1 0 000 1 040 78 100 T T T T T T T T T 95 Lis H ge 58 90 7 1 Fa hee 85 E B MR FET 80 ur 1 d 4 Toit ste by 75 4 i 70 Ji 65 d 1 er 1 55 Z 50 Utilization factor by recursive propagation 96 50 55 60 65 70 75 80 85 90 95 100 Utilization factor by earliest starting time rule Yo a Comparison with earliest stating time rule 100 gt mr 95 90 L 1 85 80 75 70 P3 65 f oL j
87. hy Subcontractor gt Material flow lt gt Information flow J 2 6 Autonomous decentralized manufacturing system 24 7710 11 0 1 1 0 0 1 1 OOOO 2 6 1000 0 distribute 00000000000 decentralize 0 0 0 4 70 1 7308 4 0 0 00000 070 0 88 0 LI r3 LI 0 0 800000000070 0 LEER EI EE E EE 08708777380 000 1 1 000 an 0 1 0 5 1 5 5 3 5 0000 0 08 2 3 5 1111 111 0 10110 00 0 1 1 1 1 4 6 0 1 1 10 0 1 10000870787 6 71 0 1 1 1 1 10 0 1 1 1 1 1 1 0 00 10 0 1 1 1 0
88. p330 1981 M S Lan and D A Dornfeld Experimental Studies of Tool Wear via Acoustic Emission Anal ysis Proc 10th NAMRC McMaster University Hamilton Ontario May 1982 7 5 00000 8088887360 8 0000 0 9 D E Rumelhart G E Hinton R J Williams Learning Internal Representation by Error Propagation Bradford Book MIT Press Bambridge MA amp London pp318 362 1986 T Moriwaki M Tobito A New Approach to Automatic Detection of Coated Tool Based on Acoustic Emission Measurement Sensors and Controls for Manufacturing Presented at The Winte Meeting of the ASME PED Vol 33 pp75 82 1988 Altintas Y Prediction of Cutting Forces and Tool Breakage in Milling from Feed Drive Current Measurements ASME Journal of Engineering for Industry Vol 114 386 392 1992 Okuma Co OSP7000L User s Manual Vol 1 1996 Bryan J International Status of Thermal Error Research Annals of the CIRP Vol 39 No2 645 656 1988 Dornfeld D A 1990 Neural Network Sensor Fusion for Tool Condition and Control Annals of the SIRP Vol 39 No 1 101 105 1990 Mitsuishi M and Nagao T et al An Open Architecture CNC CAD CAM Machining System with Data Base Sharing and Mutual Information Feedback Annals of the CIRP Vol 46 269 274 1997 00000000 AE0Q0000000000000000000000 000000 Vol 57 No 7 pp1259 1264 1991 Toshimichi MORIWAKI Eiji SHAMOTO Rei HINO Jer Wen CHEN Development of Work Clamping Sy
89. 0 4 0 716 0 1 3 0 1877370 0 760 Oooo DU DOU U 24 10 8 8 80080000008 6 0 1 1 1 1 11 1 020 12 8 19 80 8 8 8 88 8 FFS Future Factory System 0 1 80730 80 0 0 7 1 9 1 4 1 0 1 000 0 1 1 1 1 60 0 1 1
90. 100 1 118820807160 1 1 1 7 41 00000 L3 Factory 3 5 12 4 1 1 0 111 830 11 1118 1 4 0 0 0 10000 mE Rt e 27 CW A p peo 1 4 a Factory 4 ELE we 9 Bele 21 11 3 215 SSS SSS SSS Truck SIT ETA
91. 2 0 8 a t aigTi t aaTi t dt ajjT4 t j x dt a 00 1 1 1 0 41 4 0 0 1 000 0 0 nm 1 1 1 1 1 300 1 7 200mm 0 0 070 3 26 10 8708 0 1 7 0 000 11 7 1 1 1118888840470 20 2 1 30 00 1080770 0 1 0 1 110100 00 0 00 5 10m min 20m minQ 3000 130 1 301 1 0080 4 8 0 1 20 min m minD D 30 D D 10m min 5 J 130000000 70 3 5 3 J DUDULU 0 3 5 4 0
92. 6 Scheduling procedure in the hierarchical manufacturing system 1 1 0 0 800800000 1 1 1 7 4 4 gt 7310 000 uu 0 5 2 6 0 11 4 4 1 0 0 1 050 94 00111100117 6 00000000000000 001011101010100 10 0 AC 01 0 C r7 5 6 OOOO 00100 gt 1 7 1 1 70
93. 7 31000000 0 E Ox do Ox rj 05 34 0 4 0 35 Xi 0 0 3 13 unit j on output layer 3 14 unit j on hidden layer 100 70000000000000000000 OE eS 4 zi 1 07 dof do 0 0 OE OxL Oo i Qaf dor Ou k 1 at ut hi ai 0F 3 5 1 4400000 110 A6 0 0 0 0 00 OE 380 36 3 700000000 OE _ OE Oo 00 0 oj h xj 95 6 3 6 700000000 OE S 00 OE dof ol OOF 7 OE Orb dot Pr oor oar wg as 07 k 1 3 7 3 4 1 OOO AEOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO 000 0 0 0 1 000 D mean 1 n 1 3 1 si 2 0 OO variance 15 9 go 5 xi 2 34 770 37 OOOO coefficient of variation 4 OOOO UU 20 0000000880000000 1 8 0000 1 1 D uuu 1 n 1 2 g n 0 3
94. lower 6 Je level elem report results to level elem is waiting for lower level elem report no no goto step 5 goto step 5 rocedure Yen done for same report results to level elem same level elem no goto step 0 no results are restore to acceptable previous job order yes goto step 1 upper no deliver token to yes goto step 0 new no notice from notify to upper upper level level element elem yes wait for goto step 5 reports from deliver token to upper level next element elem yes goto step 0 goto step 0 5 11 Flowchart of recursive propagation method for the hierarchical manufacturing system part 3 _ 1 0
95. m min Depth of cut 2 0 mm Feed rate 0 38 mm rev 3 19 Statistical values of AE signal plotted against cutting time 34 0 7730 43 U 3190000000000 3100000000000 08087086 1 8 02 0 1 0 D utu 300 o O Macimum depth of crater wear Mean flank wear Co 0 Maximum depth of crater wear 4 m 0 T o eo T A o T Mean flank wear um ES o 0 T eo r eo L 1 10 20 30 Cutting time min eo 3 20 Examples of wear of coated tool Cutting speed 200 m min Depth of cut 2 0 mm Feed rate 0 38 mm rev 3 2 Summary of estimated state of tool wear Result of self discrimination The data corresponds to the experimental results shown in Fig 3 19 depth sec depth 5 15 25 35 45 5 15 25 35 45 1 creator wear creator wear 3 3 3 3 3 3 3 3 3 3 3 N N FRR C2 C2 WW WW 02 WW WO Note Numbers 1 2 and 3 corresponds to initial middle and final state of tool wear respectively Usi 382000000000 1 1
96. of preceding operation and notify it to machine B machine time k Adjust starting time of third operation according to change in arrival time of operation and report time tb machine 1 Final state Notification of change Report of modification result A Operation notified 43 1 1 1 machine time time b Delay starting time of first operation by machine A fort and notify it to machine B machine time c Adjust starting time of first operation of machine B machine time d Adjust starting time of second operation according to change in preceding oper ation and notify machine A change of release time of operation modified machine time e Adjust starting time of third operation and notify machine B release time of operation of machine A machine time f Adjust starting time of third operation of machine B and report machine A time ta in response notification N3 4 7 An example of propagation of information according to conventional procedure 72 040 07308 machine machine a Initial state machine tt i time Notify change of release time of third opration machine N1 2 L A time B b Delay starting time of first R operation for time t modify time succeeding operations and d Report time t as a result notify machine B change of caused by notification N3 release time of first operation mach
97. 0 108877877380 000 0 1 E L1 Ea 24 7710 13 GED organism e organ eee tissue e cell e micro organ KASE NSS molecule 7777 atom tte pene ney kk kk bd bd dad d dada a nan kaa Rees a Holon and holarchy of organism battalion company platoon b Holon and holarchy of army 0 2 7 Holon and holarchy A Koestler YO 242 0 1 1 0 0 1 fs 19 2000000000000 bolonic 000000 000 1 0 1 0 0870730 0 1 1 0 1 1 1 0 1 1 1 4 70 000 1 1 0 0 80 8 U OCIM Computer Integrated Manufacturing 1 1 1 7718 nm
98. 0 1 0000 0 18 2010 8 888 0 1 1 0 32 30 0 1 1 11180748 14208010 0 16 0 1 0 1 1 1 0 00 1060 0 1 1 1 10 0 1 0 0 3 10 Neural network 0 1 1 8 0000 1 0 0 1 1 81958481 18949 970 OQ Delta Rue D Delta Rule 10101010 0 DOU Generalized Delta Rule 0
99. 0 1 01 1 0 1 1 35000000000000000000000 1 1 8 10 0 1 0 7 08 Maximum depth of Crator wear n Notch Men Main Cutting Edge Mean flank wear Titanium compound Carbide matrix 3 5 Tool wear 3 6 Structure of coated Tool 10 4 3 100010100 0 0 _ 1 1 8 0 1 DOU
100. 0 0 0 1 1 0 ay time a Before extraction of job rn 77 NEM time b After extraction of job 4 9 Extracting a job from operation list managed by one machine i time a Before insertion of job evel time b After insertion of job 4 10 Inserting a job into operation list managed by one machine 74 040 104 8000 7007 Average 6000 1 Maximum 5000 4000 3000 2000 1000 Amount of information exchanges 0 20 40 60 80 Number of products 4 11 Relationship between number of products and amount of information exchanges among machines 0 870 0 1 1 1 0 000 0 00000 000808788770 1 0070300 0 1080087870 0 1 1 1 1 OOOO 0 0 0 0 0 0 0 DO0000000000000000000 00 O
101. 00000000000 0 10 ADD 0000 100 00 DO Ed 0 10 0 0 70 08 2 1 Oo 4 3 5 20000000 00008808000 8 DOD 4 000000 310 8 AOD 8000000000000 BOO 4 AUD 0 8300000000000 0 ADO 00 1 1101 0 0 0000 3 710 0 8 380 ADU 0 47 00000000000 ADD gt 4 00 1 10000000000000000 00 000000 730 ET EET 43 1 1 69 machine machine L1 B 3 0 0 time time 1 a Initial state f Modification of starting time and notification of the change machine machine B C time time b Change of starting time g Modification of stating and notification of the time and report of change modification results machine machine A
102. 000000000000000 00000 00 0 0000 00 000 000000000000000 000 0 000 00000 0000 0 00 0 172 000000000000 00000 000000000 0000000000 00 0 0 000000000000 000000 00000000000000000 000000000000000000000 000 00000 00 0000 0000 0 00 00000000 00 00000 00 00 00 0000000000000 000000 00000000 000000 00000 00 00000000000 00000000 00 0000000000 0 0000000 00 00 00 000000000 00 0000000000 000000000000000000 000 0000000000000000 0000 05 8 22 0 0000 0 1 1 1 1 00 0 8 1 RID DU 1 0 2 1 1 70 0 8 Bjorke O system OO OOOO 0 1 1 Lll A set of connected elements 0 0 1 1 7 00 1 1 1 090 0 1 1 0 000 1 1 22 1
103. 0000000000000000 000000000000000000000000000000000 8 0 172 0000000000000000 000 000000000000000000000 0 000000000 000000 0000000 000000000000 00000 000000000000000000000000000000000 0000 00 000 000000000000 000000 000 0000 0000 00 0000 00000000000000000000 0 00 0000000000000 000 411 00608 0000000000000000000000000000000000 200000 0000000000000000 process planning 000000000000000000 scheduling OO 0 0 00000000000000000000000000000000 00 00000000 0000 0 000 000 0 00000 000 0000 0 0000000000 000 0 000 0000 0 000000000 00 0 00 operations 000000000000000000000000000000000 0000000000000000000000000000000 00 0 2000000 000000000000000000 0 00 000000000000000 0 00000000000000000 000000000000 0000000 000 00000000000000000 000 00 000000000000000 00 00 0 72 4 000 000000000000000 200 18 756 000000 414 0000000000000000000000000 41 000000 311 77 0 000000000000000000000000000000000000 0 0000000000 2888888888688888 28 4 1 OO 59 gt conjunctive arc disjunctive arc machine time a Disjunctive graph representation b Gantt chart representation
104. 1 1 1 1 0 0 0 008088 1 1 7 150 0 04 4 4 00010 3310 0 1 1 7 0 00 00 00 0 738360 D uu E r3 00010 30 00 11 1 0 1 1 1 170 0 180001277608 0 3 0 50 DODOU 0 0 0 0000 0 Z zo z1 0 zi Zn 2 1 817770 2 02 11 00 0
105. 1 1 1 0 1 1 1 88 0 0 1 1 1 1 1 1 10808 10 1 1 1 1 1 1 9 00 0 1 1 1 9 61 4 16 1 160 040 82 4 18 An example of display 0000 1 1 1 gt 08 4169 10000000 4 1 1 1 0 1 1 00 0 1 11 0 0 1 1 8 0 1 1 1 1 0 1 1 0 1 1 1 4 160 0 00000 708 1 1 0 1 11 7060 000 0070 4 170 108 1 0 0 11 1 1 0 1116 300
106. 1 11 3 3 2 2 2 2 2 3 2 4 2 5 2 6 sl 3 1 3 2 3 3 es ATA ATT MMT 08008080888881 412 Er BEL 111141 se dents ta eeni HAE 421 0008 99 1 00 1 PU AE NN ee dan quA 00000 70 521 00000000000 eet Deka e Ss 5 2 2 8 20 37 AA IMA Bon 524 00000000000000 525 0000000000 526 00000000000000 sss ELE ay EE 53 1 00000000000 3 4 3 5 3 6 40 4 2 4 3 4 4 4 5 5 5 1 5 2 8 9 ii ss ED E PES EE ENE NEE RS ta x ie Ss es Ra AAA s ai Sa roc aen o t 0 an ord dd Se aed ert ae ED 5 3 2 5 3 3 54 D 5 53 OOO 56 DI OU 6 11 1 11 08 000 00000 0 11 08870 000000 070 m 00 08080770 0 1 0 00 000000 10 Oooo
107. 7 1091 1999 0 8 8 1969 0 8 1 1983 0 08 8 8 00000 6 000 0 48 10 11 12 13 14 15 020 OOOOG O000 00000000U0000U 00000000000 Vol 25 No 12 pp93 100 1989 1996 1985 0 874 O OOOOOOOOOO TBSOOOOO 985 000 0000 CO 7 No 658 2001 22 16 17 18 19 20 21 23 8 1 3 1 machine tool 1 1 7 8 1 11 1 1 8 0 robot 110 AGV Automated Guided Vehicle
108. 7310 0 1 1 1 70 0008 4 1 1 33800000 380000000 61 11 8 1 1 1 0 45 10 20 30 time min d Skewness 1 10 20 30 time min e Skewness 2 10 20 30 time min f Kurtosis 1 0 0 8 0 6 0 4 0 2 25 20 ao e ao Skewness Skewness 2 Kurtosis 30 30 30 34 70 20 time min a Mean 20 time min 10 b Variance 20 time min 10 c Coefficent of Var D uuu 2 0 1 5 1 0 Mean V 0 5 0 05 0 03 Variance v 0 5 0 4 Coefficent of Var Cutting speed 250m min Depth of cut 2 0 mm Feed rate 0 38 mm rev 3 21 Statistical values of AE signal plotted against cutting time 710 30 46 0 111787 710 0 1 1 0887 D D 1
109. cell B previous operation target operation machine A1 20 0 SS machine A2 machine A3 cell B machine B1 machine B2 machine B3 wasted time time b Depending on previous operation target operation cell A machine A1 machine A2 machine A3 cell B previous jobs which compose previous machine B1 operation machine B2 machine B3 time b Depending on previous jobs processed by machines 5 5 Decision of starting time of operation in hierarchical manufacturing system DOD 5 500 080080 8 00 8 ATP Automatic Tool 0 52 9 59 0 1 0 1 1 1 1 1 5 5 8000000000 0 4 42000
110. ine machine 7 tim lime I Final state c Adjust starting time of operation related and notify it to machine A C Notification of change ui Report of modification result A Operation notified machine time d Report time zero as a result caused by notifi cation N2 machine p time e Report time zero as a result caused by notifi cation N1 4 8 An example of propagation of information according to improved procedure 1 1 0 100 0 1 gt 1 1 gt 0 1 1107 0 1 1 8080730 1 1 1 1 0 D uu 44 OOO 73 0 1 1 1 1 0 887 70 1 10 4 4 OOO 441 010 0 11 0 0 1 310 0 1 8 0 8 0 1 1 1 0 1 111 1 1
111. ine C3 1 1 ES Time a Before rescheduling col A 007 p rescheduled achine A1 m 7 i Machine A2 ET EN 6670 Machine A3 I SEE Cell B L Machine B achine 82 Machine B3 mo leeway Cell C Machine C1 rescheduled 1 Machine C2 1 8 operation EI Machine C3 mur I Time b After rescheduling 5 13 An example of decentralized scheduling in the hierarchical manufacturing system 000 0 1 0 08000000 1 1 1 1 00 100 5 1 70 0 1 111 08808070 0 118878 0 1 11 1 6 130
112. ive propagation method for the hierarchical manufacturing system part 2 0 1 1 0 000 0 0 0 1 1 D 0 70 0 1 1 1 0 E 1 1 mo 5 101 110070 000 OOo E 510000 3000000070 1708 0000000000000000 0 1 0 gt 00 10 0 0 1 010 07 Ey E r3 98 51 80087773807 yes procedure deliver token to 5 done for upper next element no goto step 0 yes
113. mon 8 1 n m Machine 8 2 Bool LI mi mi Machine 9 8 EH um 1 shop 5 zn 8 0888 E 8 8 06 11188 1 1181 68 Machine 10 HN EH mi NH 18 if Machine 8 mE HU E me mou 1 Machine 11 m I m 18 m 1 m 8 Factory 3 Factory 4 Time 5 15 An example of decentralized scheduling in the hierarchical manufacturing system 0 1 70 5 14 1 0 1 510000000000000000000 0 1 1 110 0 0807300 08 0 6 04 0 2 0 0 50 100 150 200 250 300 350 400 450 500 Number of iteration for improvement Normalized utilization factor of system 5 16 Summary of convergence process 5 150 111707308 10 500 740 1 51600 0 1 1 7 D UU D U 1 1 1 4 0 0 01 007 0 73 1 55 1 7710 103 000 1 1 0 0078760 0 11 1 0000 0 55 807730
114. n ALGOL 1211 0 1 1 Send a b a 00000 b 0 Receive ab b 0000000 1 1 770 0 0 090 00 42 0 0 Bi 422 1 8 1 1 1 uU guru 2 O0000000000000 0 0 0 70 0 1 U makespan 1 0 1 1 1 00 00 7711 000 1 4 00 8700 0 11 1 4 70 0 1 1 1 0 70 00000 0000 L1
115. ne time c Final state adjustments of stating time of operations by machines 4 3 Adjustment of schedule among three machines 41 0 1 1 1 1 1 1 0 0 0 0710 4 3 0300 08 201 114 0 0000 4300000000000 1180806 0 0 0 0 AD 48 00000000000000000000000 ADD 100000000000000000000000000 040 02 Ap 100000 0 7 1 0 0770 2000 BOU 0 111 1 1 1 111 0000 0000 00 EA r3 EJ L3 0O00 43000000 AD NM 0 EES ESTE 1
116. nonononoooooonooonooonooDoonDoDoD 8 851 000000000000 CNC 3 22 Apparatus of CNC Lathe 0 3 28 30 48 2 22 _ 81 1 0 0 1 1 1 0 70 1 1 00000 0 000800 3 230000000 1 1 1 1 1 1 0
117. of tool wear Cutting speed 200 m min Depth of cut 2 0 mm Feed rate 0 38 mm rev 1605 30 40 10 0 8 gt 2 D 0 6 5 9 oat 0 0 2 0 10 20 30 0 10 20 30 time min time min a Mean d Skewness 1 0 05 5 0 04 F Ar J 8 5 c 0 amp c E 0 4 H 0 10 20 30 time min time min b Variance e Skewness 2 0 5 IF 25 ki 0 4 r 20 p 0 gt 5 0 3 2 15 L o 2 40 E 00927 x 1 o O 01 F 5 0 10 20 30 0 10 20 30 time min time min c Coefficent of Var f Kurtosis Cutting speed 200 m min Depth of cut 2 0 mm Feed rate 0 38 mm rev 3 17 Statistical values of AE signal plotted against cutting time 1 201 1 1 1 8008800 0730 1 1000300000000 20000000 41 34 770 0 1 1 0 8 1 800 1 000 Usi 1 081 gt 0 0070031 Mean AN TH Variance 2 0 NS i Initial stage Coe of Var K IQ KRY SX middle stage 000 final stage Skewness 2 Kurtosis Input Hidden Output layer layer layer 3 18 Structure of neural network 00 1 0
118. p 108 115 1995 1 1 18806 3161 OU C Vol 58 No 7 pp 270 275 1992 4 Egom 138188 Machine sequencing via disjunctive graphs an implicit enumeration algorithm Operations Research Vol 17 No 6 pp 941 957 1968 10 11 12 13 4 5 85 14 100 8080007830 C Vol 114 No 4 pp 476 482 1994 1 1 0001000000 buc 48 1 16 V pp 331 332 1998 00000 86 88 51 0 3 00000000000000000000000000 000 00000000 0000 000000 9 00000 0 0 0000 00 00000000000000000 00000000000000000 00000 000000000000000 0000 0 000000000000000000000000000000000000 03 56 000000000000000 00 000000000000000 0000 000 0 000000000000000000000 00000000 0000000 000 00 000 000 000000000000 000
119. p1259 1264 1 1 Vol 25 No 3 p58 1981 COO 5 Vol 53 N485 p255 1987 0 6 U ll Vol 54 No 4 p80 8 UU 7 1 1 U Vol 55 No 8 p73 9 1 1 1 8 L No 2 9 9 Vol 7 No 5 1987 10 1 1 Vol 7 No 11 p42 1987 111 10 768 11 O CO Vol 52 No 474 p799 1986 12 2 1 1 UL UU Vol 7 No 11 p49 7 1 1 U Vol 9 No 3 p53 1989 14 K Iwata T Moriwaki An Application of Acoustic Emission Measurement to In Process Sensing of Tool Wear Annals of CIRP 25 1 p21 1977 10 30 Kannatey Asibu E Jr Dornfeld D A Quantitative Relationships for Acoustic Emission from Orthogonal Metal Cutting Trans ASME J Eng ind Vol 103 No 3
120. sonal computer 0O O0 0000000000000 0 1 11808808 80 88 0 0 870 040 100 200 300 400 500 600 700 800 900 1000 1100 1200 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 Machine 2 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 Machine 3 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 Machine 5 a before optimization 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 yo 100 200 300 400 500 600 700 800 900 1000 1100 0 Ki 100 200 300 400 500 600 700 800 900 1000 1100 1200 100 200 300 400 500 600 700 800 900 1000 1100 1200 Machine 5 b after optimization 4 16 Example of scheduling managed by each machine 44 00 81 0 1 1 0777777830 gt Interne 080 POO O O Internet Protocol Address 0 O0 00000000000000 4 17 Apparatus of Platform constructed 1 1 1 1 4 0 1 0 0 1
121. stem Equipped with Force Sensors and Actuators Proceedings of the Fourth International Conference on Automation Technology pp489 494 1996 Yamazaki K Hanaki Y et al Autonomously Proficient CNC Controller for High Perfor mance Machine Tools Based on an Open Architecture Concept Annals of the CIRP Vol 46 275 278 1997 000000 OSP700L LCS15H 00000 O 10 00000000 00000 1996 56 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 3 6 OO 57 31 Moriwaki T and Zhao C Neural Network Approach to Identify Thermal Deformation of Machining Center Proceedings of 8th International IFIP WG5 3 Conference PROLA MAT 92 pp685 697 1992 32 Moriwaki T Intelligent Machine Tool Perspective and Themes for Future Development Manufacturing Science and Engineering Volume 2 ASME Vol 68 2 841 849 1994 S3 0000 00 00000 DOU 7 7 000000000 CD Vol 61 No584 pp1691 16 96 1995 58 1 1 1 01 41 0 3 1 127 31 02 0000000000 00000000000000000000000000000000000 0 0 0 00 00000000000000000000
122. t At 0 goto Step 2 if message REPORT then Receive At At At Ati goto Step 5 Step 2 0000000 0 800 00 0767 0 01 Step LI LI LI D D LU st f then goto Step 3 else goto Step 6 step 3 100000 1 10 Step 40 00000 8 Step 500000000 1 0 4 if nri then goto Step 4 else goto Step 5 66 040 Step 4 000000000 11 U Step 1U UU 00 mp Send NOTICE wy Send f wii goto Step 1 Step 5 Step 311010 1 11 1 U Step 0 1 3 1 2310 1 1 1 81 Step 20000 8 0 0005 5 000000000000000000 0 60000000 if 9 then t R goto Step 2 else goto Step 6 Step 6 0000000000000000 1 7 710
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