| Project/Area Number |
05650113
|
|---|
| Research Category |
Grant-in-Aid for General Scientific Research (C)
|
| Allocation Type | Single-year Grants |
|---|
| Research Field |
機械工作・生産工学
|
|---|
| Research Institution | Tokyo Institute of Technology |
|---|
Principal Investigator |
OBIKAWA Toshiyuki Tokyo Inst.of Tech.Engineering Associate Professor, 工学部, 助教授 (70134830)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
SASAHARA Hiroyuki Tokyo Inst.of Tech.Engineering Research Associate, 工学部, 助手 (00205882)
SHIRAKASHI Takahiro Tokyo Inst.of Tech.Engineering Professor, 工学部, 教授 (50016440)
|
|---|
| Project Period (FY) |
1993 – 1994
|
| Project Status |
Completed (Fiscal Year 1994)
|
| Budget Amount *help |
¥2,200,000 (Direct Cost: ¥2,200,000)
Fiscal Year 1994: ¥400,000 (Direct Cost: ¥400,000)
Fiscal Year 1993: ¥1,800,000 (Direct Cost: ¥1,800,000)
|
|---|
| Keywords | Cutting / Process Planning / Expert / Know-how / Visualization / Cutting model / Cutting temperature / Tool wear / 熟練技能者 / 切削 / ノウハウ / 切削温度 |
|---|
| Research Abstract |
Since expert skills play important roles in metal cutting, it is necessary to use the knowledge of experts to establish highly automated machining systems. Expert systems based on the quantitative knowledge, however, often does not work well because there are many conflicting knowledge of machining, which would not be resolved easily.
In this study cutting forces and cutting temperature are assumed as the solutions of welldefined problems and their visualization are used in order to clarify the know-how of experts and determine cutting conditions rationally.
The visualization systems are constructed in a EWS and personal computers. This would enable many tool engineers and workers to experience how the cutting temperature changes with cutting conditions. Tool wear is further predicted using cutting temperature and cutting forces and a wear characteristic equation, which is revised through a newly proposed concept of adaptive prediction.
Machining accuracy, surface finish and grooving wear, which are not able to be predicted analytically, are obtained through neural networks in this study. Data obtained by the simulations are used as reliable input of neural networks.
A case study for finding the optimum cutting conditions under the constraints of maximum flank wear and the maximum surface finish are examined.
|
