| Project/Area Number |
17K14570
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Production engineering/Processing studies
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| Research Institution | Gifu University |
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Principal Investigator |
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| Project Period (FY) |
2017-04-01 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2019: ¥520,000 (Direct Cost: ¥400,000、Indirect Cost: ¥120,000)
Fiscal Year 2018: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2017: ¥3,250,000 (Direct Cost: ¥2,500,000、Indirect Cost: ¥750,000)
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| Keywords | CFRP / 研削加工 / 電着エンドミル / 研削温度 / 表面粗さ / 加工条件設計 / cBN工具 / オシレーション研削 / エンドミル / トリミング / オシレーション / バリ / エンドミル加工 / モニタリング / 難削材料 / 温度測定 / 切削加工 / 加工モニタリング |
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| Outline of Final Research Achievements |
In order to carry out the trimming for carbon fiber reinforced plastics (CFRP) with high efficiency and high accuracy, cBN electroplated end-mill that can do rough-cutting and finish grinding was developed. To develop this tool, the influences of cutting teeth shapes and abrasive diameter on machining results and characteristics were evaluated, and the removal mechanism and characteristics were unraveled. Moreover, the optimum machining condition was investigated and the estimation model for machined sectional shape quality, CFRP temperature and surface roughness were established. the estimation model for surface roughness when applying the oscillation grinding was also established, to futher improve the surface roughness. The developed tool is also possible to precise machining of carbon fiber reinforced thermo plastics that has been required in the automobile field.
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| Academic Significance and Societal Importance of the Research Achievements |
炭素繊維強化プラスチック(CFRP)の使用量は年々増加しており，CFRPの高能率・高精密加工の要求が高まっている．開発したcBN電着エンドミルは当該工具のみで従来工具から加工能率を落とすことなく，CFRPを加工するものであり，工具交換による非加工時間や工具コストの低下に寄与できる．一般的に研削加工は加工温度が上がりやすく，CFRP樹脂が変質しやすいが，当該工具はエンドミル形状としたことで，加工中のCFRP温度の上昇を抑制することができた．加工条件の決定は作業者の経験や感覚に依存しやすいが，当該工具を用いた際の表面粗さ予測式を構築したことで，所望する表面粗さを創成できる加工条件決定を容易にした．
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