JP7507601B2 - 工作機械のびびり振動を予測するための方法 - Google Patents
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Description
・Budak E, Altintas Y (1998) Analytical Prediction of Chatter Stability in Milling-Part I: General Formulation. Journal of Dynamic Systems, Measurement, and Control, 120(1): p.22-30,
・Insperger T, Stepan G (2002) Semi-discretization method for delayed systems. International Journal for Numerical Methods in Engineering, 55(5): p.503-518,
・Ye Ding, LiMin Zhu, XiaoJian Zhang, Han Ding, A full-discretization method for prediction of milling stability, International Journal of Machine Tools and Manufacture, Volume 50, Issue 5, 2010, Pages 502-509,
・J. Munoa, X. Beudaert, Z. Dombovari, et al., Chatter suppression techniques in metal cuttung, CIRP Ann. Manuf. Technol. 65(2)(2016) 785-808
がある。
(1)固有振動数fnおよび減衰比ξnは、スピンドル速度nおよびスピンドル軸受温度Tに依存する;
(2)TCPダイナミクスは、X方向およびY方向において等しい;
(3)切削力係数KtcおよびKrcは、公称送り速度ftおよびワークピース材料WPに依存する、
が行われる。
fnx=fny=fn=f(n,T),ξnx=ξny=ξn=f(n,T)
Ktc=f(ft,WP),Krc=f(ft,WP)
式中、Ktc1,Ktc2はそれぞれワークピース1およびワークピース2の接線方向の切削力係数、Krc1,Krc2はそれぞれワークピース1およびワークピース2の半径方向の切削力係数であり、fnは固有振動数、ξnは減衰比である。50回の異なる切削の安定性がシミュレーションで評価される。シミュレーションごとに、スピンドル速度、軸受温度、入口角度および出口角度、刃当たりの送り量、ならびに切削の方向およびワークピース種類が、定義された範囲内でランダムにサンプリングされる。各シミュレーションサンプルの切削深さは、理論的な安定性限界を基準として、asim=(1+x)・atheoreticalによって選択され、式中、xは、-0.3~0.3の範囲から一様に抽出された乱数である。このアプローチは、切削が理論上の境界を上方超過および下方超過して実行されるのと同じ確率で、理論上の安定性限界から最大30%の偏差まで実行されるという着想に相当する。安定性限界から外れる切削が逆同定に含まれうることは、本発明の重要な利点である。
10 工作機械
11 工作機械
12 工作機械
20 びびり振動予測ユニット
21 中央データベース
Claims (11)
- 工作機械のびびり振動を予測するための方法であって、
複数の重みを有する人工ニューラルネットワークに、工作機械でワークピースを加工するために適用される、オペレータが設定した機械加工パラメータまたは測定された機械加工パラメータである、第1の既知の機械加工パラメータを含む第1の入力データを供給することと、
第1の出力データに影響を及ぼす既知のパラメータである前記第1の入力データおよび前記複数の重みに基づいて、人工ニューラルネットワークの出力における、安定性モデルの入力データとして要求される不確実なパラメータである前記第1の出力データを求めることと、
前記第1の出力データ、および、前記安定性モデルの入力データとして要求される第2の既知の機械加工パラメータである第2の入力データを、工具とワークピースとの間の振動周波数を定義する、びびり振動の発生および対応するびびり振動周波数を予測するための前記安定性モデルに提供して、前記びびり振動の発生および前記対応するびびり振動周波数を含む予測データを生成することと、
前記予測データと実験データとを、比較モジュールで比較して、前記人工ニューラルネットワークの前記複数の重みを調整することと、
を含む、方法。 - 前記人工ニューラルネットワークが、進化的アルゴリズムによってトレーニングされる、請求項1記載の方法。
- 前記人工ニューラルネットワークが、遺伝的アルゴリズムによってトレーニングされる、請求項2記載の方法。
- 前記方法はさらに、前記工作機械がワークピースを機械加工するとき、少なくとも1つの工作機械から収集されたデータを取得することを含み、特に、前記収集されたデータは、実験データ、オペレータが設定した機械加工パラメータ、および機械加工中に測定された機械加工パラメータを含む、請求項1から3までのいずれか1項記載の方法。
- 前記第1の入力データおよび/または前記第2の入力データの一部が、前記収集されたデータから導出される、請求項4記載の方法。
- 前記収集されたデータが、トレーニングセットと検証セットとに分割され、前記進化的アルゴリズムが、前記トレーニングセットを用いて前記人工ニューラルネットワークをトレーニングし、前記検証セットを用いて前記トレーニングされた人工ニューラルネットワークの精度を検証する、請求項2を引用する請求項4または5記載の方法。
- 少なくとも1つの第2の人工ニューラルネットワークが適用され、前記第2の人工ニューラルネットワークの前記出力データが、前記安定性モデルに供給される、請求項1から6までのいずれか1項記載の方法。
- 請求項1から7までのいずれか1項記載の方法を実行するように構成されたびびり振動予測ユニットであって、ニューラルネットワークモジュール、安定性モデルモジュールおよび比較モジュールを含む、びびり振動予測ユニット。
- 前記びびり振動予測ユニットが、安定性マップを確立するようにさらに構成されている、請求項8記載のびびり振動予測ユニット。
- a.前記実験データを取得するように構成されたセンシングユニットと、
b.請求項8記載の前記びびり振動予測ユニットに接続された中央データベースに、前記実験データを含む収集されたデータを送信するように構成された通信ユニットと、
c.前記びびり振動予測ユニットによって生成された安定性マップを使用して、ワークピースを機械加工するための機械加工パラメータを求めることと、
を含む、工作機械。 - 複数の請求項10記載の工作機械と、請求項8記載のびびり振動予測ユニットと、を含むシステム。
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JP2008137102A (ja) | 2006-11-30 | 2008-06-19 | Matsushita Electric Works Ltd | 工作機械総合監視装置 |
JP2015168057A (ja) | 2014-03-05 | 2015-09-28 | ミクロン アジー シャルミル アクチエンゲゼルシャフトMikron Agie Charmilles AG | びびり予測のための改良型データベース |
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