JP2021187150A - 機械学習方法、機械学習装置、機械学習プログラム、通信方法、及び混練装置 - Google Patents
機械学習方法、機械学習装置、機械学習プログラム、通信方法、及び混練装置 Download PDFInfo
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Abstract
Description
3 :ケーシング
4 :ロータ
5 :ドア
8 :ウエイト
10 :ピストン
12 :モータ
100 :サーバ
101 :通信部
102 :プロセッサ
110 :報酬計算部
120 :更新部
130 :決定部
140 :学習制御部
200 :通信装置
201 :送信器
202 :受信器
300 :混練装置
310 :通信部
320 :制御器
321 :状態観測部
322 :混練実行部
330 :メモリ
340 :センサ部
350 :入力部
Claims (18)
- 高分子材料を混練して混練物を得るための混練装置の混練条件を機械学習装置が決定する機械学習方法であって、
前記混練装置は、
前記混練物の材料が投入されるチャンバと、
前記チャンバに投入された前記材料を混練する二軸以上のロータと、
前記二軸以上のロータの制御、前記材料の混練時間の制御、及び前記混練装置の動作ステップの制御を司る制御器とを有し、
前記混練物の性能評価に関する少なくとも1つの第1評価パラメータと、少なくとも1つの混練条件とを含む状態変数を取得し、
前記状態変数に基づいて、前記少なくとも1つの混練条件の決定結果に対する報酬を計算し、
前記状態変数から前記少なくとも1つの混練条件を決定するための関数を、前記報酬に基づいて更新し、
前記関数の更新を繰り返すことによって、前記報酬が最も多く得られる混練条件を決定し、
前記少なくとも1つの第1評価パラメータは、前記混練物に関する物性特性及び形状特性の少なくとも1つを含む、
機械学習方法。 - 前記少なくとも1つの混練条件は、
前記材料に関する第1パラメータと、
前記ロータの制御に関する第2パラメータとのうちの少なくとも1つである、
請求項1記載の機械学習方法。 - 前記少なくとも1つの混練条件は、
前記材料に関する第1パラメータと、
前記ロータの制御に関する第2パラメータと、
複数のステップのそれぞれの混練時間、各ステップの材料投入時間、次のステップに進むための条件、トータル混練時間、及び積算電力量のうちの少なくとも1つから選ばれる動作ステップに関する第3パラメータとのうちの少なくとも1つであり、
請求項1記載の機械学習方法。 - 前記混練装置は、さらにウエイトを備え、
前記少なくとも1つの混練条件は、前記ウエイトの動作に関する第4パラメータを含む、
請求項1〜3のいずれかに記載の機械学習方法。 - 前記混練装置は、さらに温度調節機構を備え、
前記少なくとも1つの混練条件は、温度調節に関する第5パラメータを含む、
請求項1〜4のいずれかに記載の機械学習方法。 - 前記第1パラメータは、前記材料の配合量、前記材料の構成要素の投入順序、及び前記チャンバに対する前記材料の充填率の少なくとも1つを含む、
請求項2又は3記載の機械学習方法。 - 前記第2パラメータは、前記二軸以上のロータの回転数、前記二軸以上のロータの位相、及び各ロータの速度比の少なくとも1つを含む、
請求項2又は3記載の機械学習方法。 - 前記第3パラメータにおける前記次のステップに進むための条件は、次のステップに進むための混練時間、前記材料の温度、各ステップにおいて維持するべき前記材料の温度、前記二軸以上のロータを駆動するモータの瞬時電力、前記モータの積算電力、前記モータの瞬時電流、前記モータのトルク、及び前記材料の排出温度の少なくとも1つを含む、
請求項3に記載の機械学習方法。 - 前記第4パラメータは、前記材料を前記チャンバに押し込む際の前記ウエイトの押し付け圧力、前記ウエイトの位置、及び前記ウエイトの速度の少なくとも1つを含む、
請求項4記載の機械学習方法。 - 前記第5パラメータは、前記チャンバに入る循環媒体の温度、前記チャンバから出る循環媒体の温度、前記二軸以上のロータに入る循環媒体の温度、前記二軸以上のロータから出る循環媒体の温度、前記材料を排出するドアに入る循環媒体の温度、及び前記ドアから出る循環媒体の温度の少なくとも1つを含む、
請求項5記載の機械学習方法。 - 前記状態変数は、前記混練装置の動作の安定性に関する第2評価パラメータをさらに含む、
請求項1〜10のいずれかに記載の機械学習方法。 - 前記物性特性は、ムーニ粘度、加硫特性、ペイン効果、添加物の分散、動的粘弾性、硬度、前記混練物の重量、及び前記混練物の温度の少なくとも1つを含む、
請求項1〜11のいずれかに記載の機械学習方法。 - 前記関数は深層強化学習を用いてリアルタイムで更新される、
請求項1〜12のいずれかに記載の機械学習方法。 - 前記報酬の計算では、前記少なくとも1つの第1評価パラメータが各第1評価パラメータに対応する所定の基準値に近づいている場合、前記報酬を増大させ、前記少なくとも1つの第1評価パラメータが各第1評価パラメータに対応する基準値に近づいていない場合、前記報酬を減少させる、
請求項1〜13のいずれかに記載の機械学習方法。 - 高分子材料を混練して混練物を得るための混練装置の混練条件を決定する機械学習装置であって、
前記混練装置は、
前記混練物を得るための材料が投入されるチャンバと、
前記チャンバに投入された前記材料を混練する二軸以上のロータと、
前記二軸以上のロータの制御、前記材料の混練時間の制御、及び前記混練装置の動作ステップの制御を司る制御器とを有し、
前記混練物の性能評価に関する少なくとも1つの第1評価パラメータと、少なくとも1つの混練条件とを含む状態変数を取得する状態取得部と、
前記状態変数に基づいて、前記少なくとも1つの混練条件の決定結果に対する報酬を計算する報酬計算部と、
前記状態変数から前記少なくとも1つの混練条件を決定するための関数を、前記報酬に基づいて更新する更新部と、
前記関数の更新を繰り返すことによって、前記報酬が最も多く得られる混練条件を決定する決定部とを備え、
前記少なくとも1つの第1評価パラメータは、前記混練物に関する物性特性及び形状特性の少なくとも1つを含む、
機械学習装置。 - 高分子材料を混練して混練物を得るための混練装置の混練条件を決定する機械学習装置の機械学習プログラムであって、
前記混練装置は、
前記混練物を得るための材料が投入されるチャンバと、
前記チャンバに投入された前記材料を混練する二軸以上のロータと、
前記二軸以上のロータの制御、前記材料の混練時間の制御、及び前記混練装置の動作ステップの制御を司る制御器とを有し、
前記混練物の性能評価に関する少なくとも1つの第1評価パラメータと、少なくとも1つの混練条件とを含む状態変数を取得する状態取得部と、
前記状態変数に基づいて、前記少なくとも1つの混練条件の決定結果に対する報酬を計算する報酬計算部と、
前記状態変数から前記少なくとも1つの混練条件を決定するための関数を、前記報酬に基づいて更新する更新部と、
前記関数の更新を繰り返すことによって、前記報酬が最も多く得られる混練条件を決定する決定部としてコンピュータを機能させ、
前記少なくとも1つの第1評価パラメータは、前記混練物に関する物性特性及び形状特性の少なくとも1つを含む、
機械学習プログラム。 - 高分子材料を混練して混練物を得る混練装置の混練条件を機械学習する際の通信方法であって、
前記混練物を得るための材料が投入されるチャンバと、
前記チャンバに投入された前記材料を混練する二軸以上のロータと、
前記二軸以上のロータの制御、前記材料の混練時間の制御、及び前記混練装置の動作ステップの制御を司る制御器とを有し、
前記混練物の性能評価に関する少なくとも1つの第1評価パラメータと、少なくとも1つの混練条件とを含む状態変数を観測し、
前記状態変数をネットワーク上に送信し、機械学習済みの混練条件を受信し、
前記少なくとも1つの第1評価パラメータは、前記混練物に関する物性特性及び形状特性の少なくとも1つを含む、
通信方法。 - 高分子材料を混練して混練物を得る混練装置であって、
前記混練物を得るための材料が投入されるチャンバと、
前記チャンバに投入された前記材料を混練する二軸以上のロータと、
前記二軸以上のロータの制御、前記材料の混練時間の制御、及び前記混練装置の動作ステップの制御を司る制御器と、
前記混練物の性能評価に関する少なくとも1つの第1評価パラメータと、少なくとも1つの混練条件とを含む状態変数を取得する状態観測部と、
前記状態変数をネットワーク上に送信し、機械学習済みの混練条件を受信する通信部とを備え、
前記少なくとも1つの第1評価パラメータは、前記混練物に関する物性特性及び形状特性の少なくとも1つを含む、
混練装置。
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