SYSTEM UNDERSTANDING OF HIGH PRESSURE DIE CASTING PROCESS AND DATA WITH MACHINE LEARNING APPLICATIONS

1. 概要:

  • タイトル: SYSTEM UNDERSTANDING OF HIGH PRESSURE DIE CASTING PROCESS AND DATA WITH MACHINE LEARNING APPLICATIONS (高圧ダイカストプロセスとデータ、機械学習応用に関するシステム理解)
  • 著者: David J. Blondheim, Jr. (デイビッド J. ブロンデハイム・ジュニア)
  • 出版年: 2021年秋
  • 掲載誌/学会: コロラド州立大学 (博士論文)
  • キーワード: ダイカスト (Die casting)、機械学習 (Machine learning)、システムエンジニアリング (Systems engineering)、インダストリー4.0 (Industry 4.0)、データフレームワーク (Data framework)、非教師あり機械学習 (Unsupervised machine learning)、異常検知 (Anomaly detection)、プロセス制御 (Process control)

2. 研究背景:

  • 研究トピックの社会的/学術的背景: ダイカストは、ニアネットシェイプ鋳造品を製造するために広く使用されている非常に複雑な製造システムです。長い歴史にもかかわらず、プロセスを定義し、各サイクルで生成されるデータを活用するためのシステムエンジニアリングのアプローチは不足しています。既存の研究は、ダイカスト内の重要なパラメータの狭い範囲に焦点を当てる傾向があります。
  • 既存研究の限界: ダイカスト研究における限定的なデータパラメータへの狭い焦点は、生産現場での機械学習の成功と適用可能性を制限してきました。プロセス最適化に関する文献では、実験計画入力と範囲が不適切に選択されていることが多く、品質予測のための実際のダイカストアプリケーションの複雑さが欠けています。
  • 研究の必要性: ダイカストプロセスとデータを包括的に理解するためのシステムエンジニアリングの視点が必要です。大量の生成データを管理し、ダイカスト産業におけるプロセス制御と品質を改善するために、機械学習の有意義な応用を特定するためのデータフレームワークが必要です。

3. 研究目的と研究課題:

  • 研究目的: システムエンジニアリングの観点からダイカストプロセスを調査し、機械学習を応用して、ダイカスト産業におけるシステム理解、プロセス制御、データ活用を強化する有意義な方法を示すこと。
  • 主要な研究課題:
    • システムエンジニアリングのアプローチは、ダイカストにおける重要なプロセスとデータフレームワークをどのように定義できるか？
    • 機械学習は、プロセス制御と品質を改善するために、複雑なダイカストシステムにどのように有意義に適用できるか？
    • 非教師あり機械学習は、ダイカストにおいてデータを自動的に監視し、異常を特定することで価値を提供できるか？
  • 研究仮説: ダイカストのシステムエンジニアリングフレームワーク内で適用される非教師あり機械学習は、データを自動的に監視し、異常を特定することで価値を提供でき、これによりプロセス制御の改善とダイカストプロセスによって生成されたデータのより良い活用につながるでしょう。

4. 研究方法

  • 研究デザイン: 文献レビュー、システムエンジニアリング分析、事例研究、実験研究を含む博士論文研究。
  • データ収集方法: 装置設定、射出およびその他のシステムの時系列データ、熱画像、鋳造品質データなど、生産ダイカストプロセスから収集されたデータ。
  • 分析方法: ダイカストシステムとデータフレームワークを定義するためのシステムエンジニアリングアプローチ。実験データに対する統計分析（ウィルコクソン符号順位検定）。事例研究のためのk-平均法クラスタリングやオートエンコーダなどの機械学習アルゴリズム。
  • 研究対象と範囲: 高圧ダイカストプロセス、ダイカスト作業から生成されたデータ、マーキュリー・マリンで実施された事例研究。

5. 主な研究成果:

  • 主要な研究成果:
    • ダイカストプロセスは、ネットワーク構造、適応性、自己組織化、非線形特性を備えた複雑なシステムとして定義されています。
    • ダイカスト用の包括的なデータフレームワークが開発され、データは設計パラメータデータ、入力設定データ、出力 - 離散データ、出力 - 時系列データ、サイクル時間分析データに分類されました。
    • ダイカストにおけるデータ量は、現在産業で使用されているものよりも数桁大きいです。
    • 非教師あり機械学習、特にオートエンコーダを使用した異常検知は、ダイカストプロセスを監視し、異常を特定する上で価値を提供します。
    • 事例研究は、プロセス監視、熱画像および時系列データの異常検知、およびプロセス最適化のための非教師あり機械学習の応用を示しています。
    • 多孔性形成の確率的性質が確認され、ダイカストにおける品質予測のための従来の教師あり機械学習アプローチの限界が強調されました。
  • 統計的/質的分析結果: ウィルコクソン符号順位検定では、最高サンプルと最悪サンプル間の重要な射出パラメータに統計的に有意な差は見られず、多孔性の確率的性質を示唆しています。事例研究は、異常検知とプロセス理解における非教師あり機械学習の有効性を示しました。
  • データ解釈: ダイカストの複雑さと欠陥形成の確率的性質により、品質予測のための従来の教師あり機械学習から、プロセス監視と異常検知のための非教師あり手法への焦点の移行が必要です。システムエンジニアリングのアプローチと包括的なデータ活用は、ダイカストにおける機械学習の効果的な応用に不可欠です。
  • 図のリスト:
    • 図 1: ダイカストセルレイアウトの例
    • 図 2: V8エンジンブロックダイカストの例
    • 図 3: 金属供給: ドージングファーネス (左)、2軸レードル (中央)、7軸ロボットレードル (右)
    • 図 4: ダイとチャンバーの図
    • 図 5: 射出速度が遅すぎると空気を巻き込む波
    • 図 6: 射出速度が速すぎると乱流金属波が形成される
    • 図 7: 正しい波形成により、チャンバーからすべての空気が逃げられる
    • 図 8: 射出速度と圧力グラフの例
    • 図 9: 油圧シリンダーとスライドを備えたダイ (左) および (右) 位置
    • 図 10: イジェクターピンを備えたダイの例
    • 図 11: 生産中の温水ユニット (左) とジェット冷却ユニット (右)
    • 図 12: スプレーシステム: 2軸マニホールド (左) と 6軸ロボット (右)
    • 図 13: ダイ内の機能の主要な用語
    • 図 14: 鋳造品のビスケット、ランナー、ゲート、ベントシステム
    • 図 15: 射出中のシミュレーションされた金属流体の流れ
    • 図 16: ダイ設計における複雑な熱冷却ライン
    • 図 17: 大型ダイの可動半分にある温水およびジェット冷却ラインの例
    • 図 18: ダイカストの複雑さ
    • 図 19: [60] に基づくシステムエンジニアリング「V」ダイアグラム
    • 図 20: 階層システムの分解
    • 図 21: ダイカストコンテキストダイアグラム
    • 図 22: ツーリング管理システムダイアグラム
    • 図 23: 一般的なクローズドループフィードバックシステム
    • 図 24: ショットプロファイル用にプログラムされた設定点の例
    • 図 25: ショットプロファイル出力の例
    • 図 26: 生産ダイカスト環境における熱画像カメラ
    • 図 27: 固定半分 (左) と可動半分 (右) の熱画像例
    • 図 28: 複数のロボットとダイカスト後処理を備えた抽出セル例
    • 図 29: X線用鋸引きサンプル鋳造品
    • 図 30: X線等級 1～3 の例
    • 図 31: 9 つの最良サンプル (グレード 1)
    • 図 32: 9 つの最悪サンプル (グレード 3)
    • 図 33: 予測された多孔質ゾーンのシミュレーション結果
    • 図 34: MAGMA によって予測されたサンプル領域の液体体積
    • 図 35: データ空間の重複の例
    • 図 36: HPDC 多孔質の例
    • 図 37: 多孔質のサンプル X 線画像
    • 図 38: バイナリ許容仕様による分類問題の例
    • 図 39: シミュレーションされた予測多孔質ゾーンの例
    • 図 40: 確率的多孔質形成を示す連続鋳造品
    • 図 41: 確率的欠陥形成の例による分類問題
    • 図 42: 二次プロセス変動の例による分類問題
    • 図 43: 要素の組み合わせの例
    • 図 44: バイアス-バリアンストレードオフグラフ
    • 図 45: カウントに基づく従来の混同行列
    • 図 46: パーセンテージに基づく正規化された混同行列
    • 図 47: 従来の混同行列と正規化された混同行列の計算例
    • 図 48: さまざまな精度計算の例
    • 図 49: 臨界誤差閾値を使用したバイアス-バリアンストレードオフグラフ
    • 図 50: 固有誤差の増加
    • 図 51: バイアスの増加
    • 図 52: 人工知能、機械学習、深層学習の階層
    • 図 53: 2 クラス混同行列の例
    • 図 54: ダイカストにおける機械学習の 6 つの課題
    • 図 55: ダイカストの 2D レーザーバーコードの例
    • 図 56: 視覚的に描かれたヒューズ現象
    • 図 57: 機械学習実装のための一般的な IT アーキテクチャ
    • 図 58: スクラップ率の削減は、精度向上と低い CET 目標の必要性を促進します
    • 図 59: 機械加工後に発見された多孔質欠陥
    • 図 60: ゲートブレークイン欠陥
    • 図 61: ダイカストマシンに設置された熱画像システム
    • 図 62: データ変換を伴う熱画像例
    • 図 63: 2D バーコードとシリアル番号の例
    • 図 64: 実験実行中の増圧データ
    • 図 65: 線形回帰モデルの例
    • 図 66: 過剰適合モデルの例
    • 図 67: ニューラルネットワーク構造の例
    • 図 68: 2 次元における SVM 分類の例
    • 図 69: クラスタ数を決定するためのエルボー法
    • 図 70: 7 つのクラスタによるクラスタ割り当て
    • 図 71: 平均低速ショット速度と増圧の箱ひげ図と個々のプロット
    • 図 72: クラスタ別の主要パラメータの行列散布図
    • 図 73: 結果別の主要パラメータの行列散布図 (良対不良)
    • 図 74: 強度圧力対平均低速ショット速度の散布図
    • 図 75: 履歴スクラップデータとクラスタを比較した平均低速ショット速度と増圧の箱ひげ図と個々のプロット
    • 図 76: 異常検出のための 3 段階アルゴリズム
    • 図 77: 平均および標準偏差行列を使用した定常状態イメージスタックの視覚化
    • 図 78: 色レイヤーごとのさまざまな異常識別のための数式コード
    • 図 79: ダイカストマシンに設置された熱画像カメラシステム [45]
    • 図 80: 熱画像温度行列とグレースケール画像
    • 図 81: プレクロップ対ポストクロップ熱画像 (固定半分)
    • 図 82: スケールされたピクセル熱画像の比較 (可動半分)
    • 図 83: クラスタサイズごとの F スコア
    • 図 84: クラスタ割り当てのシーケンス
    • 図 85: 時間ベースのクラスタ割り当てのシーケンス
    • 図 86: クラスタ #1 の平均および標準偏差画像
    • 図 87: クラスタ #1 対 クラスタ #3 の平均画像のヒストグラム
    • 図 88: ウォームアップショットの異常検出
    • 図 89: 水がオフになったダイの異常検出
    • 図 90: 2 つのプロセス設定の平均および標準偏差画像
    • 図 91: 低速および高速ショット速度の一般的な範囲点設定
    • 図 92: 平均が同じ 4 つの例のプロファイル
    • 図 93: 標準コサイン類似度を使用した異常検出
    • 図 94: 修正コサイン類似度を使用した異常検出
    • 図 95: コサイン類似度計算値の分布
    • 図 96: 複数のマシンが実行されている Azure Web ページのホーム画面
    • 図 97: 異常ショットが検出された詳細ショット画面
    • 図 98: ショットプロファイルの設定点
    • 図 99: ランプ状の低速ショット速度プロファイルで設定点を変更する
    • 図 100: 同一の低速ショット速度平均を持つさまざまなプロファイルの例
    • 図 101: 単純なニューラルネットワーク
    • 図 102: 深層学習の例
    • 図 103: オートエンコーダコンポーネント
    • 図 104: MNIST 手書き数字の例
    • 図 105: プロットラスターの例
    • 図 106: データ準備
    • 図 107: 例ごとの数字予測
    • 図 108: トレーニングデータセットからの 5 秒の例
    • 図 109: 5 秒のみでトレーニングされたオートエンコーダに基づく平均絶対誤差 (MAE)
    • 図 110: テストデータの視覚的およびエラーの例
    • 図 111: オートエンコーダによって異常として識別された 5 秒
    • 図 112: スプレー流量プロファイル例
    • 図 113: 生 SQL データストレージの例
    • 図 114: トレーニングデータクラスタ
    • 図 115: 元の入力と再作成されたプロファイル
    • 図 116: テストサンプル
Figure 2: Example V8 engine block die casting (photo permission from Mercury Marine)
Figure 2: Example V8 engine block die casting (photo permission from Mercury Marine)
Figure 14: Biscuity, runner, gating, and venting system on casting (photo permission from Mercury Marine)
Figure 14: Biscuity, runner, gating, and venting system on casting (photo permission from Mercury Marine)

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List of Abbreviations

  • AFS – American Foundry Society
  • AI – Artificial Intelligence
  • BoB – Best of Best
  • CET – Critical Error Threshold
  • CT Scanning – Computed Tomography Scanning
  • HPDC – High Pressure Die Casting
  • IIoT – Industrial Internet of Things
  • IT – Information Technology
  • INCOSE – International Council on Systems Engineering
  • ML – Machine Learning
  • MQTT – Message Queuing Telemetry Transport
  • NADCA – North American Die Casting Association
  • NN – Neural Network
  • OPC UA – Open Platform Communications Unified Architecture
  • OT – Operational Technology
  • PLC – Programmable Logic Controller
  • PSI – Pounds per Square Inch
  • RPM – Revolutions per Minute
  • SE – Systems Engineering
  • SoS – Systems-of-Systems
  • SVM – Support Vector Machine
  • WoW – Worst of worst

Copyright:

  • This material is David J. Blondheim, Jr.'s paper: Based on SYSTEM UNDERSTANDING OF HIGH PRESSURE DIE CASTING PROCESS AND DATA WITH MACHINE LEARNING APPLICATIONS.
  • 본 자료는 위 논문을 바탕으로 요약되었으며, 상업적 목적으로 무단 사용하는 것을 금지합니다.
  • Copyright © 2025 CASTMAN. All rights reserved.
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