This paper presents a novel machine learning (ML) based approach to predict machining cycle (part program running) times for complex 5-axis machining. Typical 5-axis machining toolpaths consist of short-segmented discrete linear cutter location lines (CL-lines) with simultaneously varying tool center point (TCP) and orientation vectors (ORI). As the 5-axis machine tool NC (numerical control) system tries to interpolate such part programs smoothly, the TCP motion decelerates and accelerates repeatedly causing the actual feedrate to fluctuate. The actual observed (resultant) feedrate can be approximately 30 % lower than the user commanded one. Furthermore, if the toolpath requires the 5-axis machine tool to travel closer to its singular point, or if the workpiece placement on the table is not optimal, actual feedrate is even lowered further. This paper presents two ML-based approaches to accurately predict 5-axis machining toolpaths. The first strategy uses a bidirectional long short-term memory (Bi-LSTM) network to model the machine tool behavior and generates a direct final cycle time estimation for any given part program. This approach only uses the toolpath geometry to predict the cycle time, and for training it only needs the “final” cycle times of similar part programs making it practical on today’s shop floors. The second strategy requires individual CL-line processing times for training. In return, it can provide highly accurate cycle time predictions. Both strategies can capture the interpolation dynamics of 5-axis machine tool NC systems accurately. Simulation studies and experimental validations are conducted on modern 5-axis machine tools with varying workpiece placements and interpolation parameters. Proposed approaches have shown to predict cycle times with 90–95 % accuracy on real-life complex 5-axis machining toolpaths.

中文翻译：

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使用 Bi-LSTM 通过深度神经网络准确预测五轴加工周期时间

本文提出了一种基于机器学习 (ML) 的新颖方法来预测复杂 5 轴加工的加工周期（零件程序运行）时间。典型的 5 轴加工刀具路径由短段离散线性刀具位置线 (CL 线) 和同时变化的刀具中心点 (TCP) 和方向矢量 (ORI) 组成。由于五轴机床NC（数控）系统试图平滑地插补此类零件程序，TCP运动反复减速和加速，导致实际进给速度波动。实际观察到的（合成的）进给率可能比用户命令的进给率低大约 30%。此外，如果刀具路径要求 5 轴机床行进更接近其奇点，或者如果工件在工作台上的放置不是最佳的，则实际进给率甚至会进一步降低。本文提出了两种基于机器学习的方法来准确预测 5 轴加工刀具路径。第一种策略使用双向长短期记忆 (Bi-LSTM) 网络对机床行为进行建模，并为任何给定的零件程序生成直接的最终周期时间估计。这种方法仅使用刀具路径几何形状来预测循环时间，并且为了进行训练，它只需要类似零件程序的“最终”循环时间，使其在当今的车间中实用。第二种策略需要单独的 CL 线处理时间来进行训练。作为回报，它可以提供高度准确的周期时间预测。两种策略都能准确捕捉五轴机床数控系统的插补动态。在具有不同工件放置和插补参数的现代 5 轴机床上进行仿真研究和实验验证。所提出的方法已证明可以在现实复杂的 5 轴加工刀具路径上以 90-95% 的精度预测循环时间。