This paper suggests a novel robust formulation designed for optimizing the parameters of the turning process in an uncertain environment for the first time. The aim is to achieve the lowest energy consumption and highest precision. With this aim, the current paper considers uncertain parameters, objective functions, and constraints in the offered mathematical model. We proposed several uncertain models and validated the results in real-world case studies. In addition, several artificial intelligence-based solution techniques are designed to solve the complex nonlinear problem. We determined the most efficient solution approach by solving various test problems. Then, simulated several scenarios to demonstrate the robustness of our results. The results showed that the solutions provided by the offered model significantly reduce energy consumption in different setups. To ensure the reliability of the results, we carried out worst-case sensitivity analyses and found the most critical parameters. The results of the worst-case analyses indicated that the offered robust model is efficient and saves a significant amount of energy comparing to traditional models. It is shown that the provided solution by the presented robust formulation is reliable in all situations and results in the lowest energy and the best machining precision.

本文首次提出了一种新颖的鲁棒配方，旨在优化不确定环境中的车削工艺参数。目的是实现最低的能耗和最高的精度。出于这个目的，当前论文考虑了所提供数学模型中的不确定参数，目标函数和约束。我们提出了几种不确定的模型，并在实际案例研究中验证了结果。此外，还设计了几种基于人工智能的解决方案技术来解决复杂的非线性问题。通过解决各种测试问题，我们确定了最有效的解决方案。然后，模拟了几种情况以证明我们结果的可靠性。结果表明，所提供模型提供的解决方案显着降低了不同设置中的能耗。为了确保结果的可靠性，我们进行了最坏情况下的灵敏度分析，并找到了最关键的参数。最坏情况分析的结果表明，与传统模型相比，所提供的鲁棒模型是有效的，并且可以节省大量能源。结果表明，所提出的稳健配方所提供的解决方案在所有情况下都是可靠的，并且可实现最低的能耗和最佳的加工精度。最坏情况分析的结果表明，与传统模型相比，所提供的鲁棒模型是有效的，并且可以节省大量能源。结果表明，所提出的稳健配方所提供的解决方案在所有情况下都是可靠的，并且可实现最低的能耗和最佳的加工精度。最坏情况分析的结果表明，与传统模型相比，所提供的鲁棒模型是有效的，并且可以节省大量能源。结果表明，所提出的稳健配方所提供的解决方案在所有情况下都是可靠的，并且可实现最低的能耗和最佳的加工精度。