Given the complex nature of their phenomena and interactions, industrial processes often have multiple variables of interest, usually grouped into critical-to-quality and critical-to-performance characteristics. These variables often have significant correlations, which make engineering problems multivariate. For this reason, Response Surface Methodology, coupled with multivariate techniques, has been widely used as a logical roadmap for modeling and optimization of the characteristics of interest. However, the variability and prediction capability of the numerical solutions obtained are almost always neglected, reducing the likelihood that numerical results are indeed compatible with observable process improvements. To fill this gap, this paper proposes a nonlinear multiobjective optimization strategy based on multivariate prediction capability ratios. For this, rotated Factor Analysis is used as the multivariate technique for grouping process characteristics and composing capability ratios, so that the prediction variance is taken as the natural variability of the process modeled and the expected value distances to the nadir solutions of the latent variables are taken as the allowed variability. Normal Boundary Intersection method, combined with Generalized Reduced Gradient algorithm, is used as the numerical scheme to maximize the prediction capability of Pareto optimal solutions. To illustrate the feasibility of the proposed strategy, we present a case study of end milling without cutting fluids of duplex stainless steel UNS S32205. Rotatable Central Composite Design, with three cutting parameters, was employed for data collection. Traditional multivariate and proposed approaches were compared. The results demonstrate that the proposed optimization strategy is able to provide solutions with satisfactory prediction capability for all variables analyzed, regardless of their convexities, optimization directions, and correlation structure. In addition, while critical-to-quality characteristics are more difficult to control, they have been favored by the proposed optimization regarding prediction capability, which was a desirable result.

考虑到它们现象和相互作用的复杂性，工业过程通常具有多个感兴趣的变量，通常分为对质量至关重要和对性能至关重要的特征。这些变量通常具有显着的相关性，这使工程问题成为多变量。因此，响应面方法论与多元技术相结合已被广泛用作对感兴趣的特征进行建模和优化的逻辑路线图。但是，几乎总是忽略了所获得的数值解的可变性和预测能力，从而降低了数值结果确实与可观察到的过程改进兼容的可能性。为了填补这一空白，提出了一种基于多元预测能力比的非线性多目标优化策略。为此，旋转因子分析被用作对过程特征和组成能力比进行分组的多元技术，从而将预测方差作为建模过程的自然可变性，并且到潜在变量的最低解的期望值距离为视为允许的可变性。数值边界采用法向边界相交的方法，结合广义缩减梯度算法，可以最大化帕累托最优解的预测能力。为了说明所提出策略的可行性，我们提出了一种在不切削双相不锈钢UNS S32205切削液的情况下进行立铣的案例研究。可旋转的中央复合设计 具有三个切割参数，用于数据收集。比较了传统的多元方法和提议的方法。结果表明，所提出的优化策略能够为所有分析变量提供具有令人满意的预测能力的解决方案，而无论其凸度，优化方向和相关结构如何。另外，尽管关键质量特性更难控制，但建议的关于预测能力的优化已使它们受到青睐，这是理想的结果。不论它们的凸度，优化方向和相关结构如何。另外，尽管关键质量特性更难控制，但建议的关于预测能力的优化已使它们受到青睐，这是理想的结果。不论它们的凸度，优化方向和相关结构如何。此外，尽管关键质量特性更难控制，但建议的关于预测能力的优化已使它们受到青睐，这是理想的结果。