Early cost estimation of machined parts is difficult as it requires detailed process information that is not usually available during product design. Parametric methods address this issue by estimating machining time from predictors related to design choices. One of them is complexity, defined as a function of dimensions and tolerances from an analogy with information theory. However, complexity has only a limited correlation with machining time unless restrictive assumptions are made on part types and machining processes. The objective of the paper is to improve the estimation of machining time by combining complexity with additional parameters. For this purpose, it is first shown that three factors that influence machining time (part size, area of machined features, work material) are not fully captured by complexity alone. Then an optimal set of predictors is selected by regression analysis of time estimates made on sample parts using an existing feature-based method. The proposed parametric model is shown to predict machining time with an average percentage error of 25% compared to the baseline method, over a wide range of part geometries and machining processes. Therefore, the model is accurate enough to support comparison of design alternatives as well as bidding and make-or-buy decisions.

机械零件的早期成本估算很困难，因为它需要详细的过程信息，而这些信息通常在产品设计期间不可用。参数化方法通过根据与设计选择有关的预测变量估计加工时间来解决此问题。其中之一是复杂性，它是根据与信息论的类比定义为尺寸和公差的函数。但是，除非对零件类型和加工过程进行了限制性假设，否则复杂度与加工时间的关系有限。本文的目的是通过将复杂度与其他参数结合起来，改善对加工时间的估计。为此，首先显示出影响加工时间的三个因素（零件尺寸，加工特征的面积，工件材料）并不能完全通过复杂性来捕获。然后，使用现有的基于特征的方法，通过对样本零件上进行的时间估计进行回归分析，选择最佳的预测变量集。与基线方法相比，所提出的参数模型显示出可预测的加工时间，与基线方法相比，在各种零件几何形状和加工过程中的平均百分比误差为25％。因此，该模型足够准确，可以支持比较设计方案以及投标和购买或购买决策。