The presence of defects in industrial manufacturing may compromise the final quality and cost of a product. Among all possible defect causes, human errors have significant effects on the performances of assembly systems. Much research has been conducted in recent years focusing on the problem of defect generation in assembly processes, considering the close connection between assembly complexity and human errors. It was observed that the relationship between the average number of defects introduced during each assembly phase and the related assembly complexity follows a power-law relationship. Accordingly, many authors proposed a data logarithmic transformation in order to linearize the mathematical model. However, as has already been discussed in literature, when the model is retransformed in the original form a significant bias may occur, leading to completely wrong predictions. In this paper, the bias due to the logarithmic transformation of models for predicting defects in assembly is analyzed and discussed. Two alternative methods are proposed and compared to overcome this drawback: the use of a bias correction factor to the retransformed fitted values and a power-law nonlinear regression model. The latter has proved to be the best approach to predict defects with few non-repeated data and affected by high variability, such as in the case under study.

工业制造中缺陷的存在可能会损害产品的最终质量和成本。在所有可能的缺陷原因中，人为错误会对装配系统的性能产生重大影响。考虑到组装复杂性和人为错误之间的紧密联系，近年来进行了许多研究，着眼于组装过程中产生缺陷的问题。据观察，在每个组装阶段引入的平均缺陷数与相关组装复杂度之间的关系遵循幂律关系。因此，许多作者提出了数据对数转换，以使数学模型线性化。但是，正如文献中已经讨论过的，当模型以原始形式重新转换时，可能会出现明显的偏差，导致完全错误的预测。在本文中，分析和讨论了由于对装配缺陷进行预测的模型的对数转换而产生的偏差。提出并比较了两种替代方法来克服此缺点：对重新变换的拟合值使用偏差校正因子和幂律非线性回归模型。事实证明，后者是预测缺陷的最佳方法，该缺陷具有很少的非重复数据并且受高变异性的影响，例如在研究的案例中。对重新转换的拟合值使用偏倚校正因子和幂律非线性回归模型。事实证明，后者是预测缺陷的最佳方法，该缺陷具有很少的非重复数据并且受高变异性的影响，例如在研究的案例中。对重新转换的拟合值使用偏倚校正因子和幂律非线性回归模型。事实证明，后者是预测缺陷的最佳方法，该缺陷具有很少的非重复数据并且受高变异性的影响，例如在研究的案例中。