基于机器学习的首饰蜡模毛损估算
Machine Learning to Estimate Gross Loss of Jewelry for Wax Patterns
摘要：
在大规模制造珠宝时，在制造之前估计毛损，以计算将熔模铸造成多件相同珠宝的模型的蜡重量。机器学习是人工智能的一部分，它有助于基于大量用户定义的数据创建具有决策能力的模型。在本文中，作者找到了一种在珠宝行业使用机器学习来估计这一关键毛损的方法。选择一个小的制造戒指数据集，并通过回归分析，发现使用ML算法从设计阶段本身的CAD文件收集的历史数据和属性中将估计误差从+-2-3减少到+-0.5。为了评估这种方法的可行性，必须对更大的数据集进行额外的研究。
英文摘要：
In mass manufacturing of jewellery, the gross loss is estimated before manufacturing to calculate the wax weight of the pattern that would be investment casted to make multiple identical pieces of jewellery. Machine learning is a technology that is a part of AI which helps create a model with decision-making capabilities based on a large set of user-defined data. In this paper, the authors found a way to use Machine Learning in the jewellery industry to estimate this crucial Gross Loss. Choosing a small data set of manufactured rings and via regression analysis, it was found out that there is a potential of reducing the error in estimation from +-2-3 to +-0.5 using ML Algorithms from historic data and attributes collected from the CAD file during the design phase itself. To evaluate the approach's viability, additional study must be undertaken with a larger data set.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
