Machine Learning to Estimate Gross Loss of Jewelry for Wax Patterns

In mass manufacturing of jewelry, 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 jewelry. Machine learning is a technology that is a part of AI which helps to 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 jewelry 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.