**5. Conclusions**

This work allowed us to analyze, study, and test the applicability of artificial neural networks in estimating machining times. One of the main contributions of the presented work is the development of an ANN that predicts the machining time of standard parts for plastic injection molds with a percent error of 2.52%.

The application of ANNs in this work proved to be a quick and efficient way to estimate machining times. The introduction of the total quantity and volume of each of the most common features regarding the standard parts proved to be sufficient for this purpose, and it was not necessary to detail individually in the training of the networks the dimensions of each element to be machined. The specification of each element to be machined would make the presented method slower, which would not meet the proposed goals.

The different tests carried out demonstrated that:


The present work brings new knowledge about the possibility of applying ANNs in the estimation of machining times, which is not very common in the recently analyzed literature. This enables the use of these methods in industry sectors, such as the mold industry, enabling the determination of the machining time and overall production cost of standardized mold parts. However, there are some current limitations of the proposed work. This study was developed for an application regarding the production of mold parts, and although this methodology can be employed in other industry sectors (particularly machining sectors of standardized parts), this would involve calibration and training of new ANNs. Despite this fact, this work highlights the use of ANNs compared to more time-consuming and expensive alternatives when determining operation times and costs.

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/met12101709/s1, Figure S1: List of produced parts.

**Author Contributions:** A.R.: investigation, formal analysis and writing—review and editing; F.J.G.S.: conceptualization, methodology, project administration, resources, supervision and writing—review and editing; V.F.C.S.: formal analysis, validation, visualization and writing—review and editing; A.G.P.: formal analysis, validation and writing—review and editing; L.P.F.: formal analysis, validation

and writing—review and editing; T.P.: formal analysis, validation and writing—review and editing. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** No data is made available regarding this work.

**Acknowledgments:** Authors thank INEGI—Institute of Science and Innovation in Mechanical and Industrial Engineering for its support.

**Conflicts of Interest:** The authors declare no conflict of interest.
