Comparison of machine learning algorithms for optimization and improvement of process quality in conventional metallic materials

Abstract

This paper presents a particular problem dealing with the apparition of burr during the drilling process in the aeronautic industry. This burr cannot exceed a height limit of 127 μm as set out by the aeronautical guidelines and must be eliminated before riveting. If this is not performed, it can cause structural damage which would constitute a danger due to the lack of safety. Moreover, the industry needs to find an automated and optimised process in which the drilling and deburring can be carried out in real time, eliminating those other unnecessary tasks, in order to obtain high-quality pieces. The work presents the applicability of data mining and machine learning techniques so as to obtain a real time burr detection model. This model could be implanted in the computer numerical control of the machine allowing the whole process to be automated and optimised. These techniques can be applied to other types of processes.

Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth

“…The high accuracy of ensemble predictions has been demonstrated in many milling processes: Bustillo et al (2011a) proposed the use of ensembles to predict surface roughness in ball-end milling operations. Maudes et al (2017) used random forest (RF) ensembles for the prediction of dimensional parameters in laser micromanufacturing of stents, and Ferreiro and Sierra (2012) used different kinds of ensembles for burr detection in a drydrilling process on aluminum Al 7075-T6.…”

A Review of Application of Machine Learning in Design, Synthesis, and Characterization of Metal Matrix Composites: Current Status and Emerging Applications

“…Finally, in a number of previous papers on metal manufacturing, several optimization techniques, such as the genetic algorithm, [131][132][133][134] particle swarm optimization, 46,135 Bayesian optimization, [136][137][138] and even statistical approaches like response surface methodology, 17,139,140 Taguchi's design of experiment, and analysis of variance (ANOVA), [141][142][143] have been used. The main difference between these methods and the RL is that the former do not ''learn from experience.''…”

Deep learning-based visual control assistant for assembly in Industry 4.0

“…The use of AI allows an automatic inspection system to make quality control decisions. According to Ferreiro and Sierra (2012), these quality inspection processes can be carried out either by simple sensors that measure weight, color or size (Fast-Berglund et al, 2013), as well as by computer vision systems using cameras, which allow quality identification regarding the shape of products processed at other workstations (Hedelind and Jackson, 2011).…”

“…Using Artificial intelligence (AI) can allow systems to make quality control decisions at workstations. According to Ferreiro and Sierra (2012), these quality inspection processes can be carried out using simple sensors (weight, color, size, etc.) (Fast-Berglund et al, 2013), or through advanced technologies such as computer vision.…”