Nowadays, reconfiguration and adaptation by means of optimal re-parametrization in industrial cyber-physical systems (ICPS) is one of the bottlenecks for the digital transformation of the manufacturing industry. This work proposes a cloud-to-edges-based ICPS equipped with machine learning techniques. The proposed reasoning module includes a learning procedure based on two reinforcement learning techniques, running in parallel, for updating both the data-conditioning and processing strategy and the prediction model. The presented solution distributes computational resources and analytic engines in multiple layers and independent modules increasing the smartness and the autonomy for monitoring and control the behavior at shop floor level. The suitability of the proposed solution, evaluated in a pilot line, is endorsed by fast time response (i.e., 0.01s at the edge level) and the appropriate setting of optimal operational parameters for guaranteeing the desired quality surface roughness during macro and micro milling operations.

Nowadays, the application of novel soft-computing methods to new industrial processes is often limited by the actual capacity of the industry to assimilate state-of-the-art computational methods. The selection of optimal parameters for efficient operation is very challenging in microscale manufacturing processes, because of intrinsic nonlinear behavior and reduced dimensions. In this paper, a decision-making system for selecting optimal parameters in micromilling operations is designed and implemented using simple and efficient soft-computing techniques. The procedure primarily consists of four steps: an experimental characterization; the modeling of cutting force and surface roughness by means of a multilayer perceptron; multiobjective optimization using the cross-entropy method, taking into account productivity and surface quality; and a decision-making procedure for selecting the most appropriate parameters using a fuzzy inference system. Finally, two different alloys for micromilling processes are considered, in order to evaluate the proposed system: a titanium-based alloy and a tungsten-copper alloy. The experimental study demonstrated the effectiveness of the proposed solution for automated decision-making, based on simple soft-computing methods, and its successful application to a real-life industrial challenge.

One of the most important operations during the manufacturing process of a pressure vessel is welding. The result of this operation has a great impact on the vessel integrity; thus, welding inspection procedures must detect defects that could lead to an accident. This paper introduces a computer vision system based on structured light for welding inspection of liquefied petroleum gas (LPG) pressure vessels by using combined digital image processing and deep learning techniques. The inspection procedure applied prior to the welding operation was based on a convolutional neural network (CNN), and it correctly detected the misalignment of the parts to be welded in 97.7% of the cases during the method testing. The post-welding inspection procedure was based on a laser triangulation method, and it estimated the weld bead height and width, with average relative errors of 2.7% and 3.4%, respectively, during the method testing. This post-welding inspection procedure allows us to detect geometrical nonconformities that compromise the weld bead integrity. By using this system, the quality index of the process was improved from 95.0% to 99.5% during practical validation in an industrial environment, demonstrating its robustness.

A simple multi-objective cross-entropy method is presented in this paper, with only four parameters that facilitate the initial setting and tuning of the proposed strategy. The effects of these parameters on improved performance are analyzed on the basis of well-known test suites. The histogram interval number and the elite fraction had no significant influence on the execution time, so their respective values could be selected to maximize the quality of the Pareto front. On the contrary, the epoch number and the working population size had an impact on both the execution time and the quality of the Pareto front. Studying the rationale behind this behavior, we obtained clear guidelines for setting the most appropriate values, according to the characteristics of the problem under consideration. Moreover, the suitability of this method is analyzed based on a comparative study with other multi-objective optimization strategies. While the behavior of simple test suites was similar to all methods under consideration, the proposed algorithm outperformed the other methods considered in this paper in complex problems, with many decision variables. Finally, the efficiency of the proposed method is corroborated in a real case study represented by a two-objective optimization of the microdrilling process. The proposed strategy performed better than the other methods with a higher hyperarea and a shorter execution time.