In the context of the Industry 4.0, Digital Twins are essential tools for monitoring and controlling manufacturing processes. In this sense, this work presents the development an implementation of a Digital Twins architecture for machining using a CNC Haas Mini Mill machine. The proposed architecture is based on the current Digital Twins framework for manufacturing normalized by ISO 23247. MQTT and MTConnect protocols are used to transfer machine status and process data to cloud platforms for realizing storing, monitoring, visualization, and simulation activities. This approach was validated in a real CNC machine and it is feasible to be applied to other machines and manufacturing processes.

Recent developments in the field of Additive Manufacturing have been improving the capabilities of the technique not only to be able to build complex geometry parts layer by layer with different materials, but also including the so-called Industry 4.0 technologies, namely Internet of Things (IoT), big data (BD) and Digital Twins (DT). The combination of these technologies with Additive Manufacturing allows online process monitoring and simulation, along with the cloud storage of the process and geometry data collected during the material deposition. The analysis of such data allows online and post-deposition identification of eventual process instabilities that can lead to quality problems. Considering the above-mentioned concepts, this work presents a DT architecture based on the ISO 23247-Digital Twin Framework for Manufacturing standard. In this sense, an approach of a Digital Twin framework for metal additive manufacturing process in a robotic cell composed of a robotic arm, positioning table and welding machine is presented and validated, focusing on the collection and cloud storage of both geometrical and process data along with near real-time process simulation.

In the context of Industry 4.0, new methods of manufacturing, monitoring, and data generation related to industrial processes have emerged. Over the last decade, a new method of part manufacturing that has been revolutionizing the industry is Additive Manufacturing, which comes in various forms, including the more traditional Fusion Deposition Modeling (FDM) and the more innovative ones, such as Laser Metal Deposition (LMD) and Wire Arc Additive Manufacturing (WAAM). New technologies related to monitoring these processes are also emerging, such as Cyber-Physical Systems (CPSs) or Digital Twins (DTs), which can be used to enable Artificial Intelligence (AI)-powered analysis of generated big data. However, few works have dealt with a comprehensive data analysis, based on Digital Twin systems, to study quality levels of manufactured parts using 3D models. With this background in mind, this current project uses a Digital Twin-enabled dataflow to constitute a basis for a proposed data analysis pipeline. The pipeline consists of analyzing metal AM-manufactured parts’ surface roughness quality levels by the application of a Deep Neural Network (DNN) analytical model and enabling the assessment and tuning of deposition parameters by comparing AM-built models’ 3D representation, obtained by photogrammetry scanning, with the positional data acquired during the deposition process and stored in a cloud database. Stored and analyzed data may be further used to refine the manufacturing of parts, calibration of sensors and refining of the DT model. Also, this work presents a comprehensive study on experiments carried out using the CW-GTAW (Cold Wire Gas Tungsten Arc Welding) process as the means of depositing metal, resulting in hollow parts whose geometries were evaluated by means of both 3D scanned data, obtained via photogrammetry, and positional/deposition process parameters obtained from the Digital Twin architecture pipeline. Finally, an adapted PointNet DNN model was used to evaluate surface roughness quality levels of point clouds into 3 classes (good, fair, and poor), obtaining an overall accuracy of 75.64% on the evaluation of real deposited metal parts.