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.

This article presents a proposal for a new method to evaluate tool wear incremental sheet forming process. Incremental sheet forming is an innovative forming process with a high interest in fields of the industry due to its low preparation cost and high flexibility, allowing the production of small batches at a reduced cost. Among the various types of incremental sheet forming processes, the single point incremental sheet forming is the most cost-effective, and unfortunately, the single point incremental sheet forming process has high dimensional errors. In order to understand the process and its dimensional errors better, this article shows the study of tool wear and the quality of surface finish with the generated data can correlate with the tool life. The study is carried out by means of a sequence of experimental tests of galvanized steel sheet conformation by altering the stamping parameters (vertical step in, feed and rotation) and capturing the values of the surface roughness of the parts, the forming tool wear and processing time. After the completion of the tests, the classical formulation of the Taylor equation was utilized to obtain a mathematical model capable of estimating the lifetime of the single point incremental sheet forming tool associated with a tool wear value and the desired dimensional accuracy in relation to the processing parameters for the part or tool pair analyzed in a computer numerical control machine tool. The results of the study present an original model of prediction of tool wear in relation to the input parameters for the single point incremental sheet forming process; the overall error rate is 33.44% for the wear model of prediction and 35.94% for the lifetime model of prediction.

In modern manufacturing, Digital Thread and Digital Twin technologies integrate and orchestrate data throughout the production lifecycle. To fully realize their potential, it is crucial to address challenges in data integration, management, and interoperability while enhancing system intelligence and contextual awareness. STEP-NC (STandard for the Exchange of Product model data - Numerical Control) emerges as a potential solution, enriching these digital systems with contextual information about products, processes, and machines. While STEP-NC has been extensively studied in machining, its application in Additive Manufacturing (AM) is still emerging. The current reliance on legacy formats like STL and G-code fails to meet the industry’s advancing needs. This work offers a comprehensive review of the current state of STEP-NC development for AM, highlighting a growing interest in its application, despite limited publications and advancements. To bridge these gaps, we propose STEP-NC entity definitions for managing data related to process parameters and operations in Fused Deposition Modeling (FDM) and Laser Metal Deposition (LMD) technologies. STEP-NC facilitates refined data management at the individual layer level, encompassing geometry, process parameters, material properties, and other key aspects. Additionally, we introduce a cyber-physical architecture for STEP-NC in manufacturing, aligned with the ISO 23247 framework. This architecture envisions a robust digital ecosystem driven by standards-based Digital Thread and Digital Twin technologies, with STEP and STEP-NC at its core, supported by essential open standards such as QIF, MTConnect, OPC-UA, and MQTT. By integrating contextual information, it enhances Digital Twin development for virtual monitoring, optimization, knowledge generation, and informed decision-making in manufacturing environments.

Digital Twins (DTs) are transforming manufacturing by bridging the physical and digital worlds, enabling real-time insights, predictive analytics, and enhanced decision making. In Industry 4.0, DTs facilitate automation and data integration, while Industry 5.0 emphasizes human-centric, resilient, and sustainable production. However, implementing DTs in robotic metal additive manufacturing (AM) remains challenging because of the complexity of the wire-based laser metal deposition (LMD) process, the need for real-time monitoring, and the demand for advanced defect detection to ensure high-quality prints. This work proposes a structured DT architecture for a robotic wire-based LMD cell, following a standard framework. Three DT implementations were developed. First, a real-time 3D simulation in RoboDK, integrated with a 2D Node-RED dashboard, enabled motion validation and live process monitoring via MQTT (message queuing telemetry transport) telemetry, minimizing toolpath errors and collisions. Second, an Industrial IoT-based system using KUKA iiQoT (Industrial Internet of Things Quality of Things) facilitated predictive maintenance by analyzing motor loads, joint temperatures, and energy consumption, allowing early anomaly detection and reducing unplanned downtime. Third, the Meltio dashboard provided real-time insights into the laser temperature, wire tension, and deposition accuracy, ensuring adaptive control based on live telemetry. Additionally, a prescriptive analytics layer leveraging historical data in FireStore was integrated to optimize the process performance, enabling data-driven decision making.

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 manufacturing sector, industrial robots are being increasingly improved to execute machining tasks as they exhibit significant advantages in terms of flexibility, cost-effectiveness, affordability, and larger work-space when compared to traditional computer numeric control (CNC) machines. However, programming this kind of equipment for robotic machining is complex, due to closed architecture controller and proprietary programming languages limitations. For that reason, this work aims at contributing to the adoption of the STEP-NC standard (STandard for the Exchange of Product model data- Numerical Control (ISO 10303-238 and ISO 14649)), generating programs for robotic machining operations. The STEP-NC data model enables the integration of information from design, process planning, simulation, manufacturing, and even inspection in a single platform, which could create new alternatives for industrial robotic machining programming. In this context, several previous studies are described in this manuscript aiming to highlight the contribution of this work, in addition to the analysis, implementation, and validation of six different STEP-NC architectures describing the advantages that each architecture provides for achieving robotic machining capabilities. Each introduced architecture can successfully generate a STEP-NC robotic machining program, either as ISO 10303-238 or ISO 14649, which are validated in a simulation environment with both a virtual robot model and a real industrial robot equipped with a LinuxCNC controller. This approach can be implemented in different industrial robots.