Manufacturing systems are making a shift towards intelligent and automated detection systems in order to save cost and time in different phases of the product lifecycle as well as keep the production’s quality levels high. Currently, defects detection is based on either the active involvement of the human operator or the devotement of a significant amount of time in order to train models that utilize machine learning techniques. In this context, this study presents an automated optical system, which is able to detect and classify aesthetical defects in different sizes, shapes and positions right after the casting process. A lightweight and robust architecture is adopted towards supporting different products. Also, an advanced user interface is implemented on top of a web application. The aim is to support the operator and provide awareness of the process status by visualizing information related to production and quality aspects. A case study deriving from the automotive industry has been used in order to test the accuracy of the proposed optical system in metal parts. The benefits of the proposed approach are discussed and proposed as a more efficient way of inspecting the quality of finished products under the scope of Industry 4.0.

Quality inspection plays a vital role in current manufacturing practice since the need for reliable and customized products is high on the agenda of most industries. Under this scope, solutions enhancing human–robot collaboration such as voice-based interaction are at the forefront of efforts by modern industries towards embracing the latest digitalization trends. Current inspection activities are often based on the manual expertise of operators, which has been proven to be time-consuming. This paper presents a voice-enabled ROS2 framework towards enhancing the collaboration of robots and operators under quality inspection activities. A robust ROS2-based architecture is adopted towards supporting the orchestration of the process execution flow. Furthermore, a speech recognition application and a quality inspection solution are deployed and integrated to the overall system, showcasing its effectiveness under a case study deriving from the automotive industry. The benefits of this voice-enabled ROS2 framework are discussed and proposed as an alternative way of inspecting parts under human–robot collaborative environments. To measure the added value of the framework, a multi-round testing process took place with different parameters for the framework’s modules, showcasing reduced cycle time for quality inspection processes, robust HRI using voice-based techniques and accurate inspection.

The automotive industry is one of the most automatized industries, employing more than one million robots worldwide. Although several steps in car production are completely automated, many steps are still carried out by operators, especially in tasks requiring high dexterity. Additionally, customization and deployability are still pending issues in this industry, where a real collaboration between robots and operators would increase the reconfigurability of the assembly lines. This paper presents an innovative robotic cell focused on the motor and gearbox assembly, including collaborative industrial robots and autonomous mobile manipulators along the different assembly stations. The design also incorporates a human-centered approach, with an enhanced human interface to facilitate the interaction with operators with the complete robotic cell. The proposed approach has been deployed and validated on a real automotive industrial scenario, obtaining promising metrics and results.

Background: In the pursuit of sustainable manufacturing logistics, the integration of human–robot collaboration (HRC) into shop floors with legacy systems remains a significant challenge due to dynamic environments that complicate task and action planning. To address this challenge, this study investigates a sustainable HRC workstation implemented within a real-world electrical panel assembly use case in the lift manufacturing sector. Methods: The article examines the role of an AI-enabled dynamic task and action planning tool that coordinates human and robotic resources. The proposed solution integrates collaborative robotics using a multi-criteria scheduling algorithm alongside real-time replanning capabilities. Furthermore, the study proposes an evaluation framework based on the triple bottom line (TBL) approach to assess the developed solution. Results: The findings suggest that executing assembly tasks through this collaborative method supports sustainable operations scheduling for all resources. Conclusions: Ultimately, the proposed HRC solution demonstrates the potential to simultaneously enhance environmental, financial and social performance within manufacturing logistics and operations.