The relative low stiffness of industrial robots is a major limitation on the development of flexible and reconfigurable systems in applications in which process forces and vibration lead into significant tool path deviations with respect to the programmed path as in the case of robotic machining. This paper presents a novel factorial procedure that allows for the preliminary study of the main conditions in robotic machining operations and it determines the critical factors that are affecting the machining path of any robotic cell in order to obtain the process conditions with lower path deviations. In this procedure the most influential robotic machining constraints were identified and classified, the factorial design of experiments was used to enable the execution of the experimental tests and the machining tool path deviation predictive methodology (PREMET) was used to determine the cutting tool path deviation between the programmed and the experimental path as a function of the process variables. Experimental trials have been carried out in order to determine the main factors that affect the robotic machining and influence the main constraints of the process, showing a reduction greater than a 36% of the cutting tool path deviation in groove milling of aluminum. The critical factors identified in order of importance are: hardness of the material, location of the workpiece, orientation of milling head relative to working direction and cutting conditions. This procedure can be extended to future factorial studies to improve the precision of robotic machining (in operations such as face milling, contouring, pocketing) and to establish design criteria for machining robotic cells.

This article provides an insightful overview of titanium alloys, focusing on their production, key applications, and the challenges in machining and processing. It delves into the extraction methods like the Kroll and Hunter’s processes, the formation of alloys with elements like aluminum and vanadium, and their use in aerospace, biomedical, and automotive industries due to their excellent strength-to-weight ratio. This issue also addresses the difficulties in machining titanium, efforts in enhancing its properties, and the role of advanced manufacturing techniques, especially Hybrid Manufacturing, in improving efficiency and reducing costs. This study intended to perform a comprehensive literature analysis on titanium and its alloys, encompassing extraction, manufacturing, and post-processing, while examining contemporary advancements, employed approaches, and resultant findings in this domain. This work seeks to provide organized information on research pertaining to titanium, specifically the Ti6Al4V alloy, encompassing its extraction, manufacturing processes, post-processing, applications, and identified challenges, thereby facilitating the rapid knowledge acquisition of emerging researchers in this domain. Additionally, a SWOT analysis is included as a method for scenario analysis.

The current trend in machining with robotic arms involves leveraging Industry 4.0 technologies to propose solutions that reduce path deviation errors. This approach presents significant challenges alongside promising advancements, as well as a substantial increase in the cost of future industrial robotic cells, which is not always amortizable. As an alternative or complementary approach to this trend, methods encouraging the occasional use of Industry 4.0 devices for characterizing the behavior of the actual physical cell, calibration, or adjustment are proposed. One such method, called FlePFaM, predicts flatness errors in face milling operations using robotic arms. This is achieved by estimating tool path deviation errors through the integration of a simple model of the robot arm’s mechanics with the cutting forces vector of the process, thereby optimizing machining conditions. These conditions are determined through prior empirical estimations of mass, stiffness, and damping. The conducted tests enabled the selection of the most favorable combination of variables, such as the robot wrist configuration, the position and orientation of the workpiece, and the predominant milling orientation. This led to the identification of the configuration with the lowest absolute flatness error according to the model’s predictions. The results demonstrated a high degree of similarity—between 97% for the closest case and 57% for the farthest case—between simulated and experimental flatness error values. FlePFaM represents a significant step forward in adopting innovative robotic arm solutions for reliable and efficient production. FlePFaM includes dimensional flatness indicators that provide practical support for decision making.

Research on occupational accidents is a key factor in improving working conditions and sustainability. Fatal accidents incur significant human and economic costs. Therefore, it is essential to examine fatal accidents to identify the factors that contribute to their occurrence. This study presents an overview of fatal heart attack accidents at work in Spain over the period 2009–2021. Descriptive analysis was conducted considering 13 variables classified into five groups. These variables were selected as predictors to determine the occurrence of this type of accident using a machine learning technique. Thirteen Naïve Bayes prediction models were developed using an unbalanced dataset of 15,616 valid samples from the Spanish Delta@database, employing a two-stage algorithm. The final model was retained using a General Performance Score index. The model selected for this study used a 70:30 distribution for the training and test datasets. A sample was classified as a fatal heart attack if its posterior probability exceeded 0.25. This model is assumed to be a compromise between the confusion matrix values of each model. Sectors with the highest number of heart attacks are ‘Health and social work’, ‘Transport and storage’, ‘Manufacturing’, and ‘Construction’. The incidence of heart attacks and fatal heart attack accidents is higher in men than in women and higher in private sector employees. The findings and model development may assist in the formulation of surveillance strategies and preventive measures to reduce the incidence of heart attacks in the workplace.

With the advancement of technology and the digitalization of manufacturing processes, optimized production of large-scale structural components faces increasing challenges due to mass product customization, workspace constraints, and the high cost of equipment such as CNC machines. Arm-based robotic manufacturing systems, as a substitutive or complementary alternative with strong potential, have gained significant attention as a means to address current challenges in machining. This work presents a state-of-the-art review of existing techniques and methods for optimizing robotic machining performance, with a particular focus on research advances in posture optimization of articulated robots. These advances are enabling the use of serial robotic arms in material removal operations as a technically and economically viable alternative to conventional machine tools. The document is structured as a comprehensive knowledge recap and systematic analysis of the technical barriers and limitations that affect dimensional quality in robotic machining—primarily due to the limited mechanical stiffness inherent in robotic arms when performing cutting tasks. It compiles and classifies a range of robot posture optimization tools. A SWOT matrix is used to identify the key technical factors that influence the suitability of optimization methods in different industrial contexts. Unlike previous reviews that address posture or dynamics in isolation, this work (i) consolidates posture optimization methods into a practical three-pillar framework (optimize–sense–compensate), (ii) maps methods to a decision-oriented SWOT for industrial selection, and (iii) extends the analysis to collaborative machining, including posture–safety trade-offs under collaborative machining standards.