The scientific literature regarding additive manufacturing, mainly the material extrusion method, suggests that the mechanical characteristics of the parts obtained by this technology depend on a number of the input factors specific to the printing process, such as printing temperature, printing trajectory, layer height, etc., and also on the post-process operations for parts, which, unfortunately, requires supplementary setups, equipment, and multiple steps that raise the overall costs. Therefore, this paper aims to investigate the influence of the printing direction, the thickness of the deposited material layer, and the temperature of the previously deposited material layer on the part tensile strength, hardness by means of Shore D and Martens hardness, and surface finish by using an in-process annealing method. A Taguchi L9 DOE plan was developed for this purpose, where the test specimens, with dimensions according to ISO 527-2 type B, were analysed. The results showed that the presented in-process treatment method is possible and could lead to sustainable and cost-effective manufacturing processes. The varied input factors influenced all the studied parameters. Tensile strength tended to increase, up to 12.5%, when the in-process heat treatment was applied, showed a positive linear variation with nozzle diameter, and presented considerable variations with the printing direction. Shore D and Martens hardness had similar variations, and it could be observed that by applying the mentioned in-process heat treatment, the overall values tended to decrease. Printing direction had a negligible impact on the additively manufactured parts’ hardness. At the same time, the nozzle diameter presented considerable variations, up to 36% for Martens hardness and 4% for Shore D, when higher diameter nozzles were used. The ANOVA analysis highlighted that the statistically significant factors were the nozzle diameter for the part’s hardness and the printing direction for the tensile strength.

Continuous research in the field of metal additive manufacturing has led to the need for constant improvement of manufacturing parameters especially in the case of FDM (Fused Deposition Modeling) manufacturing. In recent years, the main directions outlined for productivity and quality improvement were related to higher printing speed and the use of ironing-type processes. This article aims to study the manufacturing parameters of the dimensional accuracy and surface quality of FDM-manufactured 316L stainless steel. The degree of novelty is given by the application of the ironing process for the green part. A full factorial 3³ experimental design was designed for this study, in which the factors studied were ironing angle, ironing speed, and layer spacing during ironing. The dimensional accuracy and surface roughness were analyzed by means of deviation measurement from CAD to the green part and final part after the sintering process. Using the design of experiments offers the possibility of applying the analysis of variance (ANOVA) which provides information about the degree of influence of each of the studied factors. The results obtained for the dimensional accuracy showed that the ironing direction had the biggest influence on the Z-axis shrinkage. Overall, approximately 6% shrinkage in the Z and Y directions was obtained while in the X directions, the shrinkage percentage was around 20%. Surface roughness showed an improvement with higher ironing speeds for the green part while for the sintered part the most significant factor was ironing spacing.

Artificial intelligence is increasingly used in all fields, especially in the area of risk management within Integrated Management Systems (IMS). The paper aims to highlight the role of artificial intelligence (AI) in risk management, therefore providing opportunities for industrial organizations, offering significant advantages for improving the efficiency and accuracy of risk assessment and mitigation processes. By using advanced AI technologies, organizations can anticipate and manage risks more effectively, therefore optimizing operational performance and resilience. We reviewed and explored the main applications of AI implementation, risk management, the barriers encountered, and the advantages and disadvantages of using AI. A holistic analysis of IMS risk management, identification and assessment, operational efficiency of routine tasks, real-time data analysis, and immediate decision-making using AI was performed. The methods and technologies used are analyzed, along with the associated challenges, providing a comprehensive perspective on the impact of AI in industrial organizations. We conclude that the use of AI addresses challenges related to data quality, model interpretation, ethical issues, and high costs of implementation and management, which require qualified personnel. Also, we conclude that the use of AI in risk management for IMS presents significant opportunities for industrial organizations, including enhanced process monitoring, rapid information analysis, and swift response to emerging risks. This enables the optimization of risk management strategies, ultimately leading to increased operational safety and efficiency.

In recent years, to reduce cars costs, research has been conducted on dual-phase steels with low manganese content (below 1.0%). This study investigated the influence of technological parameters of heat treatment (heating temperature and cooling medium) on such steels’ structures and mechanical properties. The ferrite-martensitic structures, specific for dual-phase steels, were obtained by intercritical quenching: heating of samples (made of alloys with 0.511% Mn, respectively 0.529% Mn) to temperatures located between critical points Ac₁ and Ac₃, followed by cooling in water without mechanical agitation and in water activated with ultrasounds at the frequency of 59 kHz. Through metallographic analyses and tensile tests, it was possible to determine the volume fraction of martensite, the ferrite microhardness, the ultimate tensile strength, the total elongation, and with the obtained data, their variations with the heating temperature and the cooling medium were established. Raising the heating temperature (between 760 °C and 820 °C) and using ultrasounds at cooling increased the volume fraction of martensite and the ferrite microhardness. This fact has increased the mechanical strength and reduced the deformability of the studied dual-phase steels. Intercritical quenching in water activated with ultrasounds provided values of structural characteristics and mechanical properties very close to those obtained by quenching in water without mechanical agitation, but was accomplished using a higher-temperature heating. The results obtained were compared with those determined in previous research, performed on dual-phase steel with 1.90% Mn.

Roxana Puscaselu(gheorghita), Traian Lucian Severin, Sonia Amariei - Use of Biopolymers in Designing Edible Packaging Materials for Food Industry Development of statistical models

The present study investigates the dry sliding wear behaviour of pure Zn, Zn–3Mg, and Zn–3Mg–0.5Y biodegradable alloys using mass loss measurements, friction torque monitoring on an Amsler tribometer, and optical profilometry of wear tracks. The microstructure of the Zn–Mg–Y alloy exhibited an α-Zn matrix comprising Zn–Mg intermetallic constituents and dispersed Y-rich phases. Tribological testing at 20 N and 30 N revealed a marked enhancement in wear resistance for Zn–3Mg in comparison to pure Zn, attributable to matrix strengthening by intermetallic phases. Despite the stabilising effect of Y on the friction response, there was no consistent reduction in wear volume under higher loads. Surface investigations have revealed a multifaceted wear mechanism, characterised by a combination of abrasion, oxide tribolayer formation, and localised adhesion. The measured wear rates were found to fall within the range documented in the available literature concerning biodegradable Zn-based alloys, thereby confirming the experimental validity of the findings. In summary, Zn–3Mg exhibited the optimal equilibrium between friction stability and wear resistance under the examined dry sliding conditions. However, further research in physiological environments is necessary to evaluate its biomedical applicability.