Today, the manufacturing industry faces significant pressure to become more sustainable while maintaining competitiveness. In the spirit of the green manufacturing philosophy, numerous efforts have been undertaken to replace or reduce waste materials that burden the environment, and to promote environmentally friendly raw materials. Minimizing energy consumption in CNC machining is also crucial, since it offers significant potential to reduce environmental impact. This review focuses on recent developments in energy consumption monitoring and modelling, energy-efficient machining strategies, energy-saving solutions in CNC machine tools and controllers, and optimized tool path planning algorithms. The first step in implementing energy optimization is modelling the energy consumption of machine tools. Traditionally, the energy demand for cutting has been described using formulas that rely on the material removal rate and specific cutting energy. However, in recent years, data-driven techniques have become more popular, necessitating the development of data monitoring and acquisition techniques. After modelling energy consumption, optimizing cutting parameters is the most straightforward way to increase energy efficiency. However, the complex effect of machining parameters and the quality requirements pose significant challenges. As a result, soft computing methods such as heuristic algorithms and machine learning techniques have become essential and remain the subject of extensive research today. Energy-saving functions of modern CNC machine tools, such as standby mode, auto-shutdown features, and regenerative drives, also play an important role in reducing overall energy consumption. AI-supported tool path generation algorithms have significant potential to improve energy efficiency and sustainability, but this potential is currently underutilized. Future research will presumably focus on intelligent machining technologies using adaptive control and real-time monitoring with predictive optimization methods. These advancements are expected to reduce the environmental impact of CNC machining while enhancing its overall sustainability and productivity.
Magnesium oxysulfate (MOS) cement exhibits excellent adhesion and binding properties. It is lightweight, cheap and easy to use as well as has very good cementing properties compared to ordinary Portland cement. It is used in many types of aesthetic applications due to its white color, moreover, can be easily prepared by simply combining magnesia and a concentrated solution of Epsom salt. Gum arabic and silica gel G were used as additives with MOS to enhance water resistance and strength. The results showed that the incorporation of different amounts of gum arabic and silica gel G in MOS enhances the compressive strength of the modified product and lengthens setting times.The results showed that among all the compositions, MOS cement containing 5% additives (2.5% gum arabic and 2.5% silica gel G) significantly enhanced the long-term strength and water resistance of MOS cement.
Gallic acid, a polyphenolic compound with strong antioxidant activity, exhibits notable anti-inflammatory, anticancer, antibacterial and antioxidant properties, making it valuable across the food, packaging, cosmetics and pharmaceutical industries. For its commercial utilization, efficient and sustainable extraction from natural sources is essential. This study investigates the feasibility of liquid–liquid extraction (LLE) for the separation of gallic acid from aqueous solutions using natural solvents. Four vegetable oils - mustard, sesame, sunflower and soybean oil - were evaluated as extractants at various gallic acid concentrations. Equilibrium data collected at 298.15 ± 1 K were used to calculate distribution coefficients (KD) and extraction efficiencies (%E). The results revealed the following average trends in KD and %E: soybean oil (0.4019, 28.69%) > sunflower oil (0.3372, 25.23%) > sesame oil (0.2982, 22.99%) > mustard oil (0.2676, 21.15%), respectively. The highest efficiency was achieved with soybean oil at 0.05 mol·L-1 gallic acid, yielding 30.40% separation. Multiple separation units in series may be applied to achieve even higher separations. These findings highlight the potential of natural solvents for environmentally safe gallic acid separation.
In this study, the effect of various co-ligands on the reactivity of iron-based catalytic systems toward the oxidation of 2-phenylpropionaldehyde (PPA) was investigated. The oxidations were carried out in the presence of hydrogen peroxide (H2O2) as the terminal oxidant. The reactivity of the catalytic species was found to strongly depend on the electronic and steric properties of the applied co-ligands. By varying the co-ligand environment, significant changes were observed in both the reaction rate and product distribution, indicating that the coordination sphere around the iron center plays a key role in modulating the catalytic behavior. These findings highlight the importance of ligand effects in tuning the selectivity and efficiency of H2O2-driven oxidation reactions of aldehydes.
Topical infections and their difficult treatments are one of the challenges arising from antimicrobial resistance. Herbal formulations are an important resource in drug development for the treatment of infections that cause various health problems. Therefore, this study aimed to determine the antimicrobial and photoprotective potential of blueberry (Vaccinium spp.) twig extract (BTE) as well as formulate a herbal cream for topical treatments. The biological activity of BTE against clinically important strains such as Escherichia coli O157:H7, Staphylococcus aureus ATCC 25923, Bacillus cereus RSKK 863, Candida albicans ATCC 10231 and Candida glabrata RSKK 04019 was evaluated using both disc diffusion and micro-dilution methods. BTE at a concentration of 4 mg/µL produced the highest inhibition zone diameter of 11.01 mm on B. cereus RSKK 863, followed by E. coli O157:H7 with 10.42 mm. The results from micro-dilution methods indicated that the lowest minimum inhibitory concentration (MIC) value (25 µg/µL) was against B. cereus RSKK 863. The protective capacity of BTE against UV-B rays was determined spectrophotometrically. The sun protection factor (SPF) of the extract was calculated as 4.60 and in cream formulations containing 10 mL of this extract, SPF increased to 26.12. Moreover, in cream formulations where BTE was combined with Streptococcus thermophilus MAS-1, a synergistic antimicrobial effect was observed with wider zones of inhibition against all test microorganisms. Based on the results, BTE may be suitable for evaluation as an antimicrobial, antifungal and photoprotective agent in multifunctional topical formulations containing natural ingredients.
This study reports the fabrication of two novel electrochemical sensors for the quantitative determination of the drug Lomefloxacin (LOM), employing LOM–silicotungstic acid (STA) and LOM–molybdophosphoric acid (MPA) ion pairs as electroactive materials. The sensors were systematically characterized in terms of gradient, linear concentration range, detection limit, response time, pH tolerance and shelf life. The applicability of the sensors was demonstrated by successfully determining LOM in pharmaceutical tablet formulations as well as in real samples such as urine using the standard addition method. The proposed sensors exhibited long operational lifetimes, high stability, sensitivity, precision, accuracy and selectivity. Moreover, they offer a cost-effective, simple and rapid analytical approach for routine LOM determination.
Plant extracts offering skin health benefits such as antioxidant and photoprotective properties are increasingly used in cosmetic formulations. The antioxidant properties of ethanol extracts of Annona muricata (soursop) leaves and bark were assessed using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging assay. The leaf extract yielded 448 µg GAE/mL (micrograms of gallic acid equivalent per milliliter) total phenolic content, 119 µg QE/mL (micrograms of quercetin equivalent per milliliter) total flavonoid content and 101 µg TAE/mL (micrograms of tannic acid equivalent per milliliter) total tannin content, while the bark extract yielded 575 µg GAE/mL total phenolic content, 24.2 µg QE/mL total flavonoid content and 126 µg TAE/mL total tannin content. The 50% inhibitory concentration (IC50) values reveal the free radical scavenging properties of the samples as follows: ascorbic acid (104 µg/mL) > bark extract (128 µg/mL) > leaf extract (198 µg/mL), showing that ascorbic acid, a commercial antioxidant, exhibited the most effective free radical scavenging property. The free radical scavenging abilities of these extracts may be attributed to their rich phytochemical contents. Cosmetic emulsions formulated with the plant extracts were oil-in-water emulsions stable for 60 days at room temperature (30 °C), in an incubator (40 °C) and in a refrigerator (7 °C). The results showed that A. muricata leaf and bark extracts could potentially be applied as natural free radical scavenging ingredients in antioxidant and anti-aging cosmetic formulations for photoprotective skincare through ultraviolet (UV) radiation shielding.
Integrating Artificial Intelligence (AI) into process control is one of the most significant technological trends in industrial automation today. The generative programming of Programmable Logic Controllers (PLCs) is a prominent example of this development. While Artificial Neural Networks (ANNs) have previously been applied for tasks such as natural language processing and fault prediction in PLC hardware, recent advancements in Large Language Models (LLMs) have further expanded AI capabilities, enabling the interpretation of complex prompts and assisting with control code generation. Development tools and industrial copilots powered by generative AI are increasingly being proposed to support engineers in managing control systems, with the potential to simplify and accelerate control software development. In contrast to these promises, using generative models in PLC programming is still in its early stages, characterized by exploratory research and cautious implementation. This review provides a systematic overview of recent developments in AI-assisted PLC programming, focusing on generative approaches. It synthesizes emerging methodologies, tools, and applications while critically examining current limitations and outlining potential research directions in industrial control systems.
The management of cutting tool wear and tool life is a long-standing topic in machining research and practice. Although a wide range of analytical and data-driven wear models has been proposed, their direct applicability indefining a robust tool life criterion and for practical tool life prediction remains limited. In this paper, weanalyze several alternative functional forms for the flank wear-time (VB-t) relationshipwith the specific aim of defining an analytically tractable tool life criterion based on the minimum wear intensity and the inflection point of the wear curve. Linear, power, exponential and third-degree polynomial models are compared in terms of goodness of fit, monotonicity and the possibility of deriving closed-form expressions for the inflection point and the corresponding lifetime. Based on these criteria, monotonically increasing exponential and cubic polynomial models are identified as the most promising candidates. Their applicability is illustrated using a representative turning test, using a single measured wear curve as a case study rather than full statistical validation. The analysis shows that once the model parameters are identified for a given cutting system, the proposed framework can provide a transparent, analytically defined lifetime criterion and can support prediction of the remaining tool life. The work is thereforeintended as a methodological contribution and as a starting point for future, more comprehensive experimental validation and for integration into digital toolcondition monitoring systems
The acoustic and mechanical properties of tonewoods commonly used in traditional instrument making -such as mahogany, oak, maple, and walnut -play a crucial role in shaping the sound characteristics of musical instruments. The aim of this research is to comprehensively analyze the vibration dynamics of these wood types through experimental measurements and finite element simulations. A further objective is to explore the potential substitution of these materials (primarily their acoustic functions) using advanced additive manufacturing technologies.In the initial phase of the study, harmonic excitation was applied to determine the vibration characteristics of the individual wood specimens. This enabled the quantitative evaluation of parameters such as amplitude, acceleration, damping behavior, and the distribution of natural frequencies. Based on the measured data, parametric material models were constructed in the ANSYS finite element simulation environment to validate the experimental results. During therefinement of the numerical models, special attention was paid to the anisotropic nature of the materials, accurate geometric representation, and realistic implementation of boundary conditions.The long-term goal of the research is to develop an alternative geometry -manufactured using 3D printing technology -that can mimic the mechanical and acoustic functions of traditional tonewoods. The geometric optimization of such prototypes is based on simulation outcomes, while also considering their acoustic performance.
Machine learning, particularly reinforcement learning, plays an increasing role in optimizing complex industrial processes. One such challenge arises in production systems, where products must be processed, often involving nontrivial scheduling and routing problems. The paper presents a reinforcement learning (RL)-based method to optimize a specific production cell, where two material-moving units and several machining units must cooperate to manufacture items that require both processing and occasional cleaning. The proposed methodology models the environment as a Markov Decision Process and employs RL algorithms to maximize throughput. Several popular RL algorithms were compared, and it was found that Maskable Proximal Policy Optimization (Maskable PPO) delivers the best performance, as agent-specific, valid and differentiated behavior is ensured for both material handling and machining units through action masking. Among the various masking strategies tested, a distinct masking approach proved to be the most effective.
In the metal chip formation zone, during the large-scale and high-speed shaping of the raw material, high-frequency but low-energy pressure waves are generated within the solid material. The entire set of these pressure waves can be detected in machining research through acoustic emission measurement. The magnitude and features of the acoustic emission mainly depend on the material of the workpiece and tool, the technological parameters, and tool wear. Exploiting this property, it has become widespread in the field of process monitoring. By relating the acoustic emission to the theoretical chip cross-section, we obtain a characteristic indicator proportional to the chip formation process. Examining the specific acoustic emission as a function of the theoretical chip cross-section allows us to clearly observe and measure the transient phase of the cutting process, i.e., when the tool edge enters and exits the cutting zone. The range of small chip thickness is considered to be the range of theoretical chip thicknesses that are equal to or smaller than the tool edge radius. In this range, the statistical behavior of the specific acoustic emission differs from that observed at larger theoretical chip thicknesses, indicating a change in chip formation quality—referred to as process transition or instability. Cutting in this unstable range is undesirable and can deteriorate the finish of the machined surface. This study presents the measurement technique for the specific acoustic emission, the development of related process indicators, and their application to monitor the transient stage of cutting.
In recent years, demand for aluminum and aluminum alloys has been increasing because of their favorable properties. The properties of aluminum alloys are similar to those of structural steel, but their weight is approximately one third of that of steel. Technological development makes it possible to weld metals that are difficult to join with traditional fusion welding, such as aluminum alloys. The process is known as Friction Stir Welding (FSW). This article briefly introduces the FSW procedure and its application. In this research, 5053 aluminum alloys were welded with this technology, using customized FSW tools. These tools were manufactured using 3D printing technology, which enabled the manufacture of complex geometries. After welding, the pieces were subjected to the following material tests: visual inspection, tensile testing, hardness testing and metallographic analysis.
As the space industry grows quickly and green propellants enjoy growing interest, the key question of research is no longer basic viability but productization. The most valuable engine is one that is qualified and available as close to "off the shelf" as possible. But publications on the process of taking an engine from first demonstrations to true product status are rare. This paper describes the process of transitioning Benchmark Space Systems'22N Ocelot engine -first flown as the 1.0 product version in2022 -into a high-volume, well-characterized product, including details on the qualification program for the 1.2 version, learnings from rate production, and a deep dive into a particular production reliability issue. In doing so, it hopes to shed light on not only spacecraft thruster production but on the productization of space technology in general.
Cutting ability shows how well and economically a material can be machined by cutting. The term is widely used in manufacturing, but there is no agreed, precise definition, usually reflecting the direct interests of the user. It is considered to be a property related to the properties of the material, but there is no generally accepted parameter for meas ring it. It is not only influenced by the physical and mechanical properties of the material, but also by the material and design of the cutting tool, the cooling, the stability of the machine tool and the cutting parameters used. Cutting ability can be assessed by various quantitative and qualitative characteristics that apply to the tool, the workpiece and the process. The aim of this article is to present various interpretations of cutting ability, to classify and investigate the parameters that influence cutting ability and characterize it, and to present a methodology for the development of qualitative and quantitative metrics.
In recent years, efforts have been made to adapt electrical discharge machining (EDM) for micro-hole production in turbine blades to safer and more environmentally friendly conditions. The use of deionized water as a working fluid in EDM represents one such solution. The study investigates the effect of EDM parameters on two difficult-to-cut materials, Inconel 718 and Ti-6Al-4V. The results are considered preliminary findings, highlighting both the advantages and limitations of the process. They indicate that electrical discharge drilling (EDD) using deionized water is a promising solution. In this process, the use of deionized water enables additional electrochemical dissolution, thereby accelerating material removal. The analysis shows that the adopted parameter values are more suitable for drilling the titanium alloy; however, the accuracy of the resulting hole geometry requires further improvement.
This paper presents novel application possibilities in industrial environments based on the experiences of disaster events, using the ecosystem of the B-prepared Horizon Europe project. The most important task of the project is to offer novel and effective solutions to prepare EU citizens for disaster situations. This topic addresses safety and resilience as priorities of EU policy. To achieve this goal, we process past disaster events and create various Gamified services that are available to interested participants in an interactive and playful form. This solution uses Brichly populated community knowledge-sharing portal and offers products available on multiple platforms: web, mobile, Augmented Reality (AR), Virtual Reality (VR). The applied approach is the theory of psychological vaccination, also known as stress vaccination training, a promising proactive tool used to prepare the population for disasters, and focuses on the preliminary strengthening of resilience and coping skills. The content packages available in each product are parts of a model used in a dedicated solution development process and for preparing all actors facing future hazards and disaster situations, which also occur during industrial production and manufacturing. The experiences gained during the completion of the project and the implemented B-prepared Ecosystem comprise a strong and reliable resource for this approach.
Nowadays, within the framework of Industry 4.0, devices that consume little energy and are capable of taking over the data collection and measurement tasks of PLCs are increasingly being integrated into manufacturing and logistics processes. The emergence of IoT devices enables a level of data collection that allows more accurate forecasting and planning in the field of logistics through data processing and analysis. However, these devices are typically powered either by the mains or by dry cells/batteries. The cost of installing a mains power supply is high, while replacing or charging power sources integrated into local devices can be problematic. Advances in electronics are also driving IoT devices towards ever lower energy consumption, therefore energy harvesting solutions may be a realistic option for recharging batteries. In this publication, we define the conditions for the application of these solutions, taking into account the characteristics of manufacturing and logistics processes. Furthermore, we illustrate the potential of their introduction through a fictional example.
Phthalates, such as di-n-octyl phthalate (DnOP), are micropollutants released from microplastics, entering the environment mainly through biowaste collected and stored in plastic bags. When compost becomes contaminated, these compounds may be transferred into agricultural systems, potentially reducing crop yield and quality as well posing risks to food safety. This study examined DnOP leaching from PVC under different conditions and its effects on white mustard (Sinapis alba) in a microcosm experiment with the aim of evaluating agricultural risks. Leaching experiments showed that DnOP release was most pronounced in water, reaching 60.43 & micro;g/L by Month 6. In contrast, release into soil was minimal with only 14.41 & micro;g/L detected, suggesting stronger retention. The 'Simple' samples displayed consistent and significant increases over time with concentrations of up to 45.25 & micro;g/L measured. Plant experiments revealed that DnOP inhibited germination and early growth in a concentration-dependent manner. By Day 12, control plants grew to a height of 5.5 cm, while those exposed to 200 mg/L DnOP were only 3 cm tall. By Day 30, plants that were exposed were shorter due to a reduction in growth rate and visible stress, although flowering was observed, indicating an altered growth strategy under chemical pressure. Importantly, no distinct morphological abnormalities were detected, even at the highest concentrations. Chemical analysis confirmed a dose-dependent accumulation of DnOP in plant tissues. Uptake was particularly significant between 100 and 150 mg/L, resulting in a 13.8% increase, while the rise between 150 and 200 mg/L was smaller (+6.1%). Plants exposed to higher concentrations contained nearly double the DnOP levels of their control counterparts, highlighting the significance of the growth medium and high exposure scenarios. The findings demonstrate that substantial amounts of DnOP can accumulate in white mustard. Further studies are needed to clarify its impact on plant nutrient metabolism, particularly sulfur-related compounds, as yield, nutritional quality and agricultural sustainability might be under threat.
This study investigates the torsion modulus of elasticity in magnetorheological elastomers (MREs) composed of silicone rubber matrices and stabilized iron nanoparticle fillers. Isotropic and anisotropic samples were prepared using different filler pre-treatment methods and magnetic field orientations. A measurement system for testing the torsion modulus of elasticity for isotropic and anisotropic samples was developed. The results show that both particle dispersion and alignment significantly affect the mechanical properties. In particular, the torsion modulus increased in the presence of a magnetic field with transverse anisotropic samples exhibiting the most pronounced response. These findings highlight the potential of MREs for applications in adaptive mechanical systems such as tunable vibration dampers, soft robotic actuators or controllable stiffness components where mechanical properties can be dynamically adjusted using an external magnetic field.
