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This article is dedicated to exploring the potential enhancement of mechanical properties, such as hardness and tensile strength, in selected Al-Si alloys (AlSi7Mg0.3, AlSi7Cu4, and Al-Si10.5Cu1.2Mn0.8Ni1.2). High-melting-point elements, such as chromium and molybdenum, are rarely utilized as additives in Al-Si alloys. However, the article demonstrates the feasibility of improving the mechanical properties of these alloys through the addition of high-melting-point elements. High-melting-point metals, often referred to as refractory metals, typically have melting points above 2000 degrees Celsius. Common refractory metals include tungsten, molybdenum, tantalum, niobium, rhenium, and others. These metals exhibit excellent mechanical properties at elevated temperatures and often possess high density and good corrosion resistance. All casts were made using by gravity casting with different heat treatment conditions at 740 °C. The microstructures, hardness, microhard-ness and tensile strenght of the samples were analyzed. Hardness measurements were conducted using two types of hardness testers according to ČSN EN ISO 6506-1 for the Brinell hardness test method and ČSN EN ISO 6507-1 for the Vickers hardness test method. A static tensile test was performed on a universal testing machine, Inspekt 100, in accordance with the standard ČSN EN ISO 6892-1. The measured data demonstrated that high-melting-point metals affect each alloy differently. In some alloys, mechanical properties improved after heat treatment, while in others, a significant deterioration was observed, particularly in tensile strength.

This study examines the influence of FDM printing parameters on replica parts for an educational robotics kit, targeting functional compatibility without post-processing. A VEX Robotics 2×12 Beam (228‑2500‑026) was used as the reference part. Reference dimensions were obtained as mean values from 10 original VEX IQ parts. Replicas were printed from PLA and PETG on Original Prusa MK4 printers using four infill patterns and six infill densities (15–70%). For each material–pattern–density combination, 10 parts were produced, resulting in 480 printed samples. Width, length, and height were measured with a Mitutoyo MiSTAR 555 CNC CMM in accordance with ISO 10360-2. Results are expressed as mean deviations from reference dimensions, standard deviations, and expanded uncertainty of the mean. Maximum deviations reached 0.062, 0.092, and 0.032 mm for PLA, and 0.046, 0.090, and 0.028 mm for PETG. The results provide guidance for selecting non-solid infill settings that reduce material use and printing time while maintaining dimensional compatibility

Investigation of cutting forces in metal cutting is of great importance for defining the effectiveness of the production as well as its impact on product quality. Several researchers studied the effect of cutting parameters on the cutting forces through statistical analysis; however, very few studies use the normalization of the data. Normalization reduces the skewness in the data and increases the accuracy of the results, which can be beneficial in modern industry where AI is being integrated with manufacturing. This research aimed to study the statistical analysis of cutting parameters and cutting forces using log-normalization and compare the accuracy of results with absolute data. The study uses a three-axis piezoelectric dynamometer to measure the cutting forces in the turning of X5CrNi18-10 steel. The results suggested that feed influences the cutting forces during machining. Coolant helps to reduce the cutting forces during the turning of hard steel. Log-normalization increases the accuracy of the results. These results can be used to predict cutting forces during the turning of chromium-nickel alloy steel.

Vehicle Routing Problem is a common problem in logistics, which can simulate in-plant and out-plant material handling. In the article, we demonstrate a Vehicle Routing Problem, which contains period, time window and multiple depots. In this case, customers must be served from several depots. The position of the nodes (depots and customers), the demand and time window of the customers are known in advance. The number and capacity constraint of vehicles are predefined. The vehicles leave from one depot, visit some customers and then return to the depot. The above-described vehicle routing is solved with construction algorithms and Ant Colony algorithms. The Ant Colony algorithms are used to improve random solutions and solutions generated with construction algorithms. According to the test results the Elitist Strategy Ant System and the Rank-Based Version of Ant System algorithms gave the best solutions.

This work aims at the description of the degradation of the intake and exhaust valve of the combus-tion engine during operation. For the experiments, new valves from Ford Fiesta were used, as well as the used ones with known mileage. In order to simulate the valve long-term operation and to esti-mate the temperature of the valve during the use, the new valves were annealed at 700 – 900 °C for 1000 h (corresponding to the mileage of 70,000 km with average speed of 70 km/h) and the grain size was compared with the used valves. It was found that this model experiment can easily predict the operation temperatures of the exhaust valves, which were made of the heat-resistant austenitic steel. However, the metallography procedure for the determination of the grain size fails in the case of the martensitic steel valves.

Cast iron with nodular graphite is one of the most important structural materials that exhibit really good mechanical properties already in the as-cast condition. Nowadays, cast iron with nodular graphite is used in many areas of the manufacturing industry, the most widespread being in the engineering and automotive industries. The applicability of this material for construction purposes is mainly due to its mechanical properties, which are close to those of steel, but the production cost of cast iron is lower. This experiment was aimed at optimizing the production of ductile iron castings in the casting pits so that the foundry could produce ductile iron castings in the casting pits. Therefore, the optimization of the moulding compound database material was carried out in numerical simulation and at the same time, the heat transfer measurement of the foundry sand mould was carried out.

This article explores the significance of microtexturing on cutting tools for improved tribological performance and reduced friction in machining operations. Drawing inspiration from biomimetic structures, the study focuses on laser surface microtexturing and evaluates its impact on cutting forces and tool wear. Experiments involve microtextures of dots with a specific emphasis on a fiber laser-processed pattern. While long-term tests reveal the formation of negative protrusions on the textured tools, reduced variability in cutting forces suggests potential benefits for stable machining processes and increased tool longevity. The findings underscore the intricate relationship between microtexturing patterns and tool performance, offering insights into the broader implications for energy-efficient machining.

Methanol, a type of alcohol, with gasoline, a conventional fossil fuel used in internal combustion engines. This blending process is often done to create an alternative fuel that may have certain advantages over using gasoline alone. The combination of methanol and gasoline can offer benefits such as improved combustion efficiency, reduced emissions, and potentially lower overall fuel costs. Methanol has a high-octane rating, which can enhance the combustion characteristics of the fuel mixture. This can lead to more efficient and cleaner combustion in internal combustion engines. Conducting this research is essential to explore potential improvements in fuel efficiency, emission reduction, and overall system performance, which are critical for advancing sustainable energy solutions. The tests were done using a mobile generator Briggs and Stratton ProMax 3500A. The tested fuels were 10 %, 20 % and 50 % blends of methanol in gasoline. The electrical output of the generator was roughly the same for all fuels even at higher load, however consumption increased significantly. The mixtures had a negative effect on the stability of engine operation and engine emissions had a negative effect at most of the measurement points. In some cases, like the concentration of formaldehyde by weight, gasoline fuel mixtures showed a decrease in mass concentration at lower engine loads and an increase at higher loads compared to the reference fuel.

The topic of this paper is the application of machine learning and neural networks in engineering, specifically in the prediction of the lifetime of bearings operating in different conditions. In addition, the basics of machine learning are introduced, giving an idea of the importance of input data quality for model training. It also presents the elements of neural network training to be used in other projects. The article is supplemented by a source code examples written using only the Python language, and some other popular libraries, like the NumPy, Matplotlib, Tensorflow, Keras, and Scikit-learn. The main advantage of the libraries used is that they are freely available and widely used, bringing variety of sophisticated tools for gen-eral use.

The given paper deals with the defects propagation in car tires for passenger vehicles under dynamic loading. The occurrence of defects has the significant influence on the lifetime and quality of the tire, especially during its operation as a part of the vehicle. The given defects are closely connected with a safety in road traffic. The aim of the study was to carry out a non-destructive analysis of the car tire for the purpose to analyze the defects propagation as well as to introduce the defects classification and their location along with the whole course of rupture as a result of increasing speed, loading and the number of hours or kilometers driven. During the analysis, we used a non-destructive method for detecting defects using a non-destructive analyzer that works on the principle of shearography. The experimental measurement was carried out for 12 car tires. The measurement results are displayed from the non-destructive analyzer in the form of protocols from measurement and video display. The evaluation of the results of the measurement for the propagation of defects is displayed graphically. In relation to the tire casing, the analysis of the defects propagation can help design engineers to solve critical issues by choosing the right material, modifying dimensions of individual components or even by redesigning the overall construction of the tire casing and thus to increase the safety from the as-pect of vehicle operation.

The article is focused on the investigation of the impact of the implementation of mechanization into the welding workplace, for the production of cylinders from austenitic X5CrNi18 10 chromium nickel steel. The welds are assembled into a production line for the processing of puff pastry. In addition to the technical improvement of the process and the verification of the sufficient quality of the welds, calculations were used to prove that after the implementation of the change, there was a significant reduction in the production time. By introducing a higher level of mechanization and necessary technological changes, the production time was reduced by up to half, compared with the original technological procedure, including an increase in quality parameters and it led to a reduction in the production costs of the welding workplace. A significant consequence of the proposed change was connected with its impact on workplace safety.

Polymers and their surface modifications are the subject of intensive research due to their wide industrial applications in the fields of food packaging, biomedicine and electronics. The most widely used polymer films include polyethylene (PE), polyethylene terephthalate (PET) and polypropylene (PP), whose surface and mechanical properties can be optimized through physicochemical modifications. Diffuse coplanar surface barrier discharge (DCSBD) represents an effective method for modifying the surface properties of polymers without significantly affecting their structural properties. This study focuses on analyzing the effect of DCSBD plasma discharge on the mechanical behavior of PE, PET and PP films by means of dynamic mechanical analysis (DMA). Experimental samples were exposed to DCSBD plasma discharge and subsequently subjected to DMA over a wide temperature range. The measurement results showed significant changes in the storage modulus (E′), loss modulus (E′′) and loss angle (tan δ), while a decrease in material stiffness and a shift in glass transition temperatures (Tg) were identified.

The effective application of additively manufactured materials requires accurate identification of their mechanical properties as well as damage mechanisms. Computer vision offers a novel approach for non-contact measurements, enabling the identification of selected mechanical properties. This paper presents a new method based on image analysis and the detection of circular markers for non-contact displacement measurements. The core principle involves detecting the centers of gravity of the circular markers formed on the sample under investigation. The centers of gravity are evaluated on each image created during the tensile test, representing nodal points. At these points, displacements are determined based on the non-contact extensometer. The deformations sought are a function of the displacements at each nodal point. These values were calculated based on several theoretical models, also used in the finite element analysis. The paper describes the computational procedure for determining the deformations based on the mentioned theoretical models. Subsequently, the total strain field is determined using linear interpolation of the displacement values at the individual nodal points. The results provided by each of the theoretical models were compared.

It is well known that the porosity of a product can have a negative effect on the mechanical properties of the product. For this reason, its control is very important. Porosity can be assessed by two methods - destructive and nondestructive inspection. However, the identification of very small pores is still very difficult for metallic materials, as the pore size may be below the resolution of most commonly used NDT techniques. In addition, different types of pores may be present in a single part, with one type usually dominating. Proper identification of porosity is essential to estimate the impact on structural properties. For pore assessment, as for other defects, the description of the morphology, distribution and frequency is important. This article deals with the comparison of methods designed to determine the porosity of products that have been manufactured using Laser Directed Energy Deposition – L-DED additive process. The samples were made from AISI 316L stainless steel. The porosity of these samples was assessed using destructive and nondestructive methods. Subsequently, their comparison was made in relation to the detection of different pore sizes. The samples were subsequently subjected to the HIP process (Hot Isostatic Pressing). For these samples, the changes that occurred in the material as a result of this process were subsequently quantified. This process should have a positive effect on improving the quality of the product produced by AM technologies, e.g. by reducing the number and size of pores.

This study proposes a multi-stage intelligent diagnostic approach integrating Physics-Guided Normalization (LPGN), enhanced Transformer networks, and Gaussian Mixture Models (GMM) for thermal fault detection in turbine generator stators. The methodology sequentially performs the following steps: (1) enhances localized anomaly features in temperature data through LPGN, (2) efficiently extracts temporal patterns via the optimized Transformer architecture, and (3) achieves unsupervised fault classification using GMM. Experimental results demonstrate the proposed method's superiority over conventional ARIMA and LSTM models across multiple evaluation metrics, exhibiting a lower RMSE and a higher detection accuracy. Ablation studies further validate the individual contributions of each component to performance improvement. This solution provides an efficient and reliable framework for intelligent thermal monitoring in large rotating electrical machinery.

This paper aims to study the efficiency of using prefabricated layers made from plain cementitious composite materials for enhancing the flexural behavior of reinforced concrete (RC) continuous beams. The strengthening system was applied at 20 mm thickness, 150 mm width, and adequate development length. The prefabricated layers were placed in the tension cover in the positive and negative zones. All beams have the same geometric dimensions and positive and negative steel reinforcement ratios. The results showed that the prefabricated layer was deformed with the RC specimen without debonding, which enhanced the cracking patterns and distributed the crack width. A slight improvement in the strengthened beam capacity was 7% for the yielding load and 6% for the ultimate load. The energy absorption capacity of the strengthened beam decreased by 30.67%, whereas both beams achieved the same ductility index.

Dynamic mechanical analysis (DMA) is an important method for evaluating the viscoelastic properties of polymeric materials, especially when investigating their mechanical response to various manufacturing parameters and surface treatments. In recent years, DMA analysis has been intensively used, among others, for the analysis of polylactide (PLA) produced by the fused filament fabrication (FFF) additive technology. The present study focuses on the effect of DCSBD plasma treatment on the dynamic-mechanical properties of PLA samples with different infill geometries (Line, Rectilinear and Concentric). In the study, experimental PLA samples were subjected to DMA analysis in the temperature range of 40 °C to 90 °C in order to analyze the changes in their viscoelastic properties after plasma discharge surface treatment. The results showed a decrease in the glass transition temperature (Tg) for all tested samples, while the extent of the decrease depended on the infill geometry used. The most significant changes were observed in samples with Rectilinear infill, which showed the best mechanical stability after plasma treatment. The study shows that plasma treatment can influence the mechanical properties of PLA products, opening new possibilities for optimizing their processing, reuse and application in technical areas requiring controlled mechanical response.

The development and characterizing of thin layers of AgCu, Cu, Ti, and Zr on nylon filters using PVD magnetron sputtering technology was conducted. The evaluation of these thin layers was mainly focused on characterizing specific parameters that may influence the expected functionality of the modified filter materials. The surface treatment of nylon filters with thin layers does not significantly affect the mechanical properties of the original nylon material. Thin layers deposited at a power of 0.9 kW exhibited greater thickness and lower static friction coefficient values than the layers deposited at 0.4 kW, except for a thin layer of the element titanium. The surface modification of the filters did not significantly change resistance to deformation and had no significant reduction in pore size. However, a significant effect on surface wettability (increased hydrophobicity) was demonstrated.

The paper presents work aimed at building practical applications of virtual reality (VR) in manufacturing environments. It contains studies of the optical properties of cameras and lenses aimed at the selection of an optimal set (camera, adapter, lens) for the realization of recordings and video transmissions in stereoscopic format for VR. In response to the increasing trend in the number of applications of VR systems in the industry, works have been initiated with the purpose of building a system levelling image noise identified thus far as an obstacle to the effective utilization of VR in production systems. It was considered that picture error correction can significantly increase an already big data stream from the recordings. Based on it, a set of parameter values was defined which determined the selection of study equipment. Three research areas were set: the verification of the optical correctness, the study of image defects and their correction and the determination of the maximum optical resolution and the achievable image parameters in various lighting and environmental conditions. An example was presented for the application of a projected system for the monitoring of undesirable events/movement at work stands and key areas of production halls as well as training in the high-risk production zones.

The article deals with sinterhardening process of lean Cr-Mo prealloyed steel for moderately loaded applications. New material Astaloy CrS with low alloying volume of chromium and molybdenum was analyzed as possible basis for sinterhardening process. Standard mechanical properties of frequently used and more expensive materials such as DistaloyDH and Astaloy CrM are chosen as a compara-tive criterion. Astaloy CrS+0.85%C samples with different compaction densities and Ni content were studied, mechanical properties and hardness after sinterhardening process were compared. The influ-ence of additional high-temperature sintering on mechanical properties was assessed. The micro-structure of the sinterhardening (SH) and high-temperature sintering + sinterhardening (HTS+SH) samples was studied quantitative analysis of the phase was given. As result, tensile strength greater than 900 MPa and hardness greater than 33 HRC can be obtained for investigated material.

Today, considerable attention is paid to the production of solid castings (approx. 2000 kg) from cast iron with spheroidal graphite. The metallurgical preparation of large quantities of melt is very difficult. This difficulty is related not only to the melting and preparation of large quantities of melt, but above all to its metallurgical treatment - inoculation and modification. Melt modification ensures the production of cast iron with spheroidal graphite. Material castings, such as machine tool components, cannot be destroyed to determine the quality of the cast iron produced. Therefore, this paper outlines a methodology to proceed in determining the quality of manufactured castings. It is possible to observe the chemical composition of cast iron, thermal analysis of cast iron using liquidus temperature value, subcooling temperature, eutectic recalescence, primary solidification recalescence, eutectic solidification time. Furthermore, to observe the mechanical values of cast iron (yield strength, ultimate strength and ductility) on fabricated bars of overmolded Y blocks or to observe the micro-structure of cast iron on microscope.

Core-shell powders have been extensively studied due to their complex structure and wide range of applications. W@Ag core-shell powders are particularly interesting due to the synergy between the tungsten and silver, which can be beneficial in the electronics industry. However, knowledge of their thermal stability is limited, particularly concerning the impact of annealing temperatures on structural integrity and oxidation resistance. In this work, W@Ag core-shell powder was heat-treated in the temperature range 100–700 °C for 1 h in air. Investigation of the microstructural changes using scanning electron microscopy equipped with energy-dispersive X-ray spectroscopy showed that the limiting temperature is 500 °C, when the shell began to decompose and the core began to oxidize. Moreover, X-ray diffraction analysis determined that the phase composition of the thus heat-treated material consisted of approxi-mately 50 % Ag and 50 % Ag2WO4.

The present paper discusses important aspects of residual stress measurements in forged shafts with defects using the X-ray method. A random population of shafts was selected for the study, for which, depending on the type of rolling process, turning was performed, measuring stress changes after successive machining passes. In the forged shafts studied, the existence and location of internal defects were identified using computed tomography. The impact of internal defects on the stress distribution on the surface of the machined workpiece was observed. It was observed that the use of the X-ray method to measure residual stresses makes it possible to determine the state of stresses and their distribution, which is crucial for the safety and durability of shaft-type parts, and allows the impact of a defect on the distribution of residual stresses to be identified. On the basis of the results obtained, it was observed that there is a correlation between the occurrence of internal defects in forged shafts and the distribution of residual stresses in characteristic sections along the length of the shaft after machining

The article describes the creation of a mathematical 3D model of the original hovercraft structure, which will be further used for research into modifying the shapes and materials of the structure to ensure better driving conditions. Proposals for new materials for individual parts of the hovercraft structure will be addressed in order to reduce the weight of the hovercraft and thereby ensure a higher possible speed of movement, reduce fuel consumption and ensure the necessary mechanical properties of individual segments. The mathematical model of the simplified hovercraft model was created in the Cradle and Adams simulation programs. The paper is presented by analyzing the hovercraft properties in order to obtain sets of advantages and disadvantages of the hovercraft. The following is a description of the creation of a geometric 3D model of the hovercraft, which is built using Autodesk Inventor. The article further describes the transformation of the 3D model into a simulation model that can be used for co-simulation of movement in the Adams and Cradle computer simulation systems. The simulations will be the first step towards modifying the structure of a real rescue UAV prototype with improved maneuverability, stability and the ability to traverse terrain with surfaces unsuitable for hovering.

This study centers on 7B50 aluminium alloy. The intention is to reduce the pre-treatment and post-treatment times of the shot peening model. By comparing and analyzing different process parameters, the best combination of peening solutions can be obtained. The pre-processing is implemented through a GUI interactive interface. Post-processing is carried out by using Python for secondary development in ABAQUS. Orthogonal test method is employed for post-processing analysis of shot peening simulations under various process conditions. The results are evaluated by using a weighted composite scoring method to determine the depth of the residual compressive stress layer on the workpiece surface, the surface residual compressive stress, and the extreme deviation of the maximum residual compressive stress value after shot peening. The combined influence degree of shot peening process parameters such as impact speed, projectile diameter and impact angle is determined. The optimal combination of shot peening process parameters is analyzed and verified through simulation.

The paper focuses on the application of machine learning techniques and optimization algorithms in predictions and controls of grinding temperature variations. The major thrust of investigation has been on how the different input conditions such as feed, depth of cut, and cooling conditions influence grinding temperatures and the effectiveness of these conditions on the control of their thermal effects. Three machine learning models: Random Forest (RF), Gradient Boosting (GB), and Artificial Neural Networks (ANN) were then used to develop prediction models for the grinding temperature on both face and shoulder of the workpiece. Out of all the models, RF achieved a much higher R² score of 0.96 as compared to both GB and ANN, indicating its greater predictive performance. Furthermore, Bayesian optimization and genetic algorithms were employed in model optimization and grind parameters and cooling condition optimization to avoid damages caused due to temperature. MQL has been found to be highly superior to the inefficient dry cooling methods in terms of achieving lower grinding temperatures and, therefore, seems to be most suited as an eco-friendly yet practical cooling solution as based on this comparison. Altogether, these research findings indicate that AI-based techniques and traditional optimization methods can lead to much better grinding in terms of efficiency and energy consumption, as well as surface quality, and assist towards greener manufacturing altogether.

Robotics plays a key role in industry and its use continues to grow. Robots are used in many industries to increase efficiency, productivity, and safety of work processes. This manuscript focuses on the spatial calibration of collaborative robot arms using appropriate statistical tools. Nowadays, there are many special programming languages, simulations or virtual realities (VR), which in most cases perform calibration using matrix relations. The mathematical-statistical solution is not solved very often, and the use of linear relationships is valid only in certain parts of the workspace of the collaborative robot. The purpose of this article is to demonstrate how to find a suitable statistical method that would respect the wear of the arm mechanism in predefined positions based on the requirements of ISO 230-2:2015. Based on these measurements, it is possible to assume that optimal solutions can be obtained using a polynomial regression function. This optimization method will be searched using the Newton and Markwartel methods.

This research focused on the production of brake pads reinforced with bronze fibers to see the anticipated performance principles for braking systems. Three unique amalgamated formulations, labeled BRZ-I, BRZ-II, and BRZ-III, were set by varying the bronze fiber content to 5%, 10%, and 15% by weight. The tribological characteristics of these composites were systematically evaluated to determine their effectiveness. Traditional manufacturing processes were used in developing the brake pad. Various properties such as physical, chemical, mechanical and tribological possessions were assessed by means of chase test rig. Worn-superficial examination stayed carried out by using chase test rig. Base results it was evident that the 10 weight percentages of the bronze fibers showed better physical, chemical, mechanical and tribological properties. Chase test results confirmed that the composite brake pad developed with 10 weight percentages of bronze showed better results at higher pressure-speed conditions than others due to better plateau formation and less wear rate. The results obtained after performing various performances such as physical, chemical, mechanical and tribological properties concluded that the bronze fiber possessed lesser wear and stable coefficient of friction.

An innovative way of using DCPD (Direct Current Potential Drop) method for off-line and online monitoring of critical parts of energy equipment in operation is presented. There are only a few NDT methods that allow detection and monitoring of defect growth in components at high temperatures and pressures. Monitoring of steam pipes and critical pipeline components in operation has been carried out for several years with different results. a relatively new way of using the DCPD method outside the laboratory is described. The carried-out tests were intended to resemble operational loads as much as possible. Therefore, the tests were performed at a temperature of 20 °C and at an increased temperature of 550 °C. By gradually deepening the groove (slot) simulating the crack type defect in predefined steps, the growth of the defect was simulated up to the full wall thickness of the test sample. The primary evaluation was carried out from the absolute and relative values of measured resistance. The disadvantage of these values is their dependence on the temperature of the monitored area of the test sample and on possibly interfering DC voltages.

The study focuses on an evaluation of mechanical properties of the H13 tool steel manufactured by the material extrusion and further comparison with conventionally produced material. Notably, for achieving sufficient surface quality of functional parts further post-processing is required. Thus, a comprehensive investigation, encompassing hardness, ultimate tensile strength (UTS) and yield strength (YS) measurement, microstructure, and machinability was performed. The material extrusion, an increasingly utilized additive manufacturing (AM) technique, offers a viable alternative to the prevalent laser powder bed fusion (LPBF) methods. This method enables a creation of complex geometries using various materials. The investigation revealed that the horizontal orientation of parts yielded the highest mechanical properties, reaching the ultimate tensile strength of approximately 1200 MPa. Additionally, the material exhibited the hardness of 47 HRC in the as-built state. The conventionally produced steel resulted in the higher UTS and YS in comparison to the AM material. The machinability of the as-built material in regard to cutting forces and surface roughness was also evaluated Lower surface roughness was achieved by decreasing feed per tooth. Optically measure material porosity was 6.13 % with maximum pore size 7.43 µm. The primary objective of this research is to optimize the mechanical properties of H13 tool steel post-printing, with a broader aim to apply the gained insights to improve other materials produced by the material extrusion.