Machining Parameters Research Articles

Assessing Energy Efficiency in Desktop-Size FFF 3D Printers

This study investigates the energy consumption characteristics of desktop-sized Fused Filament Fabrication (FFF) 3D printers, focusing on variations in energy usage and operational efficiency across different models. The results demonstrate a correlation between machine parameters and energy efficiency during the processing of PLA material. Notably, machines capable of producing identical components in shorter durations tend to consume more electricity, despite utilizing a lower volume of thermoplastic material. Furthermore, the research underscores the substantial impact of specific technological factors, such as the use of rafts, on energy consumption assessments. Relying solely on estimated manufacturing time and material volume to determine manufacturing costs may yield imprecise results, especially amid rising electricity prices. This study emphasizes the need for standardized energy metrics to enable more informed comparisons for consumers.

Parametric Optimisation of Wire Electrical Discharge Machining of 15CDV6 HSLA Steel Using Taguchi Method

15CDV6 steel is a high strength bainitic High Strength Low Alloy (HSLA) steel with low concentrations of chromium, molybdenum, and vanadium as the main alloying constituents. This steel is very robust and well-tempered i.e., it has High strength and good toughness. It is used in the manufacture of transformers, electric motors, nuclear reactors, and its application to the aerospace industry for the manufacture of rocket motor cases is immense. Simplifying any process of machining is very difficult, since it basically includes forecasts of machining parameters and working requirements that are unpredictable and extremely non-linear in nature which influence the overall production Quality and costs. A key component of producing novel products, particularly for the automotive and aerospace sectors, is wire electro discharge machining (Wire-EDM). This technology got success and offers unique advantages for specific applications, such as working with hard, super-tough, brittle, or difficult-to-machine materials, producing intricate shapes, or achieving high precision. The high degree of the obtainable accuracy and the fine surface quality makes Wire-EDM valuable. The right selection of the process parameters or input variables is the considered as most important task in Wire Electro Discharge Machining of 15CDV6 HSLA steel material. The present work concentrates on optimisation of various process parameters are Pulse-on Time, Pulse-off Time, Water pressure and sensitivity on work material 15CDV6 HSLA steel, the output responses considered are Material Removal Rate (MRR), Surface Roughness (Ra), and Power Consumption (PC). Taguchi Method technique used on experimental results for optimisation and same results are analysed using Analysis of Variance (ANOVA) for identify percentage contribution of process parameters. This work helps in analysing the suitable process parameters and machining performance for machining of 15CDV6 steel and also reveals the research work area in which maximum research work can be done in future.

Investigation on the effect of probe stiffness on surface roughness in single crystal copper nanomachining process

Surface roughness is one of the most important features in nanoscale machining. In this paper, molecular dynamics simulations have been performed to investigate the combination effect of the tool stiffness, machining parameters and the tool tip geometry on the surface roughness of the single-crystal copper workpiece in the nano machining process by diamond tool. To consider the effects of probe beam dynamics in calculating the workpiece surface roughness, the probe cantilever is modeled as an equivalent spring-mass system that the tip mass is added to its end. The differential equation of the probe motion is solved simultaneously with the molecular dynamics equations for the tool tip and the workpiece and the effect of probe stiffness on the surface roughness is obtained. The result shows that a higher cutting speed and depth of cut and lower tool radius lead to a larger effect of probe stiffness on the surface roughness of the workpiece. Also, for the negative rake angles, considering the tool as rigid creates a large error in determining the surface roughness.

Squirrel search algorithm-support vector machine: Assessing civil engineering budgeting course using an SSA-optimized SVM model

Abstract In the field of civil engineering education, accurately evaluating the effectiveness of budget courses is crucial. However, traditional methods of evaluation tend to be cumbersome and subjective. In recent years, machine learning technology has demonstrated immense potential in educational evaluation. Nevertheless, in practical application, the machine learning-based evaluation model for civil engineering budget courses faces the predicament of inadequate evaluation accuracy. To solve this problem, the squirrel search algorithm technology was used to establish support vector machine parameters and create optimization algorithms. The performance of the proposed optimization algorithm was tested, and the results showed that the accuracy of the proposed algorithm was 0.927, which was better than similar prediction algorithms. Then, the empirical analysis of the proposed civil engineering budget course evaluation model showed that student satisfaction and student examination scores had increased to 92 and 94 points, respectively. The above results reveal that the proposed optimization algorithm and course evaluation model have good performance. Therefore, the implementation of the proposed curriculum evaluation method can significantly improve the learning efficiency of students and the teaching quality of civil engineering budgeting methods courses.

A novel recast layer in situ thinning method for film cooling holes in fast EDM drilling

As the main machining method of film cooling holes, fast EDM (electrical discharge machining) drilling has the defect that the thickness of a recast layer is difficult to control, which can severely affect the service life of a turbine blade on the aero engine parts. In order to reduce the thickness of a recast layer in fast EDM drilling while ensuring machining precision and efficiency, a novel recast layer in situ thinning method, which uses the precision-controlled discharge erosion and tiny energy layer-by-layer thinning strategy, is proposed. Moreover, the recast layer removal mechanism, as well as the influence of this method on fast EDM drilling are systemically investigated, thereby, the optimal combination of machining parameters with different electrode diameters of the in situ thinning method is obtained. Machining results show that with the proposed method, the maximum and average thickness of the recast layer can be well controlled within 2.5 μm and 2 μm, with the highest reduction rate of the recast layer reaching 85.39%, which greatly improves the machining quality of film cooling holes of a turbine blade.

Comparative Study of Power Tiller Operated Machines for Small Holders Farming in Conservation Agriculture

Timeliness is very crucial in all farm operations in general and in seeding and planting in particular. Slightly delay in sowing operation may lead to tremendous loss in production, productivity and sometimes quality of the grains followed by plant health. Power tiller operated machineries play very vital role for small scale farming system as the average holding size is close to one hectare and more than 86% of India’s farming community are small and marginal farmers. Practice of conservation agriculture with natural resource management has made the use of power tiller or hand tractors very essential for small holders farming. The power tiller with matching implements is cost effective source of farm power and it also enables farmers to diversify cropping pattern and increase in yield of crops. Present efforts were carried out to study the performance of various types of power tiller operated machines for different farm operations in conservation agriculture starting from seedbed preparation to crop harvesting for various crops like wheat, paddy, maize, gram, soybean, pigeon pea, sunflower, groundnut etc. Comparative field performance parameters such as field capacity, field efficiency, operating cost etc. and machine parameters have been presented in this paper.

Developing precision in WEDM machining of Mg-SiC nanocomposites using machine learning algorithms

Abstract Wire electrical discharge machining (WEDM) is one of the most commonly used non-conventional machining processes in the aerospace, nuclear, and precision industries. This technology possesses numerous advantages over traditional machining processes due to its superior properties, such as high precision of machined surfaces, ease of machining of complex shapes, and hard material processing. This study focuses on investigating the performance characteristics of Mg-SiC nanocomposite through experimental analysis using WEDM, with surface roughness as the key evaluation parameter. Employing a fractional factorial design, twenty-five experimental datasets were generated to explore the impact of WEDM machining parameters, including Pulse on time (Ton), Pulse off time (Toff), Servo voltage (SV), and Peak current (Ip), on surface roughness. Leveraging a machine learning approach, specifically, Support Vector Regression (SVR) integrated with Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), an integrated predictive surface roughness model for machined substrates was developed. The predicted results exhibited a high level of agreement with experimental data, boasting a coefficient of determination (R2) value of 0.866 and a mean square error (MSE) of 0.364. A novel aspect of this work lies in integrating GA-PSO with SVR to obtain optimized surface roughness values. Through this methodology, SVR-GA and SVR-PSO achieved optimum surface roughness values of 0.187 μm and 0.132 μm, respectively, with SVR-PSO demonstrating superior performance by outperforming SVR-GA after 360 and 428 iterations, respectively. Thus, this study presents a novel and effective approach to optimizing surface roughness values in WEDM processes.

Surface Characteristics and Crystallographic Texture Evolution of Aviation Aluminum Alloy in High-Speed Machining Based on Visco-Plastic Self-Consistent Model

The purpose is to investigate the surface characteristics and crystallographic texture evolution of aviation aluminum alloy during high-speed machining (HSM). The influence mechanism of machining parameters on the surface quality of Al 7050 is explored through HSM experiments, and the distribution of cutting temperature, equivalent stress and strain, and the variation trend of variables in the deformation zone on the path is analyzed by finite element (FE) simulation. Combining FE simulation and polycrystalline plasticity theory, a visco-plastic self- consistent (VPSC) model is established to simulate the crystal texture evolution in the machined surface layer, and the validity of the constructed model is confirmed by dynamic impact and EBSD results. The results show that the high-speed machined surface displays tool feed marks, apophysis, cavities, side flow bands and adhered chip debris. The overall trend of temperature and equivalent stress on the path shows a “spoon-like” distribution and an “M-like” distribution, respectively. The cutting temperature, equivalent stress and strain increase in relation to three cutting factors, where the cutting temperature and equivalent stress increase by 3% and 2.9%, respectively. VPSC simulations show the textures of {001}[Formula: see text]100[Formula: see text]Cube, {123}[Formula: see text]634[Formula: see text]S, {110}[Formula: see text]112[Formula: see text]Brass, and {112}[Formula: see text]110[Formula: see text]R-Copper which are present on the machined surfaces, and high feed rate and cutting speed enhance and weaken the strength of the {123}[Formula: see text]634[Formula: see text]S and {001}[Formula: see text]100[Formula: see text]Cube textures. The difference in texture maximum density under different machining parameters is 34%, and the type and intensity of crystal texture are profoundly influenced by the coupling effect of heating/force load, stress, and strain.

A novel hole performance index to evaluate the hole geometry and drilling time in the electrochemical drilling process.

Electrochemical Drilling (ECD) is an unconventional method aimed at creating holes in metallic workpieces characterized by high hardness and complex structures. This study analyzes the influence of process variables, including machining voltage, electrolyte concentration, electrode rotational speed, electrolyte flushing pressure, and workpiece material, on the novel hole performance index (HPI) in electrical discharge machining (ECD). The HPI was identified as a suitable metric for simultaneously evaluating hole geometry and drilling time across various machining parameters and workpiece materials. The analysis of variance (ANOVA) method was employed to determine the significance of each machining parameter and workpiece material on the HPI. The research employed signal-to-noise ratio analysis to identify the optimal machining parameters. The findings demonstrated that the workpiece material and machining voltage were significant factors influencing HPI. The validation tests demonstrated that the proposed statistical method can significantly reduce HPI.

Optimization of machining parameters while turning AISI316 stainless steel using response surface methodology

The chromium-nickel alloy known as AISI 316 Stainless Steel is extensively used in various industries, such as the chemical industry, nuclear power plants, and medical devices, due to its exceptional mechanical properties and corrosion resistance. Compared to other stainless steels, AISI 316 resists corrosion from air and other corrosive conditions better. Determining the optimal selection of machining parameters is still a challenge for many researchers. The Box Behnken technique (L12 array) is used in this work to design the experiment set. Response surface methodology (RSM) is used to examine how the cutting velocity (100, 150, and 200 m/min), feed (0.10, 0.15, and 0.20 mm/rev), and depth of cut (02, 0.4, and 0.6 mm) affect the cutting force (Fc), surface roughness (SR), power consumption (Pw), and tool life (T). The ANOVA investigation shows that cutting force and surface roughness increase linearly with an increase in feed. Similarly, power consumption and tool life also increase linearly with an increase in cutting velocity. The best combination for the lowest Fc, SR, and Pw and the maximum T is cutting velocity at 122.37 mm/min, feed at 0.13176 mm/rev, and cut depth at 0.213337 mm. For cutting force, surface roughness, power consumption, and tool life, the actual and predicted values are (124.31, 129.45), (0.55, 0.57), (1.131, 1.154), and (2112, 2225), respectively. For the lowest feasible values of Fc, SR, Pw, and maximum T, the optimal settings are a cutting speed of 122.37 mm/min, feed of 0.13176 mm/rev, and depth of cut of 0.213337 mm.

Impact of Process and Machine Parameters in the Charging Section on the Triboelectric Separation of Wheat Flour in a Vertical Separator

Triboelectric separation has recently been investigated as a novel process for dry enrichment and separation of protein of various crops like wheat flour. The triboelectric effect allows for the separation of starch and protein particles in an electric field based on their different charging behavior despite having a similar density and size distribution. Particles are triboelectrically charged in a charging section before being separated in an electric field based on their polarity. While the charging section is crucial, the influence of process parameters remains largely unexplored. Thus, the influence of the charging sections’ dimensions and the particle concentration as process key parameters was investigated experimentally. Varying the length (0, 105, and 210 mm) showed that the protein shift increases with the length (max. 0.53%) during separation. Varying the diameter (6, 8, and 10 mm) influenced the charging behavior, resulting in an increase in protein accumulation on the negative electrode as the diameter decreased. Varying the mass flow of flour (40, 80, 160, and 320 g·h−1) also affected the separability, leading to a maximum protein shift of 0.61%. Based on the observed results, it is hypothesized that the electrostatic agglomeration behavior of oppositely charged particles is directly affected by alterations in machine parameters. These agglomerates have a charge-to-mass ratio that is too low for separation in the electric field.

Impact of beam asymmetries at the Future Circular Collider e+e−

In this paper, we present detailed simulations with asymmetric initial beam settings in the context of the proposed Future Circular Collider e+e− (FCC-ee) using the framework. We compare simulated equilibrium bunch sizes and luminosities against an already existing analytical model, which shows remarkably good agreement for realistic small perturbations. We investigate the longitudinal top-up injection, the currently preferred injection scheme for the FCC-ee, using self-consistent simulations featuring beam-beam collisions with beamstrahlung and the injection process for the first time. We present and assess the sensitivity and required precision of the nominal beam parameters in a potential real-life operation by providing first estimates of the tolerances in the initial asymmetry of several machine parameters, with respect to the 3D flip-flop mechanism, obtained from parameter scan simulations. Published by the American Physical Society 2024

Sensitivity Analysis of a Stator Current-based MRAS Estimator for Sensorless Induction Motor Drives

The sensitivity of speed estimators for parameter variations presents a significant challenge for sensorless Induction Motor (IM) drives, particularly at very low speeds. This paper examines the impact of parameter variations and the PI adaptation mechanism on the stator current-based Model Reference Adaptive System (MRAS). In contrast to the estimation of rotor flux, the MRAS method uses the observed stator current and the stator current estimate error within the adjustable IM model. The stability analysis for changes in machine parameters and PI controller gains is examined using small-signal perturbation. Additionally, a sensitivity analysis of stator and rotor resistance changes is included. A complete simulation using MATLAB/Simulink and experimental validation using a laboratory prototype based on the DSP-DS1103 are provided. The analytical, modeling, and measurement results reveal that the suggested observer responds well and provides precise speed estimation in all four quadrants of operation.

Improvement of NC Program Quality based on Shape Generation Motions and Feed Drives for Five-Axis CNC Machine Tools

Five-axis Computer Numerical Control (CNC) machine tools, integrated with Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM) systems, are used to machine complex parts and reduce trials and errors. However, these machine tools still rely on Numerical Control (NC) programs and often lack accuracy and precision due to poor quality when implemented in the machine. This research aims to enhance the quality of NC programs for five-axis CNC machine tools by focusing on shape generation motions and a closed-loop feed drive system with Proportional-Integral-Derivative (PID) control. The individual motions were mathematically described using 4x4 transformation matrices, incorporating kinematic motion deviations, end mill geometry, machining parameters, and cutting forces derived from virtual machining. Additionally, a closed-loop feed drive system with PID control was integrated with the new position and angular data of each axis from the shape generation motions model. The new NC programs were validated by machining an S-shaped part and measuring dimensional errors at 64 points before and after using a Coordinate Measuring Machine (CMM). The results indicate a substantial reduction in the standard deviations of form and angular errors within the NC program quality, totaling approximately 80.73%. Reductions are demonstrated in the standard deviations for the X, Y, A, and B axes, with decreases of 76.83%, 95%, 82.40%, and 68.72%, respectively indicating a significant improvement in the overall quality of the NC program.

Prediction and Control of Existing High-Speed Railway Tunnel Deformation Induced by Shield Undercrossing Based on BO-XGboost

The settlement of existing high-speed railway tunnels due to adjacent excavations is a complex phenomenon influenced by multiple factors, making accurate estimation challenging. To address this issue, a prediction model combining extreme gradient boosting (XGBoost) with Bayesian optimization (BO), namely BO-XGBoost, was developed. Its predictive performance was evaluated against conventional models, such as artificial neural networks (ANNs), support vector machines (SVMs), and vanilla XGBoost. The BO-XGBoost model showed superior results, with evaluation metrics of MAE = 0.331, RMSE = 0.595, and R2 = 0.997. In addition, the BO-XGBoost model enhanced interpretability through an accessible analysis of feature importance, identifying volume loss as the most critical factor affecting settlement predictions. Using the prediction model and a particle swarm optimization (PSO) algorithm, a hybrid framework was established to adjust the operational parameters of a shield tunneling machine in the Changsha Metro Line 3 project. This framework facilitates the timely optimization of operational parameters and the implementation of protective measures to mitigate excessive settlement. With this framework’s assistance, the maximum settlements of the existing tunnel in all typical sections were strictly controlled within safety criteria. As a result, the corresponding environmental impact was minimized and resource management was optimized, ensuring construction safety, operational efficiency, and long-term sustainability.

Geological type recognition for shield machine using a semi-supervised variational auto-encoder-based adversarial method

In the process of tunneling, accurate and timely recognition of the geological type is significant to optimize the control parameters of the tunneling machine, improving tunneling efficiency and avoiding accidents. The shield machine operator in shield tunneling machine cannot directly observe the geological environment due to the closed working environment, so the soft method that can indirectly recognize the geological type by the machine parameters has become a research hotspot. However, most current soft methods use only a small amount of labeled data for supervised learning, and large amounts of unlabeled data is wasted. In order to use all data to improve the recognition performance of the classifier, a semi-supervised variational auto-encoder-based adversarial method (VAE-EMGAN) is proposed. Firstly, 50 parameters associated with geological types are selected and pre-processed, then the Variational Auto-Encoder (VAE) is trained by unlabeled data, and the generated part of VAE is added to the structure of Enhanced Multi-Classification Adversarial Generative Network (EMGAN) as a generator. Finally, the recognition accuracy of classifier is improved through adversarial training with labeled data, unlabeled data and generated data. We used data from upper and lower tunnels in Singapore to create two tasks to verify the validity and generalization performance of VEVE-EMGAN. The results show that the proposed model not only achieves high accuracy of all test sets on both tasks, but also has much better generalization performance than other models. Mean accuracy is 10.82%, 17.68%, 11.05%, 17.72%, 17.45%, 12.68% and 5.27% higher than SVM, KNN, RF, XGBoost, MLP, DNN and CNN respectively of test set 2 on task A; Mean accuracy is 13.06%, 12.80%, 7.64%, 18.31%, 8.74%, 7.94% and 4.05% higher than SVM, KNN, RF, XGBoost, MLP, DNN and CNN respectively of test set 2 on task B. In particular, the performance of the adversarial trained classifier is better than that has the same structure but separately trained classifier. Therefore, this method can use unlabeled data for adversarial training to improve the classification accuracy and generalization performance of the classifier, which has important implications for engineering practice.

The mechanical properties of aged PA-12 FS3300PA powder: influence of layer thickness, sintering orientations, and laser power

Abstract This research investigates the influence of layer thickness, laser power, and sintering orientation on the mechanical properties of aged Polyamide-12 (PA-12) FS3300PA using the Selective Laser Sintering (SLS) 3D printing method. Specimens were sintered with three different layer thicknesses, laser powers, and sintering orientations using SLS. The study also aimed to examine the resulting powder morphology, mechanical properties, and tensile fracture behaviors of the aged (more than eight continuously sintering cycles) and virgin FS3300PA powders. The specimens divided into ten groups: nine groups of aged powder and one group of virgin powder as a benchmark. The nine groups of aged powder were sintered with three different layer thicknesses (0.07 mm, 0.12 mm, and 0.15 mm), laser powers (65 W, 70 W, and 75 W), and orientations (YZY 0°, YZY 90°, and XYY 0°). The selections of these laser power and layer thickness values for the sintering setting are due to machine and material parameter limitation. The results from these parameters then compared with those of the virgin powder, which sintered using the parameters provided by the manufacturer, in terms of powder morphology, mechanical properties, and tensile fracture behaviours. Observation made using scanning electron microscope (SEM) revealed that there were not many changes in shape, size, and distribution between the virgin and aged powder, but slightly larger sizes and the presence of cracks found in the aged powder. The tensile strength, elongation at break value, and Young’s modulus all shared a similar trend, increasing with higher laser power but decreasing with increased layer thickness. Regarding the fracture morphologies, the number of pores and dimples decreased with increased laser power but increased with thicker layer thickness. There was also the occurrence of un-molten powder, especially in specimens sintered at the YZY 90° orientation with lower laser power and thicker layer thickness.

Effects of Cutting Parameters on Tool Wear in Milling Inconel 625 Superalloys with a SiAlON Ceramic and the Prediction of Tool Life

Inconel 625 superalloys are widely preferred in various industries because of their superior mechanical properties at high temperatures. The superior properties they exhibit make the machinability of Inconel 625 alloys difficult. This situation can cause rapid wear of the cutting tools, making the cutting tool unusable in very short periods of time and causing deterioration of the workpiece surface quality. Therefore, improving the machining performance of Inconel 625 alloys, optimizing the machining parameters and using the optimum cutting tool material and geometry are important. In this study, SiAlON ceramic cutting tools, which have been widely used in recent years, were preferred. SiAlON tools supplied in raw rod form were subjected to various processes and finalized into final products. The effects of three different cutting speeds (vc), feed rates (f) and axial depth of cut (ap) on tool life (T), wear patterns and mechanisms were investigated with 9 tests designed according to the Taguchi L9 orthogonal array. The results revealed that the f values, the effects of which were analysed, had the most significant effect on T. By performing regression analysis with T values, the coefficients used in the extended Taylor T equation were determined, and T predictions were obtained. The regression model was found to be consistent with the experimental results, with an effect of 99.34%, and the model was significant according to analysis of variance. The predominant wear patterns of the cutting tools were flaking at a low vc, nose collapse at a high vc and an adhered workpiece under all conditions. The wear mechanism was found to be predominantly adhesion and fracture. The presence of Ni, Cr, Mo, Nb and Fe on the cutting tool surface was determined, and the effect of the concentrations was observed to increase/decrease significantly depending on the parameter values examined.

Grey Relationship Analysis of Cutting Forces and Surface Roughness in Turning using Cryo-treated and Untreated Cutting Tools

The aim of this experimental study is to establish the optimum heat treatment procedure and machining parameters to obtain values of cutting force and surface roughness in machining AISI 1050 workpiece by utilizing grey relations analysis based on Taguchi method. In this regard, machining operations were carried out at three different cutting speeds and three different feed rates at a constant depth of cut. Taguchi mixed-level design L18 (2^2 3^2) was chosen for machining experiments. The optimum heat treatment and machining parameters for determination of cutting force and surface roughness values were identified according to the fact that higher the Signal/Noise ratio is better based on grey relations analysis of Taguchi method. The results showed that the optimum parameters for surface roughness and cutting forces were obtained as 0.1 mm/rev at feed rate and 180 m/min at cutting speed with cryo-treated cutting tool. According to ANOVA table and Signal/Noise ratios, it was specified that the most effective parameters on cutting forces and surface roughness were feed rate, cutting speed and heat treatment conditions, respectively.

Effect of Substrate Temperature on Bead Track Geometry of 316L in Directed Energy Deposition: Investigation and Regression Modeling

The optimization of process parameters in powder Directed Energy Deposition (DED) is essential for achieving consistent, high-quality bead geometries, which directly influence the performance and structural integrity of fabricated components. As a subset of additive manufacturing (AM), the DED process, also referred to as laser metal deposition (LMD), enables precise, layer-by-layer material deposition, making it highly suitable for complex geometries and part repair applications. Critical parameters, such as the laser power, feed rate, powder mass flow, and substrate temperature govern the deposition process, impacting the bead height, width, contact angle, and dilution. Inconsistent control over these variables can lead to defects, such as poor bonding, dimensional inaccuracies, and material weaknesses, ultimately compromising the final product. This paper investigates the effects of various process parameters, specifically the substrate temperature, on bead track geometry in DED processes for stainless steel (1.4404). A specialized experimental setup, integrated within a DED machine, facilitates the controlled thermal conditioning of sample sheets. Using Design of Experiments (DoE) methods, individual bead marks are generated and analyzed to assess geometric characteristics. Regression models, including both linear and quadratic approaches, are constructed to predict machine parameters for achieving the desired bead geometry at different substrate temperatures. Validation experiments confirm the accuracy and reliability of the models, particularly in predicting the bead height, bead width, and contact angle across a broad range of substrate temperatures. However, the models demonstrated limitations in accurately predicting dilution, indicating the need for further refinement. Despite some deviations in measured values, successful fabrication is achieved, demonstrating robust bonding between the bead and substrate. The developed models offer insights into optimizing DED process parameters to achieve desired bead characteristics, advancing the precision and reliability of additive manufacturing technology. Future work will focus on refining the regression models to improve predictions, particularly for dilution, and further investigate non-linear interactions between process variables.