Depth Of Cut Research Articles

Estimation of the Maximum Angular Position for Cutting Roof Rock Material

In the mining and construction industry, the picks on an excavation machine often need to cut different rock types within the same drum revolution. If a rock type is much stronger than others, cutting this type of rock will often have a significant impact on the failure rates and performance of the picks and excavation machine. Therefore, it is necessary to understand the maximum angular position for cutting this type of rock, especially when it is hard rock. The accuracy of the maximum angular position for cutting the hard rock in a drum revolution affects the accuracy of the analysis of the maximum depth of cut for cutting the hard rock. It also affects the drum balance. In this paper, a method is developed to accurately calculate the maximum angular position for hard rock cutting based on the roof rock cutting in coal mining. In some cases, a simple but approximate method can be used to estimate the maximum angular position. One such case is when the strength differences between different rock types are not significant. The comparison of the influences of the accurate results on the pick performance analysis with that of the results given by the approximate method is also conducted. It shows that when the ratio of the height of the roof rock being cut to the tip-to-center cutting radius is 0.5, the maximum depth of cut in a drum revolution is less than 73 mm and the tip-to-center cutting radius is greater than 700 mm, the relative analysis error caused by the approximate method is less than 6%.

Investigation on Surface Roughness and Power Consumption for Sustainability Assessment in Hard Turning of HSLA Steel With SPPP‐AlTiSiN–Coated Carbide Tool Under Various Cooling‐Lubrications

ABSTRACTThe present research analyses the power consumption (Pc) and surface roughness (Ra) in hard turning of high‐strength low‐alloy (HSLA) grade AISI 4140 steel using a recently developed AlTiSiN‐coated carbide tool under different cooling‐lubrication conditions (dry, flooded, nanofluid‐MQL). The nanofluid was prepared by mixing the MWCNT nanoparticles with an eco‐friendly automotive radiator coolant (base fluid). The cooling‐lubrication performance is investigated briefly by comparing the machining responses like machined surface morphology, tool wear, cutting force and temperature. The experiments associated with 46 trials were performed by considering various machining variables, namely cutting speed, nose radius, depth of cut, feed and cooling‐lubrication methods. From the perspective of predictive modelling and multi‐response optimisation, response surface methodology has been employed to minimise power consumption and surface roughness. Thereafter, the predictive modelling and optimisation results are implemented for economic analysis and energy‐saving carbon footprint evaluation. This innovative research also addresses comparative environmental sustainability evaluation in hard turning under different cooling‐lubrication conditions using a life cycle assessment methodology for cleaner and safer production. Results indicate that cutting speed was the most influential item in power consumption enhancement. Furthermore, compared with dry and flooded turning, lower cutting force, reduced cutting temperature, shorter width of flank wear and better surface morphology were obtained under nanofluid‐MQL machining. It has been observed that nanofluid‐MQL machining outperformed sustainability improvement concerning techno‐economically viable societal acceptable and environmental friendliness.

Tool Wear Analysis During Turning with Single and Dual Supply of LN2

In the presented research work, LN2 supplied directly at rake face and in another localised machining condition, LN2 supplied at rake and flank face simultaneously. The DoE of performance of experiments was in accordance with Taguchi S/N ratio L18. It was found that flank wear length and crater wear width at LN2 supply at rake & flank face simultaneously declined by 23-38% and 20-30% respectively as compared to LN2 supply at rake face only. ANOVA gave the highest effect of contribution in the percentage to LN2 supply at rake & flank face simultaneously as 76.06% and 77.67%, next in decreasing order followed by speed, feed and depth of cut. SEM images depicted that flank wear length and crater wear width in both machining conditions. Tool wear was low during turning LN2 supply at rake and flank face. Optimized values of each response was confirmed by repeating the experiments.

Multidimensional ultrasonic vibration machining modeling of SiCp/Al cutting forces with different volume fractions: Experiments and numerical simulations

The cutting properties of the particles of aluminum-based silicon carbide (SiCp/Al) and the base material are so disparate that it is challenging to ensure the surface quality of machining. The cutting force significantly influences the quality of the machined surface. Therefore, real-time cutting force prediction is paramount for ensuring the workpiece's desired surface quality. This paper presents a 3D ultrasonic milling force model, established from four aspects: cutting formation force, extrusion friction force, SiC particle crushing force, and ultrasonic impact force. The model is based on the ultrasonic action of the cutting process and the unique removal characteristics of the material. A 3D ultrasonic milling system was constructed to experimentally verify the milling force model. The effects of each parameter on the milling force were analyzed through orthogonal experiments, which resulted in the effects of depth of cut, rotational speed, volume fraction, ultrasonic amplitude, and feed rate. The ultrasonic amplitude, feed rate, and other factors were found to influence the milling force to varying degrees. The milling force was observed to be 42.47 %, 22.37 %, 10.85 %, 12.67 %, and 11.64 % affected by the factors mentioned above, respectively. The single-factor experiment was conducted to analyze the cutting force at different machining parameters. The experimental results and the MATLAB numerical simulation results exhibited a similar trend of change. The maximum absolute error between the two was 12.71 %, with an average absolute error of 3.735 %.

Analysis and optimization of machining parameters of AZ91 alloy nanocomposite with the Influences of nano ZrO2 through vacuum diecast process

The magnesium alloy composite is a vital material for automotive applications due to its features like high stiffness, superior damping resistance, high strength, and lightweight. Here, the motto of research is to establish the AZ91 alloy nanocomposite with the exposures of 0, 1, 3, and 5 volume percentages (vol%) of nano zirconium dioxide (ZrO2) particles (50nm) through fluid stir metallurgy route associated with 1x105 Pa vacuum die cast process. Exposures on structural morphology, hardness, and impact toughness of composite are analyzed and identified as the nano AZ91 alloy composite enclosed with 5vol% is homogenous particle dispersion, enhanced hardness (97.6HV), and optimum toughness of 21.2J/mm2. However, composite faces machining difficulties due to the hard abrasive particles with higher hardness, resulting in tool wear. This experiment predicts the optimum mill parameters during the end mill operation of magnesium alloy nanocomposite (AZ91/5vol%) by using a tungsten carbide coated end mill cutter to attain the maximum metal removal rate with low surface roughness and tool wear analyzed via the general linear model (GLM) ANOVA approach. The input conditions for end milling operation vary, like feed rate (0.1 -0.4mm/rev), depth of cut (0.05 -0.2mm), and spindle speed (250-1000rpm). During the ANOVA GLM approach, the L16 design experiment is fixed for further interaction analysis. The results predicted by the depth to cut and feed rate were dominant and played a major role in deciding the tool wear, surface roughness, and MRR.

Research on production of microgroove arrays on TiAl intermetallic alloy by micro milling

TiAl intermetallic alloy is a superior lightweight material with excellent performance, and its micro features or components have promising applications in manufacturing fields. In this work, an investigation of micro milling of microgroove arrays on TiAl alloy was studied. The effects of cutting parameters including the feed per tooth (fz) and depth of cut ( ap) on the milling force, surface morphology, surface roughness and burr size of the micro-slots were investigated. The wear types and mechanisms of the micro end mills were also studied. The results showed that the optimal cutting parameters were fz = 3.5 μm/z and ap = 4 μm, which provided the best surface roughness Sa of 152 nm, top-burr heights on up- and down-milling sides of 19 and 19.5 μm respectively. Using the optimized cutting parameters, microgrooves with a width of 500 μm, aspect ratio of 5 and array period of 10 were fabricated successfully. Surface topography, dimensional accuracy and perpendicularity of the microgroove arrays were characterized. Results demonstrated that an increase in the microgroove number, microgroove edge chipping as well as burrs of the entrance and exit increased gradually. The dimensional error between the obtained width and the designed width of the microgroove was less than 2%, and the slope of the sidewalls reached more than 89°. The wear mechanisms of the micro end mills were abrasive wear and adhesive wear, and wear types were coating delamination, cutting edge chipping, and tool nose breakage.

A Study of Drilling Parameter Optimization of Functionally Graded Material Steel–Aluminum Alloy Using 3D Finite Element Analysis

Composite materials, such as aluminum alloy FGMs, provide advantageous weight reduction properties compared to homogenous pure structures while still preserving sufficient stiffness for diverse applications. Despite various research on drilling simulation concepts and ideas for these materials, there still needs to be an agreement on the process modeling. Researchers have looked into a lot of different numerical methods, including Lagrangian, Eulerian, arbitrary Lagrangian–Eulerian (ALE), and coupled Eulerian–Lagrangian (CEL), to find solutions to problems like divergence issues and too much mesh distribution, which become more of a problem at higher speeds. This research provides a global analysis of bottom-up meshing for eleven 1 mm layers using ABAQUS® software. It combines the internal surface contact approach with the Lagrangian domain’s kinematic framework. The model uses the Johnson–Cook constitutive equation to precisely predict cutting forces, stress, and strain distributions, optimizing cutting parameters to improve drilling performance. According to Taguchi analysis, the most favorable parameters for reducing cutting force and improving performance are a rotational speed of 700 rpm, a feed rate of 1 mm/s, and a depth of cut of 3 mm. The findings suggest that increasing the feed rate and depth of cut substantially affects the cutting force, while the rotational speed has a comparatively little effect. These ideal settings serve as a foundation for improving FGM drilling efficiency.

An Experimental Study in Laser-Assisted Machining of AerMet100 Steel.

To solve the problems of poor surface quality and low tool life in conventional machining (CM) of AerMet100 steel, an experimental study was conducted in laser-assisted machining (LAM) of AerMet100 steel. The effects of laser power, cutting speed, feed rate, and depth of cut on the surface roughness of AerMet100 steel were studied based on a single-factor experiment. The degree of influence of each factor on the surface roughness was evaluated by analyses of variance and range in the orthogonal experiment, and the combination of process parameters for the optimal surface roughness was obtained. The order of influence was as follows: laser power > cutting speed > depth of cut > feed rate; the optimal combination of process parameters was laser power 200 W, cutting speed 56.5 m/min, feed rate 0.018 mm/rev, and depth of cut 0.3 mm. Compared to CM, the surface morphology of the workpiece under the optimization of LAM was relatively smooth and flat, the surface roughness Ra was 0.402 μm, which was reduced by 62.11%, the flank wear was reduced from 208.69 μm to 52.17 μm, there were no tipping or notches, and the tool life was significantly improved. The study shows that the LAM of AerMet100 steel has obvious advantages in improving surface quality and reducing tool wear.

Investigating the effect of deicing parameters using high-pressure water jet

Maritime operations are impacted by ice accretion on decks, bulkheads, and offshore structures during the winter or in extremely frigid climates. In most cases, the traditional method of deicing maritime vessels is human labor, which requires enormous effort and lengthy hours for unsatisfactory results, particularly when the ice adhesion strength is high. This study experimentally examined combined effects of operational parameters, including operating pump pressure, nozzle geometry, water jet temperature, standoff distance, and time of cut, on the depth and width of a cut through an ice block. In comparison to other parameters, the influence of nozzle geometry on both the width and depth of cut was found to be more significant, with R-squared values of 85% and 62% respectively. Increasing the depth and width of the cut facilitated the delamination and disintegration of ice from an aluminum mold. This indicates that water jet deicing on maritime vessels could be effective, therefore achieving the objective of this study. The optimization of operational parameters is used to develop a cuttability chart for various thicknesses of accumulated ice on marine vessels.

Predicting error for machining thin-walled blades considering initial error

A multistage machining process is employed to machine the blades with low stiffness. Nonetheless, machining errors can be transferred and accumulate throughout the multistage machining process, complicating the precise prediction of the final accuracy of thin-walled blades. Consequently, this paper introduces a machining accuracy prediction model for thin-walled blades that takes into account initial error. The machining error prediction model of thin-walled blades is developed using Gaussian process regression optimized by the sparrow search algorithm (SSA-GPR) with the initial contour error, depth of cut, feed per tooth, and spindle speed as inputs, and the machining error as the output. And the results show that the prediction accuracy of the SSA-GPR is 6.73 % higher than that of the Gaussian process regression (GPR), 13.73 % higher than that of the back propagation neural network (BPNN), and 32.32 % higher than that of the support vector machine regression (SVR). The influence of the initial error and milling parameters on the machining error is analyzed through the length-scales of the Gaussian kernel function. The findings indicate that the depth of cut, feed per tooth and initial error significantly affect the machining error, whereas the spindle speed has a minor impact on the machining error. Furthermore, the 3D graph based on the SSA-GPR shows that the increase of the initial error will increase the machining error of thin-walled blades. This research provides a theoretical foundation for the process optimization of thin-walled blades.

Sustainable machining of Inconel 718 using minimum quantity lubrication: Artificial intelligence-based process modelling

Governments and industries are developing aggressive policies to reduce carbon emissions and shift from fossil fuels to renewable energy. On the other hand, industries struggle to reduce energy consumption and depend on production lot sizes to control energy requirements. In this regard, energy-efficient processing through CNC machine tools can potentially influence energy demand and requires energy-aware power consumption strategies for machining processes. For manufacturing a single product, predicting energy demand can be decisive in determining parametric control and other factors. Previously analytical models have been largely used to model machining requirements and energy demand. However, these models largely depend on parameterization and do not facilitate the integration of external sub-systems. Therefore, in this paper, an artificial intelligence-based power reduction strategy is developed and implemented on single material (Inconel 718), four control parameters (cutting speed, feed rate, depth of cut and flow rate) and two sub-systems (minimum quantity lubrication (MQL) and nanofluids-based minimum quantity lubrication (NF-MQL)). The paper employs four machine learning algorithms,’ K-Nearest Neighbor’, ‘Gaussian Regression’, ‘Decision Tree’, and ‘Logistic Regression’, to evaluate their functionality in predicting power consumption (Pc) of CNC machining systems using a real experimental data set. As per evaluation based on five performance metrics (R-square, MSE, RMSE, MAE, and MedAE), ‘Decision Tree’ has achieved the most accurate power consumption predictions. The comparative results highlight ‘Decision Tree’ as the most better predictor with the optimal max_depth of 2 showing Pc MQL R2 of 0.915 and Pc NF-MQL R2 of 0.931.

Parametric Analysis and Optimization on Fiber Laser Micro-Milling on AZ31 Magnesium Alloy

One of the proficient machining technologies to create micro-features in a variety of materials is the laser micro-milling process. This procedure involves irradiating a highly concentrated laser beam on the sample and performing laser cutting at various cutting patterns to change the surface conditions and enhance the characteristics of that surface tribology. In this work, an effort has been made to mill magnesium alloy (AZ31) material using a laser. The pulse frequency, laser beam power, cutting speed, number of passes, and transverse feed are among the different process variables taken into account for the current research effort. Measured results include depth of cut and surface roughness. To ensure the validity of the established empirical models, this paper also presents experimental data. Several surface plots have been used to examine the test data. To get the lowest values of surface roughness and depth of cut, multiperformance optimization is also utilized. The influence of process factors on response has also been examined using optical microscopic images. Gray relational analysis approach is also applied to obtain the multiobjective optimization parametric combination and based on the calculated results of gray relational grade, it is revealed that improvement in the value of gray relational grade at predictive setting is 0.054 compared to the initial parametric combination.

Multiobjective optimization of end milling parameters for enhanced machining performance on 42CrMo4 using machine learning and NSGA-III

The present study analyzes and optimizes machining characteristics, including feed rate (fz), depth of cut (ap), cutting speed (Vc), cutter-coated material (Mtc) and cutting-edge radius (rt), impacting on surface roughness (Ra), material removal rate (MRR) and tool wear (VB) of 42CrMo4 steel during dry end-milling. A total of 108 experimental runs were conducted, focusing on Ra, VB and MRR as response parameters. The nano TiAlN PVD-coated tool yielded better results for Ra and VB than did the TiCN/Al2O3 MT-CVD-coated tool. Then, Ra, VB and MRR optimization was carried out simultaneously using a Non-Dominated Sorting Genetic Algorithm III (NSGA-III) and Machine Learning (ML) models. Pareto solutions were found to offer a range of values for the three performance objectives: Ra (0.315–0.556 µm), VB (12.33–32.48 µm) and MRR (0.44–3.58 cm3/min). A quantitative performance score (Ps) ranking index was calculated to rank Pareto solutions for practical case studies. Validation experiments were subsequently performed to affirm that the optimal solution fell within a reasonable error range, with MAPE of 9.58% for Ra, 9.25% for VB and 13.39% for MRR. The validation results underscore the versatility of this approach, suggesting its applicability to a wide array of machining optimization challenges.

CHARACTERIZATION OF SURFACE, SUBSURFACE DAMAGE AND THEIR EFFECT ON FATIGUE LIFE AFTER MACHINING OF AEROSPACE-GRADE ALUMINUM ALLOY (Al 6082-T6)

Aluminum alloys are widely used in the automotive and aerospace industries due to their lower mass-to-strength ratio than other metallic alloys. Apart from their inherent properties, aluminum alloys like other metallic alloys show a significant change in their mechanical properties according to the machining parameters. The research literature on obtaining optimum mechanical properties of aluminum alloys that undergo machining is very limited. Moreover, the combined effect of several parameters on the machinability of aluminum alloys has not yet been explored. In this paper, the effect of three machining parameters (Depth of Cut (DoC)), feed rate (FR), and cutting speed (CS) on the subsurface damage and fatigue life of aerospace-grade aluminum alloy (Al-6082-T6) is observed. Samples are prepared using a full fractional approach to effectively capture the effect of all input parameters. Thereafter, samples were subjected to surface roughness, micro-hardness, and fatigue life tests. Results of surface roughness and micro-hardness tests are compared with fatigue life. The general linear model was employed to capture the percentage effect of each input parameter on the output parameters. The results showed that DoC was the main contributing factor that caused subsurface damage, while surface roughness and fatigue life were mainly affected by FR and CS. Optical microscope images showed a white layer formation that had higher hardness than the base metal. Overall, this research work proposes the input parameters that can be used to achieve minimum surface damage and fatigue life.

A COMPARATIVE MODELING AND OPTIMIZATION OF SURFACE ROUGHNESS IN THE END MILLING OF Al 3003 SUBJECTED TO NON-EQUAL CHANNEL ANGULAR PRESSING (NECAP)

This paper highlights the surface roughness optimization of a specific material, Al 3003, which has been subjected to the non-equal channel angular pressing (NECAP) process. Considering spindle speed, feed rate, and depth of cut as input variables and surface roughness as an output variable, experiments have been conducted based on the L27 orthogonal array of the Taguchi method. Four prediction models, namely exponential and response surface methodology (RSM) as mathematical models, and artificial neural networks (ANNs) prediction models with different training algorithms (Bayesian Regularization (BR) and Levenberg–Marquardt (LM)), are proposed. Applying effectiveness and performance criteria, the prediction accuracy of the exponential model (90.35%), RSM (93.07%), BR (97.83%), and LM (97.54%) shows that all proposed prediction models are efficient enough. The ANN model trained with BR is found to be the best fit for predicting surface roughness. In order to optimize surface roughness, a newly introduced optimization method called the Intelligible-in-time Logics Algorithm (ILA) is employed. High spindle speed (1000[Formula: see text]rev/min), low feed rate (100[Formula: see text]mm/min) and depth of cut (0.5[Formula: see text]mm) have been the optimum cutting parameter combinations to obtain minimum surface roughness (0.4956[Formula: see text][Formula: see text]m). The results have been verified by confirmation tests and Particle Swarm Optimization (PSO) method. ILA and PSO predict the same optimum parameter combinations and minimum surface roughness, while ILA performs optimization in less time (114.4[Formula: see text]s), about 3.5 times faster than PSO. The paper’s findings strongly advocate the application of ILA in machining data optimization.

Internally cooled tools: An eco-friendly approach to wear reduction in AISI 304 stainless steel machining

Austenitic stainless steel 304 is widely used in various applications due to its mechanical strength, toughness, and, most importantly, its corrosion resistance. However, machining this material presents significant challenges, primarily due to its high tendency for work hardening and generation of elevated temperatures. In the cutting process, this temperature rise can result in premature tool wear, leading to a reduction in their lifespan. To address these issues, cutting fluids are typically applied. Although this component offers advantages, it also contributes to environmental pollution, health risks, and significant costs, including disposal.Therefore, this study introduces an Internally Cooled Tool (ICT) method aimed at reducing the heat generated during machining. In this study, an analysis of tool life and wear was conducted to evaluate the effectiveness of ICTs compared to conventional machining methods during the turning of austenitic stainless steel 304 using double-coated tools (AlCrN on TiAlN, PVD (Physical Vapor Deposition)). Additionally, the use of ICTs in combination with lubrication (hybrid machining Minimum Quantity Lubrication (MQL) + ICT) was also investigated. The study covered five machining atmospheres (ICT, ICT + MQL, dry, wet, MQL). Cutting conditions were kept constant, including cutting speed (vc = 400 m/min), feed rate (f = 0.1 mm/rev), and depth of cut (ap = 0.5 mm). Scanning electron microscopy (SEM) analyses were conducted to examine the wear mechanisms and types present in each condition, along with statistical tests such as analysis of variance and Tukey tests to validate the experiments. The results indicated that ICTs (ICT and ICT + MQL) showed a longer tool life compared to dry machining and MQL techniques, while the wet machining method did not demonstrate significance compared to this technique. The observed wear mechanisms included abrasion, adhesion, and diffusion, with abrasion being the predominant mechanism. In summary, it was found that the durability of the inserts was directly related to coating adhesion, as coating detachment quickly led to the end of the insert's lifespan.

Performance analysis of a bucket drum for lunar regolith collection in lunar base construction

To facilitate the collection of lunar regolith for lunar base construction, the use of a bucket drum has emerged as an efficient and convenient method. This paper aimed to investigate the influence of drum configurations and operating parameters on collection performance. Initially, a DEM model for lunar regolith simulant particles was developed, and parameters were calibrated using RSM. Three different drum configurations were designed for simulation to compare the motion behavior of particles, drum forces, torque, and collection mass. Additionally, the effects of the rotation speed, cut depth, and linear cut speed on collection process were analyzed through simulations. Furthermore, a testing platform was constructed to evaluate the collection performance of the drum under various operating parameters. The influence of operating parameters was further confirmed, thereby validating the effectiveness and accuracy of the DEM simulations. These findings offer valuable insights for optimizing drum structures and establishing appropriate operating parameters.

Optimizing Power Consumption in Machining Nickel-Based Superalloys: Strategies for Energy Efficiency

<div>In the face of the world’s population growth and ensuing demands, the industrial sector assumes a crucial role in the management of limited energy supplies. Superalloys based on nickel, which are well-known for their remarkable mechanical qualities and resilience to corrosion, are now essential in vital applications like rocket engines, gas turbines, and aviation. However, these metals’ toughness presents a number of difficulties during machining operations, especially with regard to power consumption. This abstract explores the variables that affect power consumption during the machining of superalloys based on nickel in great detail and suggests ways to improve energy efficiency in this area. The effects of important variables on power consumption are extensively investigated, including cutting speed, feed rate, depth of cut, tool geometry, and cooling/lubrication techniques. A careful balance between these factors is necessary to maximize machining efficiency and reduce power usage. Furthermore, this study reviews the effect of different heat source applications on power consumption and the resultant quality of machined nickel-based superalloys. Additionally, the critical role of cooling and lubrication in mitigating the adverse effects of high temperatures generated during machining is thoroughly examined. Innovative cooling strategies, including cryogenic or high-pressure coolant systems, are explored as potential avenues to enhance heat dissipation and minimize power requirements. In essence, this abstract not only sheds light on the challenges inherent in machining nickel-based superalloys but also offers actionable insights into how energy efficiency can be maximized through strategic parameter optimization and the adoption of innovative cooling techniques. By addressing these aspects, manufacturers can effectively navigate the complexities of machining superalloys while minimizing their environmental footprint and operational costs.</div>

Modeling and Prediction of Surface Roughness in Hybrid Manufacturing–Milling after FDM Using Artificial Neural Networks

Three-dimensional printing, or additive manufacturing, represents one of the fastest growing branches of the industry, and fused deposition modeling (FDM) is one of most frequently used technologies. Three-dimensional printing does not provide high-quality surfaces, so finishing is required, and milling is one of the best methods for improving surface quality. The combination of 3D printing and traditional manufacturing technologies is known as hybrid manufacturing. In order to improve quality and determine optimal machining parameters, researchers increasingly use artificial intelligence methods. In the context of manufacturing technologies, both multiple regression analysis (MRA) and artificial neural networks (ANNs) have proven to be highly reliable in predicting and optimizing machining processes. This study focuses on the use of MRA and an ANN to analyze the influence of machining parameters such as feed rate, depth of cut, and spindle speed on the surface roughness of a 3D-printed part in a milling process. The study compares the measured results with the outcomes obtained through MRA and the ANN to assess their effectiveness in predicting and optimizing surface roughness. The results show that higher accuracy was obtained from the ANN method.

Smart manufacturing platform based on input-output empirical relationships for process monitoring

Intelligent monitoring and maintenance protocols are undoubtedly crucial for improving manufacturing processes. Accordingly, machine learning techniques and predictive control models have been customized and optimized to account for the specific characteristics of the processes under investigation. In this context, the management of manufacturing processes in a “smart way” requires the development of specific models based on input-output empirical data. The aim of the proposed research was to develop an easily customizable application integrated into a milling process executed at the laboratory level. The application was designed to identify and record the operator, the order and the specific work sequences. It also supports the operator in setting processing parameters according to the type of work sequence to be performed. The application analyses specific process outputs, such as the wear growth on the inserts of the cutter in relation to the main input process parameters: depth of cut, feed rate, and spindle speed. This analysis is implemented by leveraging empirical evidence.