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The prediction of performance measures is an essential one for manufacturers to increase the service life. This paper deals with the application of Artificial Intelligence (AI) to predict the performance measures like surface roughness, material removal rate, and flank wear during the milling process from the experimental data. The milling experiments were conducted in wet conditions based on the Response Surface Methodology (RSM) design of experiments. The spindle speed, feed rate, and axial depth of cut were considered as process parameters. The experimental data were used to develop the regression model, Mamdani fuzzy inference system, Backpropagation Neural Network (BPNN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) model. The output of regression, fuzzy, neural network, and ANFIS model was compared with the experimental data, and predicted results were found to be in good conformity with the measured data. The prediction capability of the quadratic and Artificial Neural Network (ANN) model was very close to experimentally measured values and the quadratic model had an accuracy of 97.89% for surface roughness, 98.38% for material removal rate (MRR), and 95.72% for flank wear.

This study aims to investigate experimentally and analytically the effects of different machining parameters such as cooling methods and cutting tool materials on surface roughness and chip thickness ratio for milling of AA7075-T6 aluminum alloy. The carbide and high-speed steel (HSS) end mills were used as cutting tools and the conventional, vapor, and compressed air were used as cooling methods in the experiments. The experiment conditions for compressed air at the cutting zone were 6 bar pressure and 30m/s speed flow rate. A mixture of boron oil and water (1/20 mixture ratio) was used as cutting fluid in conventional cooling. The study was carried out using three levels of feed rates (20, 40, 80mm/min), rotational speeds (780, 1330, 2440rpm), and a constant 2mm deep cut. As a result of the experiments, the surface roughness values increased with the increasing levels of feed rate. Besides surface roughness values decreased with increasing levels of the rotational speed. In addition, a better surface quality was obtained in milling processes by using carbide cutting tools compared to HSS tools. It was concluded that the most important parameter affecting the surface roughness and chip thickness ratio is feed rate and the rotational speed, respectively. Better surface roughness and chip thickness ratio were obtained from the vapor processing than the conventional and compressed air.

Manufacturing industries are rapidly growing with varying customer needs, and efficient quality control tools are widely used to optimize product/process performances. This paper highlights the modified quality control module to optimize the milling performances of polymer nanocomposites. The carbon fabric and reduced graphene oxide reinforced (CF/rGO) polymer composites are machined at varying process constraints. The experimentation was designed according to Taguchi’s orthogonal array. The Milling performances were optimized using a multi-criterion decision-making (MCDM) tool based on a combination distance-based assessment (CODAS) optimization method. The desired value of surface roughness (Ra) and cutting force (Fc) is examined during the machining of the developed polymer. CODAS optimization module efficiently combined the various contradictory parametric outcomes into a single objective assessment value (Hi), which could not be possible by utilizing the usual conventional Taguchi method. Specifically, the optimal machining conditions were found to be rGO wt.%—1, speed—2000rpm, feed—80mm/min, DoC—1.5mm. Overall, the findings demonstrate the practicality of the recommended MCDM tool, which outperformed the usual conventional Taguchi method. The optimal assessment score of CODAS was noted as 1.904, which confirms the better viability of the current MCDM approach. This study contributes to the advancement of efficient quality control tools that can be widely used to optimize product/process performances in manufacturing industries.

The surface roughness is a crucial factor in machining methods. The most effective factors on surface roughness are feed rate and tool nose radius. Due to the many advantages of wiper (multi-nose radius) inserts, their importance and use has been increasing recently. The purpose of this paper is to investigate the effect of wiper inserts on surface roughness and tool wear. In this study, conventional inserts and wiper inserts were experimentally compared separately in milling and turning operations. Compared to conventional inserts, the surface roughness values obtained using wiper inserts improved by 33% in turning operations and approximately 40% in milling operations. It was observed that the production time in the turning process was reduced by about 25% in the case of using wiper inserts compared to the use of conventional inserts. In milling, this ratio was determined to be approximately 43% due to the fact that it has multiple cutting edge. It has been observed that the use of wiper inserts in machining methods creates a significant time and cost saving advantage.

Abrasive water jet (AWJ) machining is one of the advanced machining techniques used in the industries for processing materials that are extremely difficult to machine using conventional machining techniques. Based on the flexibility of AWJ, this process is currently employed for milling blind pockets over different materials. The most frequent method for making blind pockets in AWJ is the controlled depth milling mode. This approach was carried out with the raster tool paths. The quality of the blind pocket surface is influenced by different AWJ parameters such as water jet pressure, traverse speed, step-over distance, abrasive flow rate, and abrasive types. Among these, the traverse rate was found to be an influencing factor in most of the AWJ milling operations as it determines the nozzle speed followed by the energy density of the abrasive particle drops while striking across the target material surface, which resulted in a controlled depth of cut. This review paper highlights the performance of the AWJ pocket milling operations with various materials. From these results, it is reported that most of the AWJ milled surfaces were found to be of rough quality even though they were using different milling tool path strategies and parameter conditions. In addition, the milled pocket defects, namely uneven flatness, grit embedment, and undercut were observed. Besides, future research and directions have been addressed in which some of the novel concepts/approaches have been introduced including the scale effect examination in AWJ with the use of different nozzle, orifice, and abrasive sizes. This study will be more helpful to produce blind pockets with tight tolerances and a significant reduction in the process defects. The outcomes of this study will bring new innovations to the AWJ milling technique in order to make a significant footprint in the manufacturing industries for machining quality blind pockets over the target materials.

The use of carbon fiber-reinforced polymer (CFRP) composites having low weight and high strength provides the substantial energy savings in space and aerospace industry. The disadvantage of these composites is that the carbon fiber is not firmly bonded to the epoxy resin and the toughness of the produced materials is low. Graphene (G) and Graphene Oxide (GO) nanoparticles are used to functionalize CFRP composites. The CFRP composites functionalized with G and GO improve the strength of these composites by improving the fiber/matrix interface bond. In this study, the effect of type of nanoparticles, feed rate, cutting speed and number of flutes on machinability (cutting force, delamination factor and surface roughness) were experimentally investigated in the milling of CFRP composites, G-CFRP (CFRP functionalized with G) and GO-CFRP (CFRP functionalized with GO) nanocomposites. Cutting force, delamination factor, and surface roughness were found to be strongly impacted by feed rate, cutting speed, number of flutes, and type of nanoparticles. The increase in the number of flutes contributed to decrease of cutting force, delamination factor and surface roughness, while the increase in the feed rate caused to increase of them. By increasing cutting speed, surface roughness reduced, delamination factor and cutting force increased. In addition, compared to the CFRP composite, the cutting forces and surface roughness were higher, and delamination factor was lower in the CFRP composites functionalized with G and GO.

The main objective of this work is to experimentally investigate and statistically evaluate the effects of the milling parameters on surface roughness (Ra) and flank wear (Vb) in the milling of Inconel 625. Thus, milling experiments on different cutting conditions with Physical vapor deposition (PVD) and Chemical vapor deposition (CVD) coated inserts have been conducted on CNC milling machine according to Taguchi L18 orthogonal array. The effect levels of the milling conditions on Ra and Vb have been determined with analysis of variance (anova) at 95% confidence level. The analysis results indicate that the cutting tool is the most significant parameter affecting Ra while the feed rate is the most significant parameter affecting Vb. Then the linear and quadratic regression models have been applied in order to estimate Ra and Vb. The results show that a higher correlation coefficient ($R^2)$ is obtained via the quadratic regression model with a value of 0.97 for both Ra and Vb. Finally, the verification results are in excellent agreement with experimental findings, regarding the surface roughness (Ra), and tool wear (Vb).

Measuring the circular passes and errors of the periodic milling tools holder is of paramount importance for ensuring excellent surface roughness. For rendering circular shapes during a milling process, a circular interpolation to measure circular passes and errors of the periodic cutting tools holder is thus used. This model is intended to complement the computerized numerically controlled (CNC) systems. The model contains a number of blocks, such as a complete circular interpolation cycle of a number passes which is specified in the programmable part of the model. This study presents a new localization approach for an elastic cutting tool holder of a milling machine. A numerical model is developed that describes the structure of the tool holder. The behavior of the periodic holder is modeled numerically. The modeling results are in agreement with the experimental measurements with a relative error of about 8%.

Using Taguchi design of experiments (DoE), experiments were conducted with 3 factors and 3 levels. The factors were the depth of cut, spindle speed, and feed. The responses were surface roughness, flank wear, material removal rate, and spindle vibration along $x$ ($V_x$), $y$ ($V_y$), and $z$ ($V_z$) axis. To convert the multi-response optimization problem into a single response optimization problem, the technique for order of preference by similarity to ideal solution (TOPSIS) was applied. The S/N of the closeness coefficients from TOPSIS was calculated and optimum machining conditions were obtained. Further, analysis of variance (ANOVA) was performed to verify which input parameter significantly affects the output responses. From TOPSIS optimization, the responses like surface roughness and flank wear were decreased by 0.99% and 2.55%. The vibration in $x$, $y$, and $z$-axis decreased by 3.84%, 16.87% and 12.48% respectively. This optimization significantly enhances the machining characteristics.

Flake Powder Metallurgy (FPM) is utilized for the processing of Al–Al₂O₃ composites. The effects of contents of 1-$\mu$m-sized alumina (0, 3, 6 and 9 vol.%) on the microstructure, hardness, porosity and wear behavior of these composites are investigated. The as-received aluminum powder particles are milled in a planetary ball mill for different time durations (0.5, 1, 1.5 and 2 h), and the resultant flake powders are characterized by sieving, SEM, optical microscopy and XRD to determine their particle size, morphology and grain size. Al flakes and different amounts of Al₂O₃ powders are stacked into the mold cavity using a floating column filled with alcohol. Then the compacts are cold pressed at 750 MPa and sintered in a tube furnace at 655^∘C for 60min. For comparison, reference samples from as-received aluminum powders are also fabricated. SEM studies showed a uniform distribution of alumina particles within the matrix of FPM-processed composites. These composites, despite their higher porosity, exhibited higher hardness levels and improved wear properties in comparison with the conventionally produced powder metallurgy (PM) counterparts. This is due to: (i) the special morphology of the flake powders that contributed to a more uniform distribution of alumina within the matrix and (ii) their smaller grain size due to work hardening that occurred during milling, which resulted in higher hardness values.

Graphene oxide (GO)-doped CFRP composites possess excellent mechanical properties for high-performance products of aircraft, defense, biomedical and chemical trades. This paper highlights a novel hybridization of the combined compromise solution-principal component analysis (CoCoSo-PCA) method to optimize multiple correlated responses during CNC milling of GO-doped epoxy/CFRP. The influence of process constraints like drill speed ($S$), feed rate ($F$), Depth of cut ($D$) and GO wt.% (GO) on machining performances like MRR, cutting force ($F_c$) and Surface roughness ($Ra$) has investigated. Taguchi $L_9$ orthogonal array considered for machining (milling) of composite by using Titanium aluminium nitride (TiAlN) milling cutter ($\varphi 5$mm). A multivariate hybrid approach based on combined multiplication rule was utilized to evaluate the ranking of the alternatives decision process and optimize responses. ANOVA reveals that spindle speed (82.24%) is the most influential factor trailed by feed rate (5.02%), depth of cut (0.55%) and GO wt.% (2.17%). This module has fruitfully tackled critical issues such as response priority weight assignment and response correlation. Finally, CoCoSo-PCA shows the higher predicted value of 9.06 and confirmatory test performed on optimum settings as $S-1600$ RPM, $F$-160mm/rev $D-0.5$mm and GO-1%, which show a satisfactory agreement with actual ones for favorable machining environment.

During the machining of aluminum alloys, the adhesion of chips to the tool affects the performance characteristics. Today, different cooling systems are used to eliminate these negativities. In this study, the effects of end milling using HSS and carbide cutting tools of 6061-T6 aluminum alloy on surface roughness, chip thickness ratio and tool wear were examined using different cooling techniques (dry, minimum quantity lubrication (MQL) and nanocutting fluid). Different cutting speeds (180, 200, 220 m/min) and different feed rates (0.05, 0.06, 0.07 mm/rev) were used in the experiments. According to experimental findings, tool wear and surface roughness decreased at low cutting speed and feed rate by using nanocutting fluid with carbide cutting tools. It has been observed that the chip thickness ratio increases with high cutting speeds using nanocutting fluid and decreases with dry machining and high feed rates. The best milling performance of the aluminum alloy was achieved in experiments using carbide cutting tools and nanocutting fluid.

In the automotive industry, sealing quality between two flat joint surfaces is directly affected by the surface flatness. To know how much flatness is caused by machining operations, a traditional trial-and-error method has been used. The prediction for machined surface error/distortion can help to assess the integrity of the structural design as well as develop fixturing scheme to optimize machining quality. In this paper, a finite element method is applied to extract the compliance matrix of milling surface of a workpiece, such as the cylinder deck face, and an encoding MatLab program is used to compute the flatness due to milling forces. The paper focuses on deriving analytical models for evaluating the flatness of the cylinder deck face and optimizing the manufacturing process. Some special considerations have been taken to manufacturing cutting force evaluations according to analysis results of the deck face flatness. Emphasis is also placed on the optimization of machining parameters by iterations of flatness results so that minimization of surface deformations under machining loads can be achieved. The methodology introduced in the paper is the closed-loop iteration by combining structural finite element analysis (FEA) simulation, tooling kinematic simulation, and MatLab data modeling.

In developing a new milling technique that can produce high precision, smoothness, and gloss on nickel workpiece surfaces, a widely used material is in industrial applications, particularly in mold manufacturing, in which the production requires exceptionally high accuracy. In this work, the factors influencing the magnetic material milling process are determined by investigating the distribution of magnetic iron (MIGs) and abrasive grains (AGs) in the working surface of magnetic liquid slurry (MLS). The magnetic liquid slurry (MLS) contained commercially available MIGs successfully applied for milling the surface of magnetic materials with extremely high accuracy. Surface roughness ($\mathrm{\text{Ra}}=0.592$ nm) without leaving scratches on the surface after milling.

This paper presents a novel concept to enhance the reconfigurability issues (i.e. convertibility, flexibility, etc.) of a small-scale reconfigurable machine tool (RMT) by combining multiple modules (both basic and auxiliary parts) and manufacturing operations on a single workstation. The proposed reconfigurable machine will reduce the overall space, transport time, ramp up time, reconfiguration time and interim storage space. Further, various characteristics of reconfigurability (such as scalability; convertibility, flexibility, etc.) and enablers of the machine tool have been discussed. The proposed design is illustrated with a figure, which is designed on a 3D design tool (solid-works software platform).This machine tool will perform six major cutting and finishing operations, namely, hacksaw cutting, milling, drilling, grinding, buffing and lathe operations on a single work platform.

The final shape of fiber-reinforced polymer (FRP) materials is usually given by machining. However, machining FRP materials is complex and difficult. Appropriate cutting tools and cutting parameters should be determined to overcome these difficulties. In this work, glass fiber-reinforced polymer (GFRP) materials with 60% and 68% fiber ratio, produced by pultrusion method, were machined. End of the milling, surface roughness (Ra), delamination damage factor (Fdd), sound and vibration results were analyzed. Experiments were carried out with Taguchi L${}_{16}$ mixed design and the results were analyzed by Taguchi, ANOVA and Pareto charts. In Taguchi and Pareto analyses, the most effective parameter for surface roughness and delamination damage factor was the feed rate, and the cutting velocity for sound and vibration. Different regression models have been tried. Linear regression was found as most suitable. The significance values of the regression models are 92.04% for surface roughness, 87.12% for delamination damage factor, 98.64% for sound and 73.27% for vibration.

This study is based on Taguchi’s design of experiments along with grey relational analysis (GRA) to optimize the milling parameters to minimize surface roughness, tool wear, and vibration during machining of Inconel-625 while using coconut oil as cutting fluid (CF). The experiments were conducted based on Taguchi’s L₉ orthogonal array (OA). Taguchi’s S/N was used for identifying the optimal cutting parameter for individual response. Analysis of variance (ANOVA) was employed to analyze the outcome of individual parameters on responses. The surface roughness was mostly influenced by feed. Flank wear was influenced by speed and the vibration was mostly influenced by the depth of cut as well as speed. The multi-response optimization was done through GRA. From GRA, the optimal parameters were identified. Further, nanoboric acid of 0.5 and 0.9wt.% was mixed with coconut oil to enhance lubricant properties. Coconut oil with 0.5wt.% of nanoboric acid minimizes the surface roughness and flank wear by 3.92% and 6.28% and reduces the vibration in the $z$-axis by 4.85%. The coconut oil with 0.5wt.% of nanoboric acid performs better than coconut oil with 0.9wt.% of nano boric acid and base oil.

Computer simulation of industrial processes is an important alternative that may be used either to complement or to replace expensive experimental procedures associated with developing new parts or modifying existing process. For a metal cutting process, numerical simulations provide vital information about cutting forces, cutting temperatures, tooling and part distortion, etc. Since the early 1970s, FEA has been applied to simulate machining process. The development of this approach, its assumptions and techniques has been widely accepted. Nowadays, the manufacturing productivity even drives the community to the next level innovation through computer utilizations. A kinematic simulation of machining processes is one of many innovative CAE applications, especially beneficial to high volume production of automotive powertrain parts. In this paper, a generic force calculation method is introduced with a modified horsepower correction factor. An example of sizing milling force, milling paths and proper milling parameters is provided by utilizing the methodology. This paper will also discuss and propose how the manufacturing industry uses this resourceful tool. Applications of the methodology would empower product and manufacturing engineers to make intelligent and cost effective decisions.

Aluminum alloys are widely employed in design and material selection in the industry due to their superior ergonomic properties and low cost. Therefore, aluminum has been strengthened with various elements and powder metal reinforcements in recent studies. In this study, the 6061 aluminum alloy, which is used widely in production, was reinforced with B₄C powder metal and copper elements in a hybrid form. The mechanical, metallurgical, and machinability properties of all reinforced 6061 aluminum materials were examined and characterized. In particular, this study examined the machinability of the materials produced differently from the literature by using equations of energy power conversion and the Taguchi Method, which is one of the experimental design methods, and compared the results of the machining process for different materials. Furthermore, the effects of feed rate, cutting speed, and amount of passes on machinability properties were investigated by conducting the ANOVA analysis on the experimental design parameters and levels. Consequently, while Cu and B₄C reinforcement improved the hardness and mechanical properties, positive results were also obtained on machinability.

Milling is the mechanical process of removing material from a piece of stock through the use of a rapidly spinning circular milling tool in order to form some desired geometric shape. An important problem in computer-aided design and manufacturing is the automated generation of efficient milling plans for computerized numerically controlled (CNC) milling machines. Among the most common milling problems is simple 2-dimensional pocket milling: cut a given 2-dimensional region down to some constant depth using a given set of milling tools. Most of the research in this area has focused on generating such milling plans assuming that the machine has a tool of a single size. Since modern CNC milling machines typically have access to a number of milling tools of various sizes and the ability to change tools automatically, this raises the important optimization problem of generating efficient milling plans that take advantage of this capability to reduce the total milling time. We consider the following multiple-tool milling problem: Given a region in the plane and a set of tools of different sizes, determine how to mill the desired region with minimum cost. The problem is known to be NP-hard even when restricted to the case of a single tool. In this paper, we present a polynomial-time approximation algorithm for the multiple-tool milling problem. The running time and approximation ratio of our algorithm depend on the simple cover complexity (introduced by Mitchell, Mount, and Suri) of the milling region.