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26 February 2024

31 Pages

A Review of the Factors Influencing Surface Roughness in Machining and Their Impact on Sustainability

,
and
1
Department of Industrial Systems Engineering and Design, Universitat Jaume I, 12071 Castellón de la Plana, Spain
2
Department of Mechanical Engineering and Materials, Universitat Politècnica de València, 46022 Valencia, Spain
3
Department of Mechanical Engineering, University of North Texas, Denton, TX 76207, USA
*
Authors to whom correspondence should be addressed.

Abstract

Understanding surface roughness generation in machining is critical to estimate the final quality of the part, optimize cutting conditions, reduce costs and improve manufacturing sustainability in industry. This work presents a review of the factors that affect surface roughness generation in machining (turning/milling) processes. Up to twenty-five different factors were identified, which were classified as setup factors (cutting tool, machine tool/fixturing and workpiece factors), operational factors (cutting and process parameters) and processing factors, which are related to the resulting cutting processes, such as built-up edge, chatter or tool wear. The importance of understanding these factors to improve machining sustainability is highlighted through three case studies, ranging from a simple change in the cutting insert to a more complex case where a controlled surface roughness leads to the elimination of a grinding stage. A case study illustrating the potential benefit of MQL in the sustainability of the machining process is also reported from the mold manufacturing industry. In all of the cases, the improvement in sustainability in terms of the reduction in kg of CO2 equivalent is notable, especially when grinding operations are reduced or eliminated from the manufacturing process. This paper can be of interest to practitioners in finishing operations at milling and turning operations that want to increase machining sustainability through a deep understanding of surface roughness generation.

1. Introduction

Surface roughness is one of the most important characteristics of product quality which has a direct impact on product performance, aesthetic appearance, fatigue, corrosion resistance, etc. This important product specification is defined in technical drawings by surface roughness annotations, such as Ra (arithmetic mean height), Rt (total height) or Rq (root mean square height) [1]. These requirements are considered by process planners in order to select the correct machine tool equipment, cutting tools or cutting parameters, and these decisions have a clear impact on machining sustainability [2]. Very often, process planners directly deal with cutting parameters, such as the cutting speed, feed rate and depth of cut, to ensure surface roughness specification. However, it is well known that surface roughness is not influenced by these cutting parameters alone, and many other factors have been identified and studied, such as the wear of the cutting tool, workpiece and cutting tool material; the geometry of the cutting tool; the stability and rigidity of the machine tool/fixturing system; the emergence of a built-up edge during cutting; the use of a refrigerant/cutting lubricant; and so on [3]. Research reviews related to factors that affect surface roughness are reported in [4,5], and relevant books, such as those presented in references [3,6,7], that are related to machining and cutting theory give a general overview of the complexity of surface roughness generation and the large number of factors that are interrelated.
Although the relationship between surface roughness and many different factors in the machining process has been shown in the literature, the influence of surface roughness on machining sustainability is commonly overlooked. In fact, when dealing with machining sustainability, most of the available research works are related to the use of dry or near-dry cooling techniques [8,9,10] and cutting parameter optimization [9,11,12], since these two strategies are the most effective. On one hand, different cooling/lubricating systems can be applied in machining, such as flood, air, minimum quantity lubrication (MQL), cryogenic cooling, hybrid cooling (cryogenic and MQL) and high-pressure jet-assisted machining (HPJAM) systems [13,14,15,16,17]. Near-dry cooling techniques can increase tool life, improve surface roughness and remove flood cooling systems, which are well known to be harmful to health operators and the environment. Dry machining is the best option for machining sustainability in relation with cooling/lubricating systems, but it is only considered acceptable by manufacturers when it ensures at least equal product quality and material removal rate as those obtained in wet machining [18]. Currently, dry machining is still challenging in terms of cutting tool life and workpiece quality, except for some specific applications, such as machining cast iron [19]. On the other hand, for a given machining scenario where only the cutting parameters may be modified, their optimization may provide an important improvement in terms of quality, tool life and productivity, and thus, in sustainability [11].
Apart from these sustainability strategies, other works are more focused on the energy consumption of machine tool components (e.g., hydraulic units, lightening and basic module run) and their cutting and idle/steady state. Under this approach, different strategies, such as the implementation of lightweight components [20], the adjustment of motors to the machine cycle requirements or switch-off and the reduction in warm-up tactics [21], have been studied to reduce cost and energy consumption and increase sustainability without compromising their functionality, usability, productivity, accuracy, etc. [22], and some of them have been recently reviewed [23,24].
The literature presents extensive work in machining, surface roughness and sustainability according to a detailed search in reputable databases (Web of Science Core Collection): during 2023, 93 papers were published in comparison to 76 (2022), 61 (2021) and 58 (2020) papers in previous years, which provides a groundwork for a fundamental study on how surface generation influences sustainability outputs. The research trend states that there is a growing interest in exploring lubrication strategies with nanofluid or nanoparticle-assisted minimum quantity lubrication [25,26,27,28] and vegetable oil-based fluid [29] or mitigating carbon footprint while improving surface characteristics, among other eco-friendly green approaches [30]. Other trends include integrating cutting-edge preparations in cutting tool development [31] using different combinations of substrate and coating materials [32], and incorporating ultrasonic vibration into machining strategies [33], among others. Nevertheless, if one is focused on finishing operations in milling and turning operations, there exist many decision variables that can improve sustainability. Simple aspects, such as the proper type of tool in terms of geometry and material, can lead to higher productivity within admissible surface roughness values, improving sustainability by reducing costs and energy consumption. As a result, it is clearly shown that a correct understanding of all factors that are related to surface roughness generation is of great importance to apply more effective and sustainable machining practices.
This work presents a review of the factors that affect surface roughness generation in machining (turning/milling) processes and the main research works where those factors are analyzed. The factors are classified in three categories and five subcategories in order to facilitate the identification of the factors and their relationship since, due to the complexity of surface roughness generation, some factors have a more direct influence than others. The three identified categories are setup factors (cutting tool, machine tool/fixturing and workpiece factors), operational factors (cutting and process parameters) and processing factors (built-up edge, chatter, tool wear, etc.). To illustrate the importance of understanding surface roughness generation to improve machining sustainability, three case studies are shown. As presented in the case studies, correct decisions in insert geometry selection, cooling systems or tool replacement and cutting parameter selection may provide a high reduction in kg of CO2 equivalent. The research work presented may be of interest to practitioners in the machining industry who want to improve machining sustainability in conventional processes. Note that this research work is limited to finishing operations in conventional machining processes, such as milling and turning processes.
This paper is organized as follows: Section 2 shows the proposed classification of the factors that influence surface roughness. Section 3 reviews the effects of the identified factors according to the relevant research conducted in the literature, providing relevant information and graphical data about experimental relations among surface roughness and different factors. Section 4 presents three case studies to illustrate the potential improvement in machining sustainability by properly working on some of the factors identified. Finally, Section 5 concludes the paper.

2. Classification of Factors

According to the literature review, the factors that influence surface roughness generation may be classified into three main categories: setup factors, operational factors, and processing factors.
Setup factors refer to the factors defined at the process-planning level. These factors may have a very important impact on surface roughness, and they are kept constant during machining; thus, only the interaction of these factors with the generation of different cutting phenomena may produce different roughness values during production. Three subcategories may be identified within the setup factors: cutting tool, machine tool/fixturing and workpiece factors.
Operation factors refer to the parameters at the machine tool level that can be modified on the spot by either changing the settings or the NC program on the machine tool. These factors are divided into two subcategories: cutting parameters and process parameters. Cutting parameters include parameters such as the cutting speed, feed rate or depth of cut, and process parameters are related to the coolant, cutting strategies and so on.
Processing factors refer to the factors related to specific cutting phenomena that influence the surface roughness generation. When they appear, these factors have a clear contribution to the final surface roughness. These factors are tool wear, vibrations/chatter, built-up edge, plastic side flow and back cutting.
It should be noted that this classification facilitates the understanding of surface roughness generation in terms of independent and dependent variables. Setup and operational factors are independent, and they are defined according to the process planner and/or machining operators. However, processing factors are dependent on both setup and operational factors. For instance, vibrations as a processing factor will impact the surface roughness, but the importance of vibrations as a processing factor will depend on previous factors such as the geometry of the insert, dynamics of machine tool/workpiece/fixturing, cutting parameters, etc., and it will also depend on other processing factors such as tool wear or built-up edge. Figure 1 illustrates this classification and shows the relationships between them. The explanation of each factor and its impact on surface roughness is detailed in the next section.
Figure 1. Factors that influence surface roughness generation.

4. Linking Surface Roughness and Sustainability: Case Studies

Machining sustainability has been widely studied, and many different strategies have been proposed to reduce resource consumption and environmental impact. The most common approaches to improve machining sustainability are related to cooling/lubrication systems and cutting parameter optimization to reduce CO2 generation [10,12]. Although many other aspects have been studied to improve machining sustainability, less attention has been paid to the factors related to surface roughness generation, which are directly related to the quality output of the process. Therefore, by acting on these factors, a specific surface roughness can be ensured while minimizing the consumption of resources.
Three levels of actions may be identified: basic, intermediate and advanced. Basic actions are related to changes in setup factors, which may be conducted in a straightforward manner. For instance, these actions may include the use of proper cutting inserts that allow for higher material removal rates while keeping a good surface roughness; the use of tool materials that reduce the adhesion of the material, improving the surface roughness and avoiding lower material removal rates and scraps; the use of inserts with proper edge preparation that allow for lower surface roughness rates at higher production rates, etc. Note that, in general, high material removal rates lead to lower specific cutting energies, i.e., a lower kJ per volume of material is removed, which means less kg of CO2 equivalent.
The intermediate level would refer to changes in the operation factors that may require a more in-depth study. For instance, this may include changes in the coolant/lubricating systems or the optimization of cutting parameters, which may require experimental data or some tests to set the operating parameters and ensure benefits are obtained from the change.
Finally, the advanced level would refer to the implementation of systems that can monitor the processing factors and thus act properly on the system to optimize the use of resources while ensuring surface roughness specification. This level may be related to the use of sensors and monitoring systems that can estimate the influence of processing factors, such as tool wear, vibrations and built-up edge generation, and then adapt the cutting conditions or call for an early tool replacement in order to optimize the machining performance and avoid the need for scraps or parts to be reworked.
To illustrate these levels of acting and how studying and acting on surface roughness factors can notably improve machining sustainability, three examples are provided in this section. First, an example is shown where only a change in the type of cutting tool insert, from a conventional insert to a wiper insert geometry, produces a notable impact on productivity, reducing power consumption and, thus, CO2 generation. Secondly, a change in the coolant system is highlighted as a straightforward method to improve sustainability by increasing tool life and productivity with CO2 reduction. Finally, a third example shows the high impact that could be achieved if grinding processes can be replaced or minimized when the surface roughness can be controlled during conventional machining operations. It should be mentioned that the second and third examples are extracted from a previous research work by the authors in the mold manufacturing sector, and detailed information can be found in [136].

4.1. Insert Change Geometry

A straightforward improvement in machining sustainability can be achieved by using wiper inserts in milling operations. As shown above, the edge geometry of wiper inserts allow for the feed rate to be increased while the surface roughness remains low in comparison with conventional inserts. To illustrate the impact on machining sustainability, consider the example shown in [137], where the use of two different inserts, namely a wiper insert and a conventional one, is studied in hard turning operations in AISI 4340 steels (Figure 14). In this work, the estimation of the surface roughness for each insert is explicitly stated as follows:
R a w i p e r = 0.518 + 0.21 × f n + 0.076 × V c + 0.032 × a − 0.017 × f n × V c − 0.007 × f n × a − 0.008 × V c × a − 0.011 × f n 2 − 0.029 × V c 2 − 0.005 × a 2 ,
R a c o n v e n t i o n a l = 1.431 + 0.589 × f n − 0.147 × V c + 0.085 × a − 0.215 × f n × V c − 0.024 × f n × a − 0.039 × V c × a − 0.026 × f n 2 − 0.127 × V c 2 − 0.017 × a 2
Figure 14. Conventional (a) and wiper inserts (b) analyzed in [137] and applied in case study 1. Original figure from [137] under Creative Commons CC BY 4.0 license.
As can be seen in Figure 15, the evolution of the surface roughness is different for both inserts, and the wiper insert presents lower roughness values. Thus, replacing the conventional insert with the wiper insert and increasing the feed rate is a straightforward solution to reduce power consumption and the kg of CO2 equivalent. The power consumption in machining operations is estimated as follows [138]:
P m a c h i n i n g = P c + P i d l e
where Pidle refers to the power consumption of the machine tool in its idle state and Pc is the cutting power required in the operation, which includes the energy for material removal, the energy for moving the machine tool table and the energy for rotating the spindle. Considering only the material removal energy, the cutting power is estimated as follows [34]:
P c = Q × k c 60 × 10 3
Figure 15. Surface roughness for conventional and wiper inserts for different cutting parameters according to regression models in [137].
In this expression, Q refers to the material removal rate, calculated as follows [34]:
Q = V c × a × f n
kc is the specific cutting force that is estimated as follows [34]:
k c = k c 1 × 1 h m m c × 1 − γ o 100
The value of kc1 refers to the specific cutting force when cutting an undeformed chip thickness of 1 mm, hm is the undeformed chip thickness, and γ0 is the rake angle of the insert. The undeformed chip thickness depends on the feed per revolution fn and the entering angle κ according to the following expression [34]:
h m = f n × sin ⁡ κ
However, since the feed rate is higher under wiper inserts, the total energy consumption is lower when using wiper inserts. Energy consumption, which is directly related to the kg of CO2 emission, depends on the total machining time, and thus, it can be estimated as follows:
E c = P c × t c
For the sake of simplicity, the machining time, tc, is expressed by only considering the cutting movements, and thus, it can be defined as follows:
t c = L c N × f n
where Lc is the length of the cut in mm, and N is the spindle speed in rev/min. To illustrate the impact of the change in the cutting tool geometry on the energy consumption, consider that the hard turning process is conducted at 120 m/min and 0.05 mm/rev with the conventional insert. The same surface roughness quality can be achieved at the same cutting speed but by increasing the feed rate up to 0.2 mm/rev according to the roughness models shown in Equations (1) and (2) and Figure 15. Both cutting inserts present an entering angle of 45°, a rake angle of 6° and a specific cutting force (kc1) of 2000 N/mm2 [34]. Under these values, the reduction in energy consumption by using wiper inserts instead of conventional inserts is shown in Figure 16 as a function of the idle power consumption of the machine tool. As can be seen, since the feed rate is increased from 0.05 to 0.2 mm/rev, the time required for machining the same number of parts in a batch is reduced by up to 75%. However, although higher feed rates reduce the specific cutting force, the chip thickness is higher, and thus, the cutting forces and power consumption are higher when wiper inserts are applied. Taking into consideration the power consumption at the idle state, the evolution of the reduction in kg of CO2 for this case study is presented in Figure 16, where the highest reduction is given at higher power consumptions at the idle state, with a maximum value related to the increase in productivity, a 75% value.
Figure 16. Reduction in kg of CO2 equivalent when replacing conventional inserts by wiper inserts depending on the machine tool power consumption at its idle state. Squares refer to computed data from the equations.
It should be noted that the efficiency of the insert geometry change in terms of sustainability depends on the tool wear evolution on the wiper insert, which has been assumed to be the same for both wiper and conventional inserts. The same authors in a subsequent work reported that the wiper inserts presented a similar tool wear behavior than the conventional ones [139]. However, it should be noted that other research works present very different tool wear behaviors, with some of them even showing that for specific operations, the wiper inserts form a built-up edge, which decreases their tool lives notably with respect to conventional tools [140]. Furthermore, even when assuming that wiper and conventional inserts present similar tool wear evolution under the same cutting operations, it should be noted that wiper inserts are used at higher feeds to increase productivity, which is likely to reduce their tool lives. This is shown in the well-known extended Taylor’s expression [3]:
T l i f e = C V c n 1 × f z n 2
where C, n1 and n2 are coefficients that depend on a particular tool–workpiece combination. Therefore, to consider the effect of an increase in the tool wear when using wiper inserts, the energy consumption equation shown in Equation (8) is changed as follows:
E c = P c × t c + E i n s e r t × n i n s e r t s ,
where Einsert is the energy required to manufacture a carbide insert in kJ, which is estimated as 1500 kJ [11], and ninserts is the number of inserts used for machining a batch, calculated as follows:
n i n s e r t s = N p a r t s × t c T l i f e × n e d g e s
In the above expression, Nparts refers to the total number of parts in a batch, and nedges is the number of edges that can be used per insert. Considering the reduction in tool life when a wiper insert is used, Figure 17 shows how the reduction in energy consumption (i.e., kg of CO2 equivalent) evolutes as a function of the ratio between the conventional insert’s tool life under low feed rates and the wiper insert’s tool life under higher feed rates. As can be seen, even if the reduction in tool life is very high, for instance, a ratio of 0.25, which means that the wiper insert’s tool life is one-fourth the conventional insert’s tool life, the reduction in energy consumption is very significant in the case that power consumption at the idle state is not negligible (values around 35% reduction if the idle consumption is 0.5 kW, which is a conservative assumption for most machine tools), which remarks the importance of increasing the feed rate to reduce the cutting time while keeping low surface roughness values under the use of wiper inserts.
Figure 17. Reduction in kg of CO2 equivalent when replacing conventional inserts with wiper inserts depending on the ratio of wiper insert tool life and conventional insert tool life.
Summary: Changing a conventional insert to a wiper insert multiplies the material removal rate by a factor of 3 or 4 while maintaining the surface roughness quality. The use of wiper inserts notably reduces the CO2 equivalent generation, and it can easily reach reductions of 30–50% if the wipers do not show built-up edge formation or other issues.

4.2. Lubricating Change

Another change in the machining process that can notably improve machining sustainability is the change in the cooling/lubricating system. As an example, consider the machining of the punch part shown in [136] related to mold manufacturing (Figure 18). A common approach to machine this part made of DIN C45 steel is conventional milling with air cooling to remove chips and avoid the effect of thermal fatigue and cracking due to flood cooling. However, the potential use of an MQL system can increase tool life and surface quality, as presented in [136]. The air-cooling strategy is conducted under a cutting speed of 160 m/min and a feed per tooth of 0.4 mm. The introduction of an MQL system with 15 mL/h of lubricant consumption allowed the cutting speed to be increased to 200 m/min and the feed rate to be increased to 0.5 mm/tooth while keeping a similar tool life and surface roughness. In this example, the cutting tool used is a face cutter of 52 mm in diameter with five octagonal/round carbide inserts, and the rest of the cutting parameters for both scenarios are a radial depth of cut that is 75% of the diameter and an axial depth of cut of 1 mm.
Figure 18. Typical mold applied in manufacturing tile lines. Courtesy of Macer SL. The machining process of the part “upper punch” is analyzed in [136], and the data are used for case studies 2 and 3.
For this milling application, the material removal rate is calculated as follows [34]:
Q = a e × a p × v f
where ae is the radial depth of cut, ap is the axial depth of cut, and vf is the feed rate of the cutting tool in mm/min. By applying the same equations as those used for the turning operations in case study 1, the power consumption is evaluated in a straightforward manner. In this example, the power consumption under the MQL system is given by a linear regression as follows [136]:
P c o n s = 6127 − 0.4214 × V c − 3616 × f z + 83.13 × V c × f z
Therefore, the cutting power consumption under the MQL operation is 12.55 kW, while for air cooling, the power consumption, assuming a similar behavior, can be estimated as 9.9 kW. For the batch studied in [136], the time required for machining under air cooling is 3207 min, whereas the time needed under the MQL system is 2052 min. As a result, the application of the MQL system reduces the total kg of CO2 equivalent by 36% at the milling stage, assuming that the differences in energy consumption from the air-cooling system and the MQL system are negligible. Note that the research work in [136] states a reduction of 17% in kg of CO2 equivalent in the punch manufacturing process since it is considered to consume both milling and grinding power.
Summary: A change in the lubricating systems from dry/air cooling to MQL systems can easily improve the surface roughness, which may lead to higher removal rates. Thus, the MQL used may lead to a significant reduction (15–30%) in kg of CO2 equivalent.

4.3. Feed Rate Optimization and Early Tool Replacement to Eliminate Secondary Operations

A more efficient strategy to improve machining sustainability can be conducted if surface roughness monitoring is possible, which can lead to the control of the surface roughness to ensure a specific quality requirement and reduce or even replace expensive grinding operations. If the surface roughness can be assumed to be a repeatable process, where surface roughness evolves over time, due to the natural wear process of the insert being relatively known and stable, a roughness monitoring system may be substituted with an early tool change replacement strategy to ensure surface roughness specifications.
An example is provided in the same case study presented by the authors in [136]. The punch part presented above requires, in addition to milling, a grinding process to ensure a surface roughness lower than 1.5 µm is obtained. Two options are compared with the same MQL lubricating system, namely one with a maximum removal rate during milling with a subsequent grinding operation, and a second option where the surface roughness is controlled under proper cutting parameters and an early tool change replacement strategy, which avoids the final grinding step. In this case, the cutting parameters are obtained by minimizing a function cost, which is a costing function. For the first option, the milling operation is set up with the parameters of a cutting speed of 200 m/min and a feed rate of 0.5 mm/tooth. For the second option, the cutting parameters are set up to 200 m/min and 0.15 mm/tooth for the cutting speed and the feed rate, respectively, and an early tool change strategy is set to 40% of the tool life for the first option, which is equivalent to change the tool at approximately 0.15 mm of tool flank wear. Although the productivity at the milling stage decreases with an important cost of cutting tools, the elimination of the grinding process highly improves the global sustainability of the process. The power consumption at the grinding process is estimated as follows [141]:
P g r i n d i n g = E s × Q g + P i d l e
where Es is the specific energy for grinding, which can be estimated as 40 J/mm3 according to [141], Qg is the material removal rate at grinding and Pidle is the power consumption of the grinding machine at the idle state. By analyzing the kg of CO2 equivalent due to the total power consumption from both the milling and grinding processes and the kg equivalent of CO2 related to cutting tools (1.5 MJ/insert for carbide inserts and 50 MJ/kg for aluminum oxide for grinding wheels), the second option with an early tool replacement leads to a reduction of 67% in kg of CO2 equivalent. Furthermore, the elimination of the flood cooling at the grinding state contributes to 3357 L of water being saved annually, although the MQL system, which is set up to 50 mL/h, requires the consumption of 6.1 L of vegetable oil per year.
Summary: A critical step in machining sustainability is the elimination of secondary operations. The implementation of early tool changes, cutting parameter optimization or online monitoring systems may ensure part quality without costly and unsustainable grinding operations. If correctly applied, high reductions (40–60%) in kg of CO2 equivalent and water consumption can be achieved.

5. Conclusions

The interplay between surface roughness and sustainability in machining processes has emerged as a critical focal point in contemporary manufacturing. As industries increasingly pivot towards environmentally conscious practices, optimizing surface roughness not only enhances product quality but also aligns with sustainable objectives by reducing material waste, energy consumption and environmental impact.
This work presented a review of the factors that affect surface roughness generation in machining (turning/milling) processes. Up to twenty-five different factors were identified, which were classified as setup factors (cutting tool, machine tool/fixture and workpiece factors), operational factors (cutting and process parameters) and processing factors (those related to the resulting cutting process, such as built-up edge, chatter or tool wear).
The importance of understanding surface roughness generation to improve machining sustainability was presented under three case studies. The first case showed a reduction of approximately 75% in CO2 equivalent by using a wiper insert instead of a conventional insert in hard turning. The second case study illustrated a reduction of 35% in kg of CO2 equivalent through a change from air cooling to MQL cooling in milling a punch in the tile mold industry. The third case showed the high impact that can be achieved if grinding processes can be replaced by controlling the surface roughness in previous milling/turning operations. In the presented case, an improvement of 67% in kg of CO2 equivalent and the elimination of water consumption required in flood cooling were obtained by optimizing the cutting operations and using an early tool change replacement instead of conventional milling and subsequent grinding operations.
The future trends in sustainable machining seek to optimize manufacturing resources while ensuring part specifications at both the macro and micro geometric levels. The major research directions in this field are as follows.
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The design of special geometries in cutting inserts. Different edge preparation techniques and textures on rake faces are being actively investigated to reduce the friction coefficients and cutting temperatures. Textures with different sizes and patterns are being machined using a laser or other methods and subsequently tested to improve machining performance.
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Advanced cooling and lubrication systems. Cooling/lubrication systems are bring continuously improved to reduce cutting temperatures and tool wear and limit their impact on surface roughness. Intensive research has been conducted on MQL systems with nanofluids (vegetable oils with small percentages of dispersed nanoparticles, such as carbon nanotubes, molybdenum disulfide or silica nanoparticles), especially in the field of machining difficult-to-cut materials such as titanium or nickel-based alloys.
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Advanced monitoring systems. The implementation of the industrial internet of things (IIOT) facilitates the development of monitoring systems that can apply machine learning or other artificial intelligent approaches to provide optimum machining performances. Under these systems, multi-objective optimization strategies that simultaneously consider diverse performance metrics, including surface roughness, energy consumption, tool wear, and environmental impact, can be deployed.

Author Contributions

Conceptualization, J.V.A.-N., C.V.P. and H.R.S.; methodology, J.V.A.-N.; software, J.V.A.-N.; validation, J.V.A.-N., C.V.P. and H.R.S.; formal analysis, J.V.A.-N.; resources, J.V.A.-N.; data curation, J.V.A.-N.; writing—original draft preparation, J.V.A.-N., C.V.P. and H.R.S.; writing—review and editing, J.V.A.-N., C.V.P. and H.R.S.; visualization, J.V.A.-N.; supervision, J.V.A.-N.; project administration, J.V.A.-N.; funding acquisition, J.V.A.-N. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Universitat Jaume I Institution, grant number P1-1B2015-53 and UJI-B2020-33.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

Data sharing is not applicable.

Conflicts of Interest

The authors declare no conflicts of interest.

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