Monitoring coated carbide tool wear via chip analysis in hightemperature machining

ABSTRACT

Stainless steels are pivotal materials in industry and find extensive application in various types of equipment due to their excellent chemical properties, such as high corrosion resistance and the ability to withstand elevated temperatures. However, they exhibit greater machining challenges compared to common carbon and low-alloy steels, mainly because of their high work-hardening rate during cutting operations. Consequently, the industry has a strong interest in understanding and monitoring machining techniques for these materials. This study introduces a novel approach by correlating tool wear progression with chip morphology in the dry turning of SAE 304 stainless steel, using coated carbide tools. Wear tests were carried out, and chip samples were collected at each stage of tool wear. Analyses of chip type and shape were performed, followed by metallographic evaluations. The results indicate a measurable correlation between wear stages and chip morphology, suggesting that chip analysis can serve as a practical method for real-time tool condition monitoring.This finding provides a cost-effective alternative for tool wear assessment, enhancing the efficiency and reliability of machining operations involving austenitic stainless steels.

Keywords:
Austenitic stainless steel (SAE 304); Tool wear progression; Dry turning operations; Chip morphology analysis; High-temperature machining

1. INTRODUCTION

Stainless steels play a crucial role in numerous industrial applications due to their corrosion resistance, high durability, and aesthetic appeal [¹]. Nevertheless, their utilization poses significant challenges that must be overcome to maximize their technical and economic potential [²].

The presence of alloying elements such as nickel, chromium, and molybdenum in these materials considerably increases their cost relative to carbon steels [³].

This expense can jeopardize the economic feasibility of FEA projects requiring large volumes of material or operating in highly budget-sensitive sectors, such as machining [⁴].

According to MACHADO et al. [⁵], these materials exhibit low thermal conductivity and a high tendency for work-hardening, which complicates operations like cutting, drilling, and milling. TRENT and WRIGHT [⁶] argue that the high temperatures generated during machining can lead to deformations or rapid tool wear, thereby increasing production time and costs. Stainless steels also tend to produce long chips that accelerate tool wear [⁷]. According to PRAMANIK et al. [⁸], the workpiece material and its alloy composition are the most influential factors in determining the type of chips formed.

SHARMA et al. [⁹] investigated the surface integrity of SAE 304 under various manufacturing processes, including machining. MUSHTAQ et al. [¹⁰] examined the machinability of SAE 304 steel under different heat treatments; results were compared to samples without heat treatment, and the authors concluded that such treatments improved this material’s machinability. ROY et al. [¹¹] investigated the machining of stainless steel alloys under various lubrication conditions. The study considered multiple evaluation criteria, including surface roughness, cutting temperature, tool wear, and energy consumption.

JAYAGANTH et al. [¹²] and KUMAR et al. [¹³] studied the influence of different carbide-tool coatings on machining SAE 304 steel. SUBHEDAR et al. [¹⁴] explored the potential of Al₂O₃ particles in mineral-based cutting fluids for machining SAE 304 stainless steel. KHAN et al. [¹⁵] concluded, through machining simulations and microstructural analysis of SAE 304, that the addition of manganese sulfide (MnS) enhances friction conditions and increases the tool’s wear resistance at the workpiece-tool interface. MAO et al. [¹⁶] described a constitutive model for analyzingthe forces involved in themachining processes of SAE 304 steel.

Several tool wear monitoring techniques have been explored in previous studies to ensure process reliability and surface quality during machining. Acoustic emission sensors, for instance, have been widely used due to their high sensitivity to microstructural changes and crack initiation during tool wear progression [¹⁷,¹⁸,¹⁹]. JESSEL et al. [²⁰] proposed an approach for tool condition monitoring using acoustic emission to detect wear in machining tools. NAKANO et al. [²¹] introduced an innovative method for tool condition monitoring during machining operations, focusing on anomaly segmentation techniques. STECKEL et al. [²²] investigated the use of ultrasonic sensors combined with convolutional neural networks to predict tool wear in CNC turning operations. MAIA et al. [²³] explored the application of acoustic emission combined with Short-Time Fourier Transform for real-time tool wear monitoring during machining operations.

Force monitoring is another common approach, as variations in cutting forces can reflect changes in tool condition and provide early signs of wear [²⁴,²⁵,²⁶]. Thermal imaging, often employed via infrared cameras, allows non-contact monitoring of temperature changes in the cutting zone, which can be correlated with wear intensity and cutting performance [²⁷]. Compared to these methods, chip-based analysis, such as chip morphology, color and root deformation, offers a simpler, cost-effective, and real-time alternative. While not yet as widely adopted as sensor-based systems, chip observation requires no additional instrumentation and can be integrated visually on the shop floor, making it a promising complementary approach for wear detection and thermal evaluation.

Furthermore, compared to other non-intrusive techniques like AE, force monitoring, and thermal imaging, chip-based analysis offers distinct advantages in terms of simplicity and cost-effectiveness. While these sensor-based methods are effective, they often require specialized equipment and can be influenced by external factors, making their implementation more complex in certain industrial environments.

Determining tool wear levels using a monitoring method that does not require interrupting the cutting process could offer a viable way to reduce costs and improve productivity. Therefore, this study aims to establish the relationship between the morphology and temperature of chips produced in external cylindrical turning of austenitic stainless steel ABNT 304 at various levels of flank wear on coated carbide tools.

2. EXPERIMENTAL METHOD

The test specimens used in this work were made of SAE 304 stainless steel. This material is an iron-chromium-nickel alloy featuring good mechanical strength and excellent resistance to various atmospheric conditions [⁹]. It also exhibits high corrosion resistance, making it predominantly suitable for use in the food industry (sinks, cookware and food processing equipment), chemical industry (tanks and piping for corrosive substances), civil construction (handrails, architectural structures and facade cladding), and the medical sector (manufacturing surgical instruments and hospital equipment) [²⁸,²⁹,³⁰].

In the metalworking industry, these materials are typically used for connecting rods, shafts, rams, axles, screws, bolts, spindles, ratchets, worms, gears, and guide rods [³¹]. Table 1 shows the chemical composition of this material, and Table 2 presents some of its mechanical and physical properties [³²].

Table 1
Chemical constituent of SAE 304alloy [9].

Element (wt. %)	Ni	Cr	FE	C	Mn	Si	S	P	Ni
Min.	8	17.5	Bal.	–	–	–	–	–	–
Max.	10	19.5	–	0.07	2	1	0.02	0.4	0.11

Table 2
Physical and mechanical properties of the SAE 304 alloy [32].

Properties	Specification
Density	~8.00 g/cm3
Melting point	~1450 °C
Hardness	~70 HRC
Thermalconductivity	~16.2 W/Mk
Modulusofelasticity	~193 GPa
Tensilestrength	540–750 MPa
Elongation	~45%

For the chip analysis, samples were embedded in resin, ground with water-based abrasive papers (#320, #400, #600 and #1200 mesh), polished with 3 μm diamond paste, and etched using 2% Nital for 5 seconds, in accordance with [³³,³⁴,³⁵]. This preparation included perpendicular cross-lapping of the grinding tracks, following [³⁶]. Images of the chips were taken using a Sony Cyber-shot camera, and photomicrographs were captured with a Shimadzu microhardness tester connected to a computer.The chip thickness was measured using a digital micrometer (resolution 0.01 mm, Mitutoyo, Japan) at three different points along each chip, and the average value was taken as the representative thickness. The measurement procedure is illustrated in Figure 1a. All measurements were performed at a constant temperature of 25 °C.

Figure 1
(a) Measurement procedure of chip thickness; (b) Cutting tool used in the experiments [³⁷].

The tools employed in the tool life and cutting temperature tests were SNMG 120404-MF 431 carbide inserts with eight cutting edges, manufactured by Sandvik (Sweden) and coated with TiN. These inserts have a square geometry with a negative rake angle, allowing four cutting edges on the top surface and four on the bottom, totaling eight usable edges. The flank wear (V_B) was measured on each individual cutting edge used, and a new edge was employed for each test condition to ensure consistency. According to the manufacturer’s specifications, the hardness of the tool substrate is approximately 1800 HV, while the TiN coating is approximately 2400 HV (Figure 1). According to SANDVIK [³⁷], these tools are recommended for finishing to medium roughing operations. The tool holder used bears the code PSSNR 2020 K12 and is also manufactured by Sandvik (Sweden).

The wear tests were conducted on a Deb’Maq Revolution 220 lathe, powered at 7.5 hp (Germany). Tool wear was monitored and measured using an Olympus stereo microscope (Japan) in conjunction with the Image-Pro analysis software. The measurement of flank wear (V_B) was carried out following the guidelines of ISO 3685:1993 (Tool-life testing with single-point turning tools). According to this standard, the width of the flank wear land (V_B) was measured at the tool’s major cutting edge, considering the average of three readings evenly distributed along the worn region. Figure 2 illustrates the cutting tool after the machining process has been included in the manuscript to demonstrate the wear characteristics observed.In addition, a scanning electron microscope (SEM) was employed to analyze the chip’s microstructure. Quick-stop tests were conducted using an IMOR MAX II 520 mechanical lathe (Brazil).

Figure 2
Cutting tool after machining process showing flank wear (V_B).

An infrared sensor (Raytek MI3, USA; spectral response: 8–14 µm, temperature range: –40 to 500 °C, accuracy: ±1%, response time: 150 ms) was utilized for measuring the cutting temperature. The sensor was positioned with a direct line of sight to the tool–chip interface to ensure reliable thermal readings during the machining process. Data acquisition and analysis were conducted using the RaytekDataTemp Multidrop software. Figure 3 illustrates the experimental setup, highlighting the position of the infrared sensor relative to the tool and workpiece. The infrared sensor lens was positioned 80 mm from the tool’s exit surface, optimizing the sensor’s focal adjustment.

Figure 3
Setup of the cutting temperature measurement system.

Five cutting speeds were selected for this study: 130, 140, 150, 160 and 170 m/min. These speeds were chosen to cover a broad range of typical machining conditions that are representative of industrial turning operations. The selection aimed to investigate the impact of cutting speed on tool wear progression and chip formation at varying thermal and mechanical loads. For each cutting speed, one main test and two repetitions (as three runs per cutting speed) were performed, totaling 15 experiments. To ensure the consistency and reliability of the results, each experiment was conducted using a fresh cutting edge. Consequently, 15 distinct cutting edges were utilized throughout the study. Given that the inserts used in this work feature eight usable edges each, only two inserts were required to complete all tests. It is also important to note that, during each of the 15 experimental runs, tool wear stages were monitored and evaluated progressively on the single cutting edge used for that run, allowing for the controlled observation of wear evolution over time.To support the statistical validity of the experimental results, Analysis of Variance (ANOVA) was applied to evaluate the significance of differences observed between wear stages and cutting conditions.

3. RESULTS AND DISCUSSION

Figure 4 illustrates the variation in chip thickness under different tool wear levels and cutting speeds. A general trend of increasing chip thickness with greater tool wear is evident. This behavior is consistent with the findings of MACHADO et al. [⁵], who reported that tool wear modifies the cutting wedge geometry and increases the contact area between the chip and the tool, leading to higher cutting forces. According to TRENT and WRIGHT [⁶], increased tool wear also intensifies deformation in the primary shear zone and raises energy dissipation in the secondary shear zone. These factors contribute to a reduction in shear angle, resulting in thicker chips.

Figure 4
Chip thickness behavior in relation to tool wear.

The present results demonstrate a strong correlation between growing flank wear and both chip thickness and temperature during the dry turning of SAE 304 stainless steel. As shown in Figure 4, chip thickness consistently rises with advancing wear, supporting theoretical expectations that worn tools alter the cutting geometry and increase mechanical loads [⁵]. Statistical data presented in Tables 3 and 4 further validate this trend, highlighting significant percentage increases in chip thickness, especially when transitioning from mild to severe wear at the highest cutting speed (170 m/min).

Table 3
Average percentage difference in chip thickness.

PERCENTUAL DIFFERENCE (%)
DIFFERENCE	vc = 130 m/min	vc = 140 m/min	vc = 150 m/min	vc = 160 m/min	vc = 170 m/min
VB = 0 mm → VB = 0.1 mm	+ 15.38	+ 22.92	– 14.29	– 1.96	– 33.33
VB = 0 mm → VB = 0.2 mm	– 7.68	+ 2.08	– 10.20	– 17.65	– 25.93
VB = 0 mm → VB = 0.3 mm	– 15.38	+ 8.33	– 2.04	– 9.80	– 7.43
VB = 0 mm → VB = 0.4 mm	– 11.54	+ 6.25	+ 4.08	– 13.73	– 3.71
VB = 0 mm → VB = 0.5 mm	+ 19.23	+ 20.83	+ 22.45	+ 23.53	+ 3.71
VB = 0.1 mm → VB = 0.2 mm	– 20.00	– 16.95	+ 4.76	– 16.02	+ 11.11
VB = 0.1 mm → VB = 0.3 mm	– 26.67	– 11.86	+ 14.29	– 8.01	+ 38.89
VB = 0.1 mm → VB = 0.4 mm	– 23.33	– 13.56	+ 21.43	– 12.36	+ 44.44
VB = 0.1 mm → VB = 0.5 mm	+ 3.33	– 1.69	+ 42.86	+ 26.03	+ 55.56
VB = 0.2 mm → VB = 0.3 mm	– 8.33	+ 6.12	+ 9.09	+ 9.52	+ 25.01
VB = 0.2 mm → VB = 0.4 mm	– 4.17	+ 4.08	+ 15.91	+ 4.76	+ 25.05
VB = 0.2 mm → VB = 0.5 mm	+ 29.17	+ 18.37	+ 36.36	+ 50.02	+ 40.01
VB = 0.3 mm → VB = 0.4 mm	+ 4.55	– 1.92	+ 6.25	– 4.35	+ 4.03
VB = 0.3 mm → VB = 0.5 mm	+ 40.91	+ 11.54	+ 25.01	+ 36.96	+ 7.69
VB = 0.4 mm → VB = 0.5 mm	+ 34.78	+ 13.73	+ 17.65	+ 43.18	+ 7.77

Table 4
Statistical analysis of chip thickness differences.

P-VALUE
DIFFERENCE	vc = 130 m/min	vc = 140 m/min	vc = 150 m/min	vc = 160 m/min	vc = 170 m/min
VB = 0 mm → VB = 0.1 mm	0.1216	0.0544	0.1955	0.8354	0.0162
VB = 0 mm → VB = 0.2 mm	0.3827	0.8185	0.3297	0.1167	0.0361
VB = 0 mm → VB = 0.3 mm	0.1216	0.3827	0.8354	0.3297	0.4252
VB = 0 mm → VB = 0.4 mm	0.2156	0.5032	0.6803	0.1955	0.6803
VB = 0 mm → VB = 0.5 mm	0.0705	0.0705	0.7138	0.0563	0.5423
VB = 0.1 mm → VB = 0.2 mm	0.0424	0.0905	0.6803	0.1507	0.4252
VB = 0.1 mm → VB = 0.3 mm	0.0173	0.1955	0.2541	0.4252	0.0361
VB = 0.1 mm → VB = 0.4 mm	0.0266	0.1507	0.1167	0.2541	0.0238
VB = 0.1 mm → VB = 0.5 mm	0.6499	0.8354	0.0162	0.0449	0.0114
VB = 0.2 mm → VB = 0.3 mm	0.3827	0.5423	0.4252	0.4252	0.0908
VB = 0.2 mm → VB = 0.4 mm	0.6499	0.6803	0.1955	0.6803	0.0563
VB = 0.2 mm → VB = 0.5 mm	0.0266	0.1167	0.0238	0.0096	0.0238
VB = 0.3 mm → VB = 0.4 mm	0.6499	0.8354	0.5423	0.6804	0.6803
VB = 0.3 mm → VB = 0.5 mm	0.0116	0.2541	0.0563	0.0196	0.2541
VB = 0.4 mm → VB = 0.5 mm	0.0173	0.1955	0.1167	0.0135	0.4252

Cutting speed was not as decisive a factor in chip thickness as tool wear. In austenitic stainless steels, cutting forces generally tend to decrease as speed increases, as seen in medium-carbon steels [⁶]. At high speeds, such as those used in this test, cutting forces remain almost constant, which explains why the chip thickness did not vary significantly.

Although cutting speed can influence thermal and mechanical aspects of machining, its effect in this study was relatively minimal compared to the impact of tool wear. As previously noted by TRENT and WRIGHT [⁶], higher speeds tend to stabilize cutting forces in austenitic steels, which explains the less variation in chip thickness at different speeds.

Tables 3 and 4 respectively present the average percentage differencesin chip thickness and the statistical significance of these differences under different tool wear conditions. Positive values in Table 3 indicate an increase in thickness, while negative values indicate a decrease. Statistically significant results at the 95% confidence level are shown in bold (Table 4).

Among all analyses presented in Table 3, the comparison “V_B = 0.1 mm → V_B = 0.5 mm” at v_c = 170 m/min registered the largest percentage difference, indicating a 55.56% increase in chip thickness. Conversely, the smallest difference (–1.69%) occurred for the comparison “V_B = 0.1 mm → V_B = 0.5 mm” at v_c = 140 m/min. Table 4 shows that employing v_c = 140 m/min did not yield any statistical differences among the tested wear levels, except for comparisons between flank wear levels of 0.2 mm and 0.5 mm.

Table 5 presents the analysis of variance (ANOVA) applied to the chip thickness data, considering a 95% confidence interval and a 5% significance level. The results indicate that tool wear had a statistically significant effect on chip thickness. In contrast, cutting speed did not show a statistically significant influence. This outcome can be attributed to the fact that, within the tested speed range, tool wear has a greater impact on chip formation than moderate variations in cutting speed.

Table 5
Analysis of variance of chip thickness.

EFFECT	SQ	GL	MS	F	P-VALUE
Wear	0.0656	5	0.0131	4.849	0.004
vc	0.0103	4	0.0025	0.953	0.454
Error	0.0541	20	0.0027	–	–

Regardless of the tool wear conditions, all chips formed were continuous due to the high ductility of the SAE 304 stainless steel. However, chip shape and coloration varied according to cutting parameters and tool wear. At lower cutting speeds (e.g., 130 m/min), chips exhibited a yellowish hue and formed connected, arc-like shapes. As cutting speed and tool wear increased, chips tended to become more deformed, darker (purple or red) and occasionally disconnected.

Figure 5 illustrates these variations, showing that the coloration shifts are likely caused by oxide formation on the chip surface due to elevated temperatures. Similar observations find support in in the literature, which indicates that high-temperature machining leads to oxide layer formation and corresponding changes in chip color [³⁸,³⁹,⁴⁰,⁴¹]. However, it is important to note that in the present work, surface characterization techniques such as X-ray Photoelectron Spectroscopy were not employed to confirm the chemical composition of the chip surfaces. Therefore, while the color change suggests thermal oxidation, the hypothesis is based on indirect visual evidence and literature precedent rather than direct spectroscopic analysis.

Figure 5
Chips collected during the experiments.

Quick-stop test images revealed increased adhesion at the chip root under severe wear conditions, which can lead to chip weakening and partial disconnection. This phenomenon can be attributed to the increased friction and localized temperature rise at the tool–chip interface as flank wear progresses. The worn tool surface promotes the formation of adhesion zones due to higher contact stresses and material affinity, especially in austenitic stainless steels like SAE 304. These conditions facilitate micro-welding between the tool and the chip, resulting in greater material transfer and potential detachment at the root. Consequently, the observed changes in chip morphology and coloration, such as more pronounced deformation and darker hues, are linked to these thermomechanical effects. These findings reinforce the potential of chip analysis as a practical, indirect indicator of tool condition and process stability during machining.

Figure 6 shows the behavior of cutting temperature throughout tool wear for different cutting speeds. The temperatures reached by the chips were also more strongly influenced by wear. With increasing wear, the maximum temperatures reached by the chips rose significantly. Most of the heat generated in the primary shear zone is dissipated through the chip, and as wear rises, so do the deformations in the primary shear zone, thereby heightening the generated heat and chip temperature.

Figure 6
Behavior of cutting temperature across different tool wear levels.

Regarding temperature measurements, most of the heat generated in the primary shear zone is dissipated via the chip. As wear worsens, increased friction and plastic deformation elevate chip temperatures. These findings confirm the assertion in BOOTHROOYD [⁴²] that the cutting speed alone does not drastically alter chip temperature, especially when compared to the effect of flank wear.

BOOTHROYD [⁴²] proposes a method that enables approximate calculations of chip temperature increases due to heat generated in the primary shear zone, indicating that chip temperature is not greatly affected by cutting speed. The test results confirm this observation, as there were practically no major temperature variations with increased speed, except in the tests conducted at the highest tool wear level.

Tables 6 and 7 present, respectively, the average percentage difference in cutting temperature and the statistical significance of these differences under various tool wear conditions. Upon examining Table 6, it is evident that the highest percentage difference observed (+77.27%) occurred in the comparison “V_B = 0.1 mm → V_B = 0.5 mm” at a cutting speed of v_c = 170 m/min.

Table 6
Average percentage difference in cutting temperature.

PERCENTUAL DIFFERENCE (%)
DIFFERENCE	vc = 130 m/min	vc = 140 m/min	vc = 150 m/min	vc = 160 m/min	vc = 170 m/min
VB = 0 mm → VB = 0.1 mm	+ 0.71	– 1.75	+ 4.35	– 6.38	– 12.00
VB = 0 mm → VB = 0.2 mm	+ 7.14	+ 7.41	+ 17.39	+ 19.15	+ 12.00
VB = 0 mm → VB = 0.3 mm	– 0.36	+ 5.56	+ 26.09	+ 23.40	+ 16.00
VB = 0 mm → VB = 0.4 mm	+ 25.00	+ 3.70	+ 26.96	+ 31.91	+ 52.00
VB = 0 mm → VB = 0.5 mm	+ 14.29	+ 22.22	+ 44.78	+ 55.74	+ 56.00
VB = 0.1 mm → VB = 0.2 mm	+ 6.38	+ 9.43	+ 12.50	+ 27.27	+ 27.27
VB = 0.1 mm → VB = 0.3 mm	–1.06	+ 7.55	+ 20.83	+ 31.82	+ 31.82
VB = 0.1 mm → VB = 0.4 mm	+ 24.11	+ 5.66	+ 21.67	+ 40.91	+ 72.73
VB = 0.1 mm → VB = 0.5 mm	+ 13.48	+ 24.53	+ 38.75	+ 66.36	+ 77.27
VB = 0.2 mm → VB = 0.3 mm	– 7.00	– 1.72	+ 7.41	+ 3.57	+ 3.57
VB = 0.2 mm ? VB = 0.4 mm	+ 16.67	– 3.45	+ 8.15	+ 10.71	+ 3.57
VB = 0.2 mm → VB = 0.5 mm	+ 6.67	+ 13.79	+ 23.33	+ 30.71	+ 39.29
VB = 0.3 mm → VB = 0.4 mm	+ 25.45	– 1.75	+ 0.69	+ 6.90	+ 31.03
VB = 0.3 mm → VB = 0.5 mm	+ 14.70	+ 15.79	+ 14.83	+ 26.21	+ 2.63
VB = 0.4 mm → VB = 0.5 mm	– 8.57	+ 17.86	+ 14.04	+ 18.06	+ 2.63

Table 7
Statistical analysis of of cutting temperature differences.

P-VALUE
DIFFERENCE	vc = 130 m/min	vc = 140 m/min	vc = 150 m/min	vc = 160 m/min	vc = 170 m/min
VB = 0 mm → VB = 0.1 mm	0.9107	0.7851	0.5825	0.4210	0.1476
VB = 0 mm → VB = 0.2 mm	0.2983	0.2983	0.0752	0.0548	0.1480
VB = 0 mm → VB = 0.3 mm	0.9552	0.4210	0.0231	0.0304	0.0752
VB = 0 mm → VB = 0.4 mm	0.0139	0.5825	0.0208	0.0110	0.0015
VB = 0 mm ? VB = 0.5 mm	0.0752	0.0231	0.0035	0.0014	0.0011
VB = 0.1 mm → VB = 0.2 mm	0.3429	0.2097	0.0139	0.0231	0.0231
VB = 0.1 mm → VB = 0.3 mm	0.9552	0.2983	0.0405	0.0139	0.0139
VB = 0.1 mm → VB = 0.4 mm	0.0153	0.4210	0.0360	0.0058	0.0007
VB = 0.1 mm → VB = 0.5 mm	0.0858	0.0178	0.0051	0.0010	0.0005
VB = 0.2 mm → VB = 0.3 mm	0.3199	0.7801	0.2983	0.5825	0.5825
VB = 0.2 mm → VB = 0.4 mm	0.0405	0.5825	0.2591	0.1476	0.0039
VB = 0.2 mm → VB = 0.5 mm	0.2983	0.0752	0.0197	0.0068	0.0028
VB = 0.3 mm → VB = 0.4 mm	0.0146	0.7852	0.9107	0.2983	0.0058
VB = 0.3 mm → VB = 0.5 mm	0.0883	0.0548	0.0621	0.0105	0.0039
VB = 0.4 mm → VB = 0.5 mm	0.1476	0.0405	0.0705	0.0287	0.5825

As shown in Table 7, all comparisons between the 0.1 mm and 0.5 mm flank wear levels exhibited statistically significant differences, with the sole exception being at a cutting speed of v_c = 130 m/min.

Table 8 presents the ANOVA for the machining temperature data, considering a 95% confidence interval and a 5% significance level. Among the variables analyzed, only tool wear showed a statistically significant effect. This result suggests that increased wear on the cutting edge directly contributes to a rise in machining temperature, while variations in cutting speed were not impactful enough to statistically influence the temperature within the tested range.

Table 8
Analysis of variance of cutting temperature.

EFFECT	SQ	GL	MS	F	P-VALUE
Wear	38963	5	7793	12.314	0.001
vc	3229	4	807	1.276	0.312
Error	12657	20	633	–	–

Figures 7 and 8 show images of the chip root for different tool wear levels obtained via quick-stop tests. For tools with V_B = 0 mm (no flank wear), the chip root region reveals the presence of inclusions (Figure 7a), likely originating from the base material. The chip exhibits a large curvature radiusand there is no indication of material adhesion to the cutting tool. No built-up edge formation was observed, and the interface between the tool and the workpiece shows a clean and well-defined separation, indicating an efficient cutting process under minimal wear conditions. From a metallurgical perspective, the flow zone within the chip root appears well-oriented and continuous, suggesting a stable plastic deformation process. The shear zone is clearly distinguishable, with material flow lines uniformly aligned in the direction of chip formation. This indicates that, in the absence of flank wear, the material undergoes smooth shear without significant disruption, favoring the formation of consistent chip morphology and reducing energy losses due to friction or secondary deformation mechanisms.

Figure 7
Chip Root Images: (a) V_B = 0 mm; (b) V_B = 0.5 mm.

Figure 8
Chip root images to V_B = 0.2 mm.

In Figure 8, the sample displays a cleaner surface, once again highlighting the formation of a well-defined separation zone between the workpiece and the tool, with a smooth interface surface clearly visible. However, at the beginning of the chip root, lighter patches can be seen, which are likely early signs of material adhesion from the workpiece onto the cutting tool. Such adhesion can compromise the surface quality of the machined part and negatively impact process stability. These observations are directly associated with progressive tool wear, particularly flank wear. As the tool wears, the cutting edge becomes rougher, increasing friction between the tool and the chip. This leads to higher cutting temperatures and alters the material flow conditions, promoting adhesion of workpiece material to the tool surface. The intensified tribological interaction contributes to more unstable shear zones and greater plastic deformation of the chip, which is reflected in both chip morphology and the overall machining performance.

Figure 7b also shows no evidence of built-up edge (BUE) formation, and the chip curvature radius remains large, indicating that the material continues to exhibit ductile behavior even under more pronounced tool wear conditions. However, this sample presents more intense adhesion marks at the chip root region compared to Figure 8. This increase in adhesion signs may be associated with the progression of flank wear, which alters the tribological interaction between the tool and the workpiece material. As flank wear becomes more severe, cutting conditions grow harsher, leading to higher temperatures and increased plastic deformation of the removed material. Consequently, there is a greater tendency for the workpiece material to adhere to the tool surface, potentially compromising the machined surface integrity and contributing to process instability.

CHILDS et al. [⁴³] note that when there is only slight adhesion between the chip and the tool, a clean separation is observed in quick-stop tests, a fact particularly common for carbide tools. The images suggest that the unworn tool (V_B = 0) maintains a clean separation on the chip’s underside and the chip root. However, for tools with wear (V_B = 0.2 mm and V_B = 0.5 mm), adhesion marks appear in the chip root, and the area of adhesion expands with greater wear. For this reason, as wear increases, the chip becomes more disconnected. Adhesion grows in the chip root, causing it to weaken progressively until it breaks.

Overall, these results highlight the importance of continuous wear monitoring to maintain consistent machining conditions and avoid critical tool failure. By focusing on flank wear evolution, manufacturers can optimize cutting parameters, reduce production costs, and maintain higher surface quality in machined parts.

4. CONCLUSIONS

Based on the results presented in this work, the following conclusions can be drawn:

1. Scientific Contribution and Novelty: This work highlights the critical role of flank wear as the primary factor influencing chip thickness and temperature during the dry turning of SAE 304 stainless steel. The research introduces a systematic approach to monitor tool wear progression through chip morphology analysis, which provides a more cost-effective and real-time alternative to traditional wear measurement methods.

2. Key Findings: The high ductility of SAE 304 promotes the formation of continuous chips under all tested conditions. However, as tool wear intensifies, chip deformation becomes more pronounced, leading to significant changes in chip color due to oxide formation. These observations were not only consistent with the expected thermal effects but also introduced a new correlation between tool wear and chip coloration, which could be used as a visual indicator of wear progression.

3. Impact of Cutting Speed: The study reveals that cutting speed has a minimal effect on chip behavior compared to tool wear, emphasizing that wear is the dominant parameter influencing chip formation and temperature. This finding offers new insights into the machining of austenitic stainless steels, suggesting that wear management should be prioritized over cutting speed adjustments to optimize performance.

4. Chip Thickness Variation and Statistical Analysis: The experimental results showed that the greatest variation in chip thickness occurred at a cutting speed of 170 m/min, where flank wear progression from 0.1 mm to 0.5 mm resulted in a 55.56% increase. In contrast, the smallest variation (–1.69%) was observed at 140 m/min for the same wear progression. Statistical analysis indicated that, at 140 m/min, only the comparison between 0.2 mm and 0.5 mm flank wear levels showed significant differences.

5. Heat Generation and Frictional Forces: The analysis demonstrates that the majority of heat generated during machining is carried away by the chip, and as wear increases, the size of deformation zones also increases, intensifying frictional forces and temperature. This thermal and mechanical interplay plays a significant role in tool wear progression and chip formation, contributing to the understanding of heat dynamics in high-temperature machining processes.

6. Future Directions: The study also opens up avenues for future research, particularly in the development of real-time monitoring systems for tool wear based on chip analysis. Further investigations into the correlation between chip color and wear progression, as well as the application of advanced surface characterization techniques such as XPS, could provide deeper insights into the oxidation processes and adhesion phenomena in machining. Additionally, exploring the use of different cutting fluids and coatings may further enhance the efficiency and sustainability of machining processes.

5. ACKNOWLEDGMENTS

We would like to express our deep gratitude to Wisley Falco Salles (in memoriam) for his invaluable contribution to this work. His dedication, knowledge and enthusiasm were essential to the development of this study. We also thank Tiago for bringing an unique and innovative perspective that enriched our discussions and propelled the research through various stages.

6. BIBLIOGRAPHY

• ^[1] BOBAN, J., AHMED, A., “Improving the surface integrity and mechanical properties of additive manufactured stainlesssteel components by wire electrical discharge polishing”, Journal of Materials Processing Technology, v. 291, pp. 107–113, 2021. doi: http://doi.org/10.1016/j.jmatprotec.2020.117013.
  » https://doi.org/10.1016/j.jmatprotec.2020.117013
• ^[2] NATH, C., ZHENG, L., “Effect of in-built anisotropic and heterogeneous material properties on machinability in drilling of AISI 304 stainless steel”, Journal of Manufacturing Processes, v. 59, pp. 122–130, 2020. doi: http://doi.org/10.1016/j.jmapro.2020.08.067.
  » https://doi.org/10.1016/j.jmapro.2020.08.067
• ^[3] JAYAGANTH, A., JAYAKUMAR, K., DEEPAK, A., et al, “Experimental studies on drilling of 410 stainless steel”, Materials Today: Proceedings, v. 5, n. 2, pp. 7168–7173, 2018. doi: http://doi.org/10.1016/j.matpr.2017.11.382.
  » https://doi.org/10.1016/j.matpr.2017.11.382
• ^[4] ELSHEIKH, A.H., “Applications of machine learning infriction stir welding: prediction of joint properties, real-timecontrol and tool failure diagnosis”, Engineering Applications of Artificial Intelligence, v. 121, pp. 105–112, 2023. doi: http://doi.org/10.1016/j.engappai.2023.105961.
  » https://doi.org/10.1016/j.engappai.2023.105961
• ^[5] MACHADO, A.R., ABRÃO, A.M., COELHO, R.T., et al, Teoria da usinagem dos materiais, 3 ed., São Paulo, Editora Blücher, v. 3, 2018.
• ^[6] TRENT, E.M., WRIGHT, P.K., Metal cutting, 4 ed., Boston, Butterworth-Heinemann, 2020.
• ^[7] HEMA, P., GANESAN, R., “Experimental investigations on SS 304 alloy using plasma arc machining”, SN Appl Sci., v. 2, n. 4, pp. 624, 2020. doi: http://doi.org/10.1007/s42452-020-2350-y.
  » https://doi.org/10.1007/s42452-020-2350-y
• ^[8] PRAMANIK, A., BASAK, A.K., DIXIT, A.R., et al, “Processing of duplex stainless steel by WEDM”, Materials and Manufacturing Processes, v. 33, n. 14, pp. 155–167, 2018. doi: http://doi.org/10.1080/10426914.2018.1453165.
  » https://doi.org/10.1080/10426914.2018.1453165
• ^[9] SHARMA, P., SONI, H., SETHY, S., et al, “Surface characterization of SAE 304 after WED cutting: an experimental investigation and optimization”, Journal of Materials Research and Technology, v. 12, pp. 5723–5732, 2023. doi: http://doi.org/10.1016/j.jmrt.2023.02.183.
  » https://doi.org/10.1016/j.jmrt.2023.02.183
• ^[10] MUSHTAQ, M., RAMESH, B., “A novel investigation on effect of process parameters on surfaceroughness while drilling normalized and annealed SAE 304 stainless steel and comparing the outputs with untreated stainless steel”, Materials Today: Proceedings, v. 69, pp. 980–985, 2022. doi: http://doi.org/10.1016/j.matpr.2022.08.005.
  » https://doi.org/10.1016/j.matpr.2022.08.005
• ^[11] ROY, S., KUMAR, R., PANDA, A., et al, “A comparative performance investigation of single-and double-nozzle pulse mode minimum quantity lubrication systems in turning super-duplex steel using a weighted pugh matrix sustainable approach”, Sustainability, v. 15, n. 20, pp. 151–160, 2023. doi: http://doi.org/10.3390/su152015160.
  » https://doi.org/10.3390/su152015160
• ^[12] JAYAGANTH, A., JAYAKUMAR, K., DEEPAK, A., et al, “Experimental studies on drilling of 410 stainless steel”, Materials Today: Proceedings, v. 5, n. 2, pp. 7168–7173, 2018. doi: http://doi.org/10.1016/j.matpr.2017.11.382.
  » https://doi.org/10.1016/j.matpr.2017.11.382
• ^[13] KUMAR, P., KUMAR, M., ANAND, K., et al, “Experimental investigationof processing parameters for AISI 304 stainless steel using abrasive assistedconventional drilling”, Optimiz. Eng. Res., v. 2, n. 2, pp. 55–65, 2020. doi: http://doi.org/10.47406/OER.2020.1205.
  » https://doi.org/10.47406/OER.2020.1205
• ^[14] SUBHEDAR, D.G., PATEL, Y.S., RAMANI, B.M., et al, “An experimental investigation on the effect of Al2O3/ cutting oil-based nano coolant for Minimum Quantity Lubrication drilling of SS 304”, Cleaner Engineering and Technology, v. 3, pp. 100–104, 2021. doi: http://doi.org/10.1016/j.clet.2021.100104.
  » https://doi.org/10.1016/j.clet.2021.100104
• ^[15] KHAN, A.M., GUPTA, M.K., HEGAB, H., et al, “Energy based cost integrated modelling and sustainability assessment of Al-GnP hybrid nano fluid assisted turning of AISI52100 steel”, Journal of Cleaner Production, v. 257, pp. 120–502, 2020. doi: http://doi.org/10.1016/j.jclepro.2020.120502.
  » https://doi.org/10.1016/j.jclepro.2020.120502
• ^[16] MAO, C., HUANG, Y., ZHOU, X., et al, “The tribologicalproperties of nanofluid used in minimum quantity lubrication grinding”, International Journal of Advanced Manufacturing Technology, v. 71, n. 5–8, pp. 1221–1228, 2014. doi: http://doi.org/10.1007/s00170-013-5576-7.
  » https://doi.org/10.1007/s00170-013-5576-7
• ^[17] TETI, R., “Advanced sensors for tool monitoring in machining”, CIRP Annals, v. 51, pp. 819–839, 2002.
• ^[18] WANG, X., LI, N., LU, D., et al, “Tool wear state recognition for variable sensor combinations by deep forest with parameter adaptive fine-tuning”, Applied Soft Computing, v. 169, pp. 112–129, 2025. doi: http://doi.org/10.1016/j.asoc.2024.112629.
  » https://doi.org/10.1016/j.asoc.2024.112629
• ^[19] MATHIYAZHAGAN, V., MEENA, A., “Predictive modelling of tool wear in CFRP drilling using acoustic emission sensors under dry and cryogenic conditions”, Wear, v. 88, pp. 205–230, 2025. doi: http://doi.org/10.1016/j.wear.2025.205930.
  » https://doi.org/10.1016/j.wear.2025.205930
• ^[20] JESSEL, T., BYRNE, C., EATON, M., et al, “Tool condition monitoring of diamond-coated burrs with acoustic emission utilising machine learning methods”, International Journal of Advanced Manufacturing Technology, v. 130, n. 3–4, pp. 1107–1124, 2024. doi: http://doi.org/10.1007/s00170-023-12700-7.
  » https://doi.org/10.1007/s00170-023-12700-7
• ^[21] NAKANO, T., KORESAWA, H., NARAHARA, H., “Tool condition monitoring method by anomaly segmentation of time-frequency images using acoustic emission in small hole drilling”, Journal of Advanced Mechanical Design, Systems and Manufacturing, v. 17, n. 3, pp. 13–25, 2023. doi: http://doi.org/10.1299/jamdsm.2023jamdsm0034.
  » https://doi.org/10.1299/jamdsm.2023jamdsm0034
• ^[22] STECKEL, J., AERTS, A., VERREYCKEN, E., “Tool wear prediction in CNC turning operations using ultrasonic microphone arrays and CNNs”, arXiv, pp. 1–4, 2024. doi: http://doi.org/10.48550/arXiv.2406.08957.
  » https://doi.org/10.48550/arXiv.2406.08957
• ^[23] MAIA, L.H.A., ABRÃO, A.M., VASCONCELOS, W.L., et al, “Enhancing machining efficiency: real-time monitoring of tool wear with acoustic emission and STFT techniques”, Lubricants, v. 12, n. 11, pp. 380–396, 2024. doi: http://doi.org/10.3390/lubricants12110380.
  » https://doi.org/10.3390/lubricants12110380
• ^[24] DINIZ, A.E., MICARONI, R., GOMES, J.F.S., “Tool wear monitoring of turning operations by using cutting force and acoustic emission signals”, Wear, v. 259, pp. 1151–1160, 2016.
• ^[25] REIS, A., SOUSA, J.A.G., VAUGHAN, L.L.T., et al, “Critical analysis of wear mechanisms in carbide tools applied in the machining of high-strength cast iron alloys”, Journal of the Brazilian Society of Mechanical Sciences and Engineering, v. 46, pp. 918–925, 2024.
• ^[26] CARVALHO, P.P., FERNANDES, G.H.N., BARBOSA, L.M.Q., et al, “Different cooling strategies applied during the process of aluminum alloy boring”, International Journal of Advanced Manufacturing Technology, v. 128, n. 1-2, pp. 563–579, 2023. doi: http://doi.org/10.1007/s00170-023-11840-0.
  » https://doi.org/10.1007/s00170-023-11840-0
• ^[27] DAVIES, M.A., UEDA, T., M’Saoubi, R., et al, “On the measurement of temperature in material removal processes”, CIRP Annals, v. 56, n. 2, pp. 581–604, 2007. doi: http://doi.org/10.1016/j.cirp.2007.10.009.
  » https://doi.org/10.1016/j.cirp.2007.10.009
• ^[28] MUTHUSWAMY, P., “Investigation on sustainable machining characteristics of tools with serrated cutting edges in face milling of AISI 304 Stainless Steel, 29th CIRP Life Cycle Engineering Conference”, Procedia CIRP, v. 105, pp. 865–871, 2022. doi: http://doi.org/10.1016/j.procir.2022.02.143.
  » https://doi.org/10.1016/j.procir.2022.02.143
• ^[29] SU, Y., ZHAO, G., ZHAO, Y., et al, “Multi-objective optimization of cutting parameters in turning AISI 304 austenitic stainless steel”, Metals, v. 10, n. 2, pp. 112–119, 2020. doi: http://doi.org/10.3390/met10020217.
  » https://doi.org/10.3390/met10020217
• ^[30] YAN, H., WANG, J., ZHANG, Z., et al, “Effects of cutting parameter on microstructure and corrosion behavior of 304 stainless steel in simulated primary water”, Journal of Materials Science and Technology, v. 122, pp. 219–230, 2022. doi: http://doi.org/10.1016/j.jmst.2021.04.081.
  » https://doi.org/10.1016/j.jmst.2021.04.081
• ^[31] BABU, P.D., BUVANASHEKARAN, G., BALASUBRAMANIAN, K.R., “Experimental studies on the microstructure and hardness of laser transformation hardening of low alloy steel”, Transactions of the Canadian Society for Mechanical Engineering, v. 36, n. 3, pp. 241–258, 2012. doi: http://doi.org/10.1139/tcsme-2012-0018.
  » https://doi.org/10.1139/tcsme-2012-0018
• ^[32] NAWAZ, Y., MAQSOOD, S., NAEEM, K., et al, “Parametric optimization of material removal rate, surface roughness, and kerf width in high-speed wire electric discharge machining (HS-WEDM) of DC53 die steel”, International Journal of Advanced Manufacturing Technology, v. 107, n. 7-8, pp. 323–345, 2020. doi: http://doi.org/10.1007/s00170-020-05175-3.
  » https://doi.org/10.1007/s00170-020-05175-3
• ^[33] AGARWAL, H., GOKHALE, A.M., GRAHAM, S., et al, “Void growth in 6061-aluminum alloy under tri-axial stress state”, Materials Science and Engineering A, v. 341, n. 1-2, pp. 35–42, 2003. doi: http://doi.org/10.1016/S0921-5093(02)00073-4.
  » https://doi.org/10.1016/S0921-5093(02)00073-4
• ^[34] SCHAEFFER, L., LIMA, D.R.S., YURGEL, C.C., “Study of microstructure and hardness of materials”, In: Anais do Congresso Brasileiro de Engenharia e Ciência dos Materiais, Foz do Iguaçu, 2006.
• ^[35] TAN, E., ÖGEL, B., “Influence of heat treatment on the mechanical properties of AA6066 alloy, Turkish”, J Eng Env Sci, v. 31, pp. 53–60, 2007.
• ^[36] PACE Technologies, Metallographic preparation, www.metallographic.com/Etchants/, accessed in November, 2024.
  » www.metallographic.com/Etchants/
• ^[37] Sandvik, Catálogo de ferramentas, https://www.sandvik.coromant.com/, accessed in November, 2024.
  » https://www.sandvik.coromant.com/
• ^[38] KITAGAWA, T., KUBO, A., MAEKAWA, K., “Temperature and wear of cutting tools in high-speed machining of Inconel 718 and Ti6Al6V2Sn”, Wear, v. 202, n. 2, pp. 142–148, 1997. doi: http://doi.org/10.1016/S0043-1648(96)07255-9.
  » https://doi.org/10.1016/S0043-1648(96)07255-9
• ^[39] SHAW, M.C., Metal cutting principles, 2 ed., Oxford, Oxford University Press, 2005.
• ^[40] PUSAVEC, F., KRAJNIK, P., KOPAC, J., “Transitioning to sustainable production – part I: application on machining technologies”, Journal of Cleaner Production, v. 18, n. 2, pp. 174–184, 2010. doi: http://doi.org/10.1016/j.jclepro.2009.08.010.
  » https://doi.org/10.1016/j.jclepro.2009.08.010
• ^[41] KÖNIG, W., KLINGER, F., LINK, R., “Machining of titanium and titanium alloys”, CIRP Annals, v. 33, pp. 393–399, 1984.
• ^[42] BOOTHROOYD, G., Fundamentals of metal machining and machine tools, 2 ed., New York, Marcel Dekker Inc., 1989.
• ^[43] CHILDS, T.H.C., MAEKAWA, T., OBIKAWA, T., et al, Metal machining: theory and applications, 1 ed., London, Arnolds, 2000.

Publication Dates

• Publication in this collection
  11 July 2025
• Date of issue
  2025

History

• Received
  04 Feb 2025
• Accepted
  30 May 2025