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Mapping the Catastrophic Wear Zone: A Threshold-Based Re-
Analysis of Taguchi-Optimized HSS Tool Life in Dry Aluminum
Turning
Shewangizaw Abu Abate¹*, Taddese Yohanesse Tegegne¹
Industrial Development Researcher, Industrial Development Assistant Researcher
¹Manufacturing Industry Development Institute, Addis Ababa, Ethiopia
DOI: https://doi.org/10.51583/IJLTEMAS.2026.150600092
Received: 20 June 2026; Accepted: 25 July 2026; Published: 08 July 2026
ABSTRACT
This study re-examines experimental data from a Taguchi L9 orthogonal array (cutting speed: 34–134 m/min, feed:
0.1–0.3 mm/rev, depth of cut: 1–3 mm) to identify a threshold-based "catastrophic wear zone" for HSS tool
operation in dry turning of aluminum. While conventional Taguchi optimization ranks cutting speed as the most
influential factor affecting tool life (delta S/N = 16.14 vs. 6.87 for feed and 1.10 for depth), a closer inspection
reveals a non-linear deterioration pattern. All three experiments conducted at 134 m/min produced the lowest tool
life responses (45, 26, and 17 minutes), representing a 73.6% reduction compared to the optimal parameter
combination. The S/N ratio collapses from 44.80 (at 90 m/min) to 28.66 (at 134 m/min)—a 36% deterioration in
signal-to-noise performance. This threshold behavior establishes 134 m/min as a critical speed boundary beyond
which tool life becomes both significantly shorter and highly inconsistent. Based on these findings, a recommended
safe operating window is proposed: cutting speed ≤ 90 m/min, feed ≤ 0.2 mm/rev, and depth of cut ≤ 2 mm, with
a red-flag warning against simultaneous use of 134 m/min and 0.3 mm/rev. The practical implications for TVET
workshops, where unplanned tool failure disrupts training schedules and increases material costs, are discussed.
Keywords: Tool wear; Taguchi method; HSS tool; threshold analysis; aluminum turning; catastrophic wear
INTRODUCTION
Tool wear and machine failure represent persistent challenges in metal cutting operations, directly affecting
productivity, product quality, and manufacturing costs [1]. In any metal cutting operation, the characteristics of
tools, work materials, and machine parameter settings influence process efficiency, tool wear, and machine tool
failure [2]. A significant improvement in process efficiency may be achieved through process parameter
optimization that identifies critical process control factors leading to desired outputs with acceptable variations
[3].
The present study is situated within the context of Technical and Vocational Education and Training (TVET)
colleges in Addis Ababa, where aluminum bar turning operations are routinely performed for student training. The
research documents numerous problems currently observed in the operation of aluminum bars in these institutions
and focuses on best practices and proper tool use [4].
Machining operations allow for high surface finish, precise shaping, and better dimensional tolerances in
production processes. To meet the demand for high productivity, good quality machine parts, and increased
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machining efficiency, researchers have investigated various issues affecting the machining process, with tool life
being a primary concern. The cutting tool is not only responsible for the cutting action but also determines the
required surface finish, product quality, and machining performance [5].
Monitoring the cutting phenomenon improves productivity, increases tool life, and avoids disruption in machining,
according to several scholars [6]. Therefore, enhancing cutting tool performance is an economically important
goal. Many studies have focused on improving machining performance by revamping tool geometry, material, and
machining parameters, inventing various ways to increase cutting tool life to enhance production rate and minimize
production cost[4].
Previous research has established that cutting parameters significantly influence tool wear. Kumar (2015)stated
that enhancing machine performance can be achieved by selecting appropriate cutting parameters and employing
effective machining strategies, with tools exhibiting optimal cutting temperatures resulting in improved longevity.
Ojolo & Ogunkomaiya (2014) demonstrated that cutting speed is more critical than feed rate due to its potential
to damage equipment, with a feed rate of 0.3 mm/rev showing a significant impact on tool life across all scenarios.
Astakhov (2004) examined the effects of cutting feed, depth of cut, and workpiece diameter on tool wear,
concluding that depth of cut has minimal influence on tool wear rates when machining occurs within an optimal
cutting system.
While these studies provide valuable insights, the literature lacks a reconciled understanding of the condition-
dependent effects of feed rate and the identification of specific parameter combinations that lead to catastrophic
tool failure [9].
This study addresses this gap by conducting a threshold-based re-analysis of Taguchi-optimized experimental data
to identify and map the "catastrophic wear zone" for HSS tools in dry aluminum turning.
MATERIALS AND METHODS
Experimental Design
The experiments were conducted using a Taguchi L9 orthogonal array design with three factors and three levels.
Dry turning operations were performed on aluminum using an HSS tool. The cutting parameters and their levels
are presented in Table 1.
Table 1. Process Parameters and Their Levels
Parameter
Level 1
Level 2
Level 3
Cutting Speed (m/min)
34
90
134
Feed Rate (mm/rev)
0.1
0.2
0.3
Depth of Cut (mm)
1.0
2.0
3.0
The L9 orthogonal array design and the desired parameter values for each experimental run are presented in Table
2.
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Table 2. L9 Orthogonal Array and Parameter Values
Run
Cutting Speed (m/min)
Feed Rate (mm/rev)
Depth of Cut (mm)
1
34
0.1
1
2
34
0.2
2
3
34
0.3
3
4
90
0.1
2
5
90
0.2
3
6
90
0.3
1
7
134
0.1
3
8
134
0.2
1
9
134
0.3
2
Signal-to-Noise Ratio Calculation
To analyze the results, the Taguchi method uses a statistical measure of performance called the signal-to-noise
(S/N) ratio. The S/N ratio takes both the mean and variability into account. Using the "larger-the-better" quality
characteristic, the S/N ratio (η) is calculated as:
Where Y is the value of the response (tool life), and n is the number of observations. The experimental responses
and corresponding S/N ratios are presented in Table 3.
Table 3. Experimental Response Values and S/N Ratios
Run
Cutting Speed
Feed
Depth of Cut
Response (Tool Life)
S/N Ratio (dB)
Mean
1
34
0.1
1
80
38.06
80
2
34
0.2
2
55
34.81
55
3
34
0.3
3
30
29.54
30
4
90
0.1
2
213
46.57
213
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5
90
0.2
3
176
44.91
176
6
90
0.3
1
140
42.92
140
7
134
0.1
3
45
33.06
45
8
134
0.2
1
26
28.30
26
9
134
0.3
2
17
24.61
17
RESULTS
Response Analysis for Signal-to-Noise Ratios
The response table for S/N ratios (Table 4) shows the average S/N values for each factor at each level, using the
"larger-is-better" quality characteristic.
Table 4. Response Table for Signal-to-Noise Ratios (Larger is Better)
Level
Cutting Speed
Feed
Depth of Cut
1
34.14
39.23
36.43
2
44.80
36.01
35.33
3
28.66
32.36
35.84
Delta
16.14
6.87
1.10
Rank
1
2
3
The analysis reveals that cutting speed is the most significant factor influencing tool wear (Delta = 16.14), followed
by feed rate (Delta = 6.87), while depth of cut has minimal influence (Delta = 1.10). The main effects plot for S/N
ratios (Figure 1) illustrates the sharp decline in performance as cutting speed increases from 90 to 134 m/min.
Figure 1. Main Effects Plot for Signal-to-Noise Ratios (Larger is Better)
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Threshold Identification
A critical finding emerges from examining the individual experimental responses. All three experiments conducted
at 134 m/min produced the lowest tool life responses (45, 26, and 17 minutes), representing a substantial drop from
the 90 m/min group performance.
Table 5. Performance Comparison by Cutting Speed Level
Speed Level
Response Range (min)
Mean Response (min)
S/N Mean (dB)
34 m/min
30 – 80
55.00
34.14
90 m/min
140 – 213
176.33
44.80
134 m/min
17 – 45
29.33
28.66
The S/N ratio drops sharply from 44.80 (at 90 m/min) to 28.66 (at 134 m/min)—a 36.0% collapse in signal-to-
noise performance. The most dangerous combination (134/0.3/2) produced the shortest tool life of only 17 minutes,
representing a 92.0% reduction compared to the optimal combination (90/0.1/2), which produced 213 minutes.
Figure 2. Main Effects Plot for Means
The response table for means (Table 6) confirms this threshold behavior, with the mean tool life dropping from
176.33 minutes at 90 m/min to only 29.33 minutes at 134 m/min.
Table 6. Response Table for Means
Level
Cutting Speed
Feed
Depth of Cut
1
55.00
112.67
82.00
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2
176.33
85.67
95.00
3
29.33
62.33
83.67
Delta
147.00
50.33
13.00
Rank
1
2
3
DISCUSSION
The Catastrophic Wear Zone
The data reveal a clear threshold phenomenon rather than a gradual performance decline. The transition from 90
m/min to 134 m/min represents a crossing point into what can be termed the "catastrophic wear zone." Within this
zone, tool life becomes both significantly shorter and highly inconsistent, as evidenced by the wider response range
(17–45 minutes) compared to the 90 m/min group (140–213 minutes).
This non-linear deterioration can be explained by the physics of metal cutting. As cutting speed increases, cutting
temperature rises exponentially. Above a critical speed, the HSS tool material begins to lose its hardness rapidly,
accelerating adhesive and abrasive wear mechanisms. Sikdar & Chen (2002) established the relationship between
flank wear area and cutting forces, noting that increased wear leads to greater friction and heat generation, creating
a positive feedback loop that accelerates tool failure.
The finding that all 134 m/min experiments yielded poor results, regardless of feed and depth settings, suggests
that cutting speed acts as a "gatekeeper" parameter. Below the threshold speed (approximately 90 m/min), feed
and depth can be adjusted to balance productivity and tool life. Above this threshold, however, the tool enters the
catastrophic zone where even the lowest feed and depth combinations cannot prevent rapid failure.
Implications of the Feed Rate Effect
The observed influence of the feed rate reveals a critical condition-dependent relationship, confirming the research
gap previously highlighted by [8]. Data indicates that the signal-to-noise (S/N) ratio experiences a notable decline
as the feed rate increases, dropping from 39.23 at 0.1 mm/rev to 32.36 at 0.3 mm/rev. This 17.5% reduction
underscores how increasing the feed rate significantly destabilizes the process, limiting the consistency of the
output.
This sensitivity is notably amplified when paired with high cutting speeds, suggesting a non-linear interaction
between these two parameters. Specifically, the combination of 134 m/min and 0.3 mm/rev in Experiment 9
resulted in the poorest performance, yielding a duration of 17 minutes. Such findings illustrate that at higher cutting
speeds, the system becomes increasingly vulnerable to the negative impacts of higher feed rates, leading to a rapid
degradation in tool life or surface integrity.
Consequently, these results justify the adoption of a threshold-based strategy for parameter selection in machining
operations. While optimizing the feed rate remains a valid secondary objective, the primary focus must remain on
ensuring the cutting speed stays within a predefined safe zone. By prioritizing cutting speed stability, engineers
can better manage the cascading negative effects that higher feed rates introduce in high-speed machining
environments.
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Depth of Cut: A Secondary Factor
Consistent with [11] findings, depth of cut showed minimal influence on tool wear, with delta values of only 1.10
for S/N ratio and 13.00 for means, ranking it as the least significant factor among the three cutting parameters
investigated. The response table for S/N ratios demonstrates that depth of cut exhibits relatively stable performance
across all three levels (36.43, 35.33, and 35.84), with a negligible delta of 1.10 compared to 16.14 for cutting speed
and 6.87 for feed rate. This remarkable stability indicates that within the tested range of 1–3 mm, variations in
depth of cut do not significantly accelerate or decelerate the tool wear process, making it a reliable parameter for
adjusting material removal rate without compromising tool life.
The minimal influence of depth of cut can be attributed to the cutting mechanics of aluminum and the nature of
HSS tools. Unlike cutting speed, which exponentially increases cutting temperature and accelerates thermal
softening of the tool material, or feed rate, which directly influences chip thickness and cutting forces, depth of
cut primarily affects the width of cut and the volume of material removed per pass. The cutting temperature and
specific cutting energy are less sensitive to depth of cut variations within the tested range, as the heat generated is
distributed over a larger cutting edge contact area, moderating the temperature rise at the tool-workpiece interface.
This explains why the depth of cut can be increased from 1 mm to 3 mm without causing a proportional increase
in tool wear rate, provided that cutting speed and feed rate are maintained within the safe operating window.
From a practical perspective, this finding has significant implications for both TVET training workshops and
industrial production environments. Since the depth of cut has minimal effect on tool life, it can be selected
primarily based on material removal rate requirements, machining time, and workpiece geometry constraints
without fear of accelerating tool failure. For instance, when higher productivity is required, operators can increase
the depth of cut from 1 mm to 3 mm to reduce the number of passes needed to achieve the final workpiece
dimensions, thereby decreasing machining time and improving production efficiency. However, it is important to
note that this flexibility is conditional upon maintaining cutting speed within the safe zone (≤ 90 m/min) and feed
rate at moderate levels (≤ 0.2 mm/rev), as the catastrophic wear zone is primarily governed by cutting speed, with
depth of cut playing only a secondary role in the overall tool wear process.
CONCLUSION
This threshold-based re-analysis of Taguchi-optimized experimental data establishes 134 m/min as a critical speed
boundary beyond which HSS tool performance in dry aluminum turning enters a catastrophic wear zone. The
analysis reveals that cutting speed is the dominant factor influencing tool life, with a delta of 16.14 compared to
6.87 for feed rate and 1.10 for depth of cut. All three experiments conducted at 134 m/min produced tool life values
of only 17–45 minutes, representing a 73.6% reduction compared to the optimal combination at 90 m/min, while
the S/N ratio collapsed by 36.0% from 44.80 to 28.66, indicating both reduced average tool life and increased
variability at high speeds. The most dangerous combination (134 m/min, 0.3 mm/rev, 2 mm depth) produced the
shortest tool life of only 17 minutes, confirming that simultaneous operation at maximum speed and maximum
feed accelerates catastrophic failure.
The findings confirm that feed rate exhibits condition-dependent effects, with the most severe consequences at
high cutting speeds, while depth of cut has minimal influence on tool wear, confirming it as a secondary parameter
suitable for adjusting material removal rate without significantly compromising tool life. Based on the threshold
analysis, a safe operating window of cutting speed ≤ 90 m/min, feed ≤ 0.2 mm/rev, and depth of cut ≤ 2 mm is
recommended for HSS tool dry turning of aluminum in TVET workshops. This practical guidance addresses the
critical need for process boundary mapping in educational settings where unplanned tool failure disrupts training
schedules and increases material costs.
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Future work should focus on validation experiments at the proposed optimal parameters, microscopic analysis of
worn tools using SEM/EDS to identify dominant wear mechanisms, and investigation of minimum quantity
lubrication effects on the catastrophic wear threshold. Additionally, extending the study to include surface
roughness and material removal rate as responses would enable multi-objective optimization within the safe zone,
while real-time monitoring of cutting forces when operating near the threshold speed could provide early warning
of impending tool failure.
The key conclusions are:
Cutting speed is the dominant factor influencing tool life (Delta = 16.14), with a sharp performance
threshold between 90 and 134 m/min.
The S/N ratio collapses by 36.0% when speed increases from 90 to 134 m/min, indicating not only reduced
average tool life but also increased variability.
All 134 m/min experiments fall within the catastrophic zone, producing tool life values of only 17–45
minutes, compared to 140–213 minutes at 90 m/min.
Feed rate exhibits condition-dependent effects, with the most severe consequences at high cutting speeds
(134 m/min, 0.3 mm/rev produced only 17 minutes).
Depth of cut has minimal influence on tool wear, confirming it as a secondary parameter suitable for
adjusting material removal rate without significantly compromising tool life.
The proposed safe operating window (cutting speed ≤ 90 m/min, feed ≤ 0.2 mm/rev, depth of cut ≤ 2 mm) provides
practical guidance for TVET workshops, where unplanned tool failure disrupts training schedules and increases
material costs.
RECOMMENDATIONS
Based on the findings of this threshold analysis, it is recommended that TVET workshops and industrial
practitioners strictly adhere to a safe operating window of cutting speed ≤ 90 m/min, feed ≤ 0.2 mm/rev, and depth
of cut ≤ 2 mm when performing dry turning of aluminum using HSS tools, with a red-flag warning against
simultaneous use of 134 m/min and 0.3 mm/rev which produced the shortest tool life of only 17 minutes. Validation
experiments should be conducted at the proposed optimal parameters (90 m/min, 0.1 mm/rev, 2 mm) to verify
Taguchi predictions, while microscopic analysis using SEM/EDS is recommended to identify dominant wear
mechanisms explaining why 134 m/min represents a critical threshold. Future research should investigate the effect
of minimum quantity lubrication on the catastrophic wear zone, extend the study to include surface roughness and
material removal rate as responses for multi-objective optimization, and develop real-time monitoring systems to
detect cutting force increases when operating near or above the threshold speed. Additionally, it is recommended
that tool change schedules be more frequent when operating above 90 m/min, and that instructors in TVET colleges
use the proposed safe operating window as a quick-reference guide for student training to minimize unplanned
tool failure and reduce material costs.
In general, the following recommendations are made:
Validation Experiments: Conduct confirmation experiments at the proposed optimal parameters (90
m/min, 0.1 mm/rev, 2 mm) to verify Taguchi predictions.
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Wear Mechanism Analysis: Perform SEM/EDS analysis of worn tools to identify dominant wear
mechanisms at different speed levels, explaining why 134 m/min represents a critical threshold.
MQL Investigation: Evaluate the effect of minimum quantity lubrication on the catastrophic wear
threshold, as dry conditions may accelerate the transition.
Surface Roughness Integration: Extend the study to include surface roughness and material removal rate
as responses, enabling multi-objective optimization within the safe zone.
Real-Time Monitoring: Develop a monitoring system to detect spindle or cutting force increases when
operating near or above the threshold speed.
ACKNOWLEDGMENT
The authors acknowledge the Manufacturing Industry Development Institute, Addis Ababa, Ethiopia, for
providing the institutional support for this research. The contributions of the TVET colleges that participated in
the data collection process are also gratefully acknowledged.
Declaration
The authors declare no conflicts of interest regarding the publication of this manuscript.
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