Optimization of High-Efficiency CNC Milling Processes

In the field of mechanical manufacturing, CNC milling serves as a core machining method, and the level of its process directly determines product quality and production efficiency.

Currently, in enterprise production, CNC milling processes often rely on operators setting parameters based on experience, which leads to two major issues:

• Test Equipment and Materials

The test equipment selected is the industry-standard VM C850 CNC milling machine, with a maximum spindle speed of 8,000 rpm and a maximum feed rate of 8,000 mm/min.

The cutting tools used are standard carbide-coated tools commonly found in the workshop, with the grade WC-Co and a TiAlN coating; the tool specifications are φ10 mm end mills.

No need to purchase specialized equipment, lowering the barrier to entry for companies.

The following two core machining materials used in the workshop were selected for testing:

① 45 steel: quenched and tempered (hardness 220–250 HBW), dimensions 100 mm × 100 mm × 50 mm, used for shaft and flange components.

② 6061 aluminum alloy: aged condition (hardness 90–110 HBW), dimensions 100 mm × 100 mm × 50 mm, used for lightweight brackets and housing components.

• Design of Experimental Parameters

Cutting parameters are key factors influencing efficiency, quality, and cost.

This paper focuses on the three most critical parameters in CNC milling: cutting speed (v_c), feed rate (f), and depth of cut (a_p).

A two-step design approach combining “single-factor preliminary tests” and “orthogonal experiments” is adopted to avoid the uncertainty in parameter ranges associated with direct orthogonal experiments.

(1) Single-Factor Preliminary Test

Using the single-factor method, the effects of different parameters on machining efficiency (volume of material removed per unit time), tool wear (rake face wear VB), and surface quality (surface roughness value Ra) were tested separately to determine the parameter levels for the orthogonal test. The results are as follows.

1) 45 Steel: Cutting speed v_c = 80–140 m/min (efficiency is too low below 80 m/min, and tool wear increases sharply above 140 m/min);

Feed rate f = 0.10–0.20 mm/r (below 0.10 mm/r results in low efficiency; above 0.20 mm/r causes surface roughness to exceed tolerances);

Depth of cut a_p = 1.5–3.0 mm (below 1.5 mm requires multiple passes; above 3.0 mm causes spindle load to exceed limits).

2) 6061 Aluminum Alloy: Cutting speed v_c = 200–350 m/min (below 200 m/min, built-up edge is likely to form; above 350 m/min, vibration intensifies);

Feed rate f = 0.15–0.25 mm/r (below 0.15 mm/r results in low efficiency; above 0.25 mm/r increases the risk of tool chipping);

Back cutting depth a_p = 2.0–4.0 mm (aluminum alloy has good plasticity and can withstand a larger back cutting depth; above 4.0 mm, tool rigidity is insufficient).

(2) Orthogonal Design

Based on the reasonable ranges of each parameter determined during the preliminary testing phase, this study employed an L₉(3⁴) orthogonal design to develop a comprehensive orthogonal experimental plan.

This orthogonal table consists of a 4-column, 3-level structure, in which three columns correspond to the three key experimental parameters, while the fourth column serves as a dummy column specifically designated for subsequent error analysis and significance testing.

Each experimental group was repeated three times under identical conditions.

After all replicate data were rigorously recorded, the arithmetic mean was calculated as the final result for that experimental group, thereby effectively reducing the impact of random errors and enhancing the stability of the data and the reliability of the results.

Analysis of Cutting Parameter Optimization

Through a carefully designed orthogonal experimental design, multiple sets of experimental data covering the three key dimensions of “efficiency, quality, and cost” were systematically collected.

Based on this data, range analysis and a comprehensive scoring method were employed to quantitatively evaluate and compare the effects of various parameter combinations, ultimately determining the optimal cutting parameter combinations suitable for different material properties.

This approach ensures machining efficiency while fully considering economic requirements in actual production, achieving an effective balance between efficiency metrics and cost control.

• Evaluation Criteria and Weighting

Weights for evaluation criteria are set based on the company’s production priorities (efficiency > cost > surface quality).

(1) Machining Efficiency (A)

Weight: 40%. Measured by “material removal rate per unit time (cm³/min)”; a higher value is better.

(2) Tool Life (B)

Weighted at 35%, measured by “machining time (min) until the tool reaches VB = 0.3 mm”; a higher value is better.

(3) Surface Roughness (C)

Weighted at 25%, measured by the “surface roughness value Ra (μm)”; the lower the value, the better.

For 45 steel, Ra ≤ 1.6 μm is required; for 6061 aluminum alloy, Ra ≤ 3.2 μm is required.

Using the “standardized scoring method,” each indicator is converted to a unified score ranging from 0 to 100.

The composite score is calculated as 40%A + 35%B + 25%C.

The parameter combination with the highest composite score is the optimal solution.

• Results of Cutting Parameter Optimization for 45 Steel

Partial data and composite scores from the orthogonal experiments on 45 steel are shown in Table 1 (the complete set of experiments includes 9 data sets; only key sets are presented here).

Table 1: Selected Data and Overall Scores from Orthogonal Tests on 45 Steel

Based on range analysis, the priority of factors affecting the overall score for 45 steel is as follows: depth of cut (a_p) > cutting speed (v_c) > feed rate (f).

When a_p = 2.5 mm, the required material removal per pass is ensured without excessively increasing the spindle load; when v_c = 120 m/min, a balance is achieved between tool wear and efficiency; when f = 0.15 mm/r, surface roughness meets requirements and efficiency is optimized.

The optimal parameter combination for 45 steel is ultimately determined to be: v_c = 120 m/min, f = 0.15 mm/r, a_p = 2.5 mm.

• Results of Cutting Parameter Optimization for 6061 Aluminum Alloy

The machinability of 6061 aluminum alloy is superior to that of steel; therefore, parameter optimization focuses more on “high efficiency” and “chipping prevention.”

Partial data and comprehensive scores from the orthogonal experiments on 6061 aluminum alloy are shown in Table 2 (the complete experiment includes 9 sets of data; only key sets are selected here).

Range analysis indicates that the priority of factors affecting the comprehensive score for 6061 aluminum alloy is: cutting speed v_c > depth of cut a_p > feed rate f.

When v_c = 300 m/min, chip buildup can be effectively prevented (chip buildup in aluminum alloy tends to occur when v_c < 250 m/min), and tool wear is moderate; when a_p = 3.0 mm, efficiency and tool rigidity are balanced; when f = 0.20 mm/r, surface roughness meets specifications and feed resistance is low.

The optimal parameter combination for 6061 aluminum alloy was ultimately determined to be: v_c = 300 m/min, f = 0.20 mm/r, a_p = 3.0 mm.

Table 2: Selected Data and Overall Scores from Orthogonal Tests on 6061 Aluminum Alloy

Production Adaptability Validation

Optimized parameters must be validated in actual production environments to ensure their practical applicability.

This paper examines two typical production scenarios in the workshop—batch processing and multi-product changeovers—and compares production metrics before and after optimization to verify the process’s adaptability and practicality, thereby selecting the most suitable solution.

• Verification in Mass Production Scenarios

Common parts used by enterprises were selected: (1) A 45 steel flange, 80 mm in diameter and 20 mm thick, requiring end face milling and bolt hole milling; (2) A 6061 aluminum alloy bracket, measuring 120 mm × 80 mm × 15 mm, requiring contour and groove milling.

Fifty units of each part were produced in batches; Table 3 shows a comparison of key performance indicators before and after optimization.

As shown in Table 3, the optimized parameters performed exceptionally well in batch machining, with efficiency increasing by over 28% and tool life extending by over 30%.

Meanwhile, surface quality remained unchanged, fully meeting the enterprise’s “high-efficiency + low-cost” requirements for batch production.

Table 3: Comparison of Key Metrics Before and After Batch Processing Optimization

• Verification of Multi-Product Changeover Scenarios

Small-batch, multi-product orders account for 40% of the company’s total orders, making process changeover efficiency a key indicator of production adaptability.

We selected a sequence of changeover orders consisting of 10 pieces each of “45 steel flanges → 6061 aluminum alloy brackets → 45 steel bushings” to compare “parameter tuning time” and “first-piece yield rate” before and after optimization.

Table 4 shows a comparison of these metrics before and after the optimization of multi-product changeovers.

Table 4. Comparison of Metrics Before and After Optimization of Multi-Product Changeover

Following optimization, debugging time was significantly reduced.

The primary reason for this is that a “material-parameter” correspondence table was created based on optimal parameters, allowing operators to simply enter the parameters from the table rather than performing repeated test cuts.

Additionally, since the parameters have been validated through testing, the first-piece pass rate has increased from 68% to 93%, reducing rework waste and fully meeting the adaptability requirements of multi-product production.

Mechanisms of Tool Wear and Measures to Extend Tool Life

Tool wear is a key component of CNC milling costs.

Building on parameter optimization, this study further analyzes the mechanisms of tool wear and proposes targeted measures to extend tool life, thereby developing a dual-optimization approach combining “parameters and operations” to enhance the practicality of the process.

• Analysis of Tool Wear Mechanisms

Observation of the wear surfaces of the tools after testing using a scanning electron microscope (SEM) revealed differences in the wear mechanisms among various materials.

(1) For the machining of 45 steel, the primary wear modes are abrasive wear and chemical wear.

The hard phase (Fe₃C) in 45 steel causes scratching on the tool surface (abrasive wear), while at high temperatures, the tool coating (TiAlN) reacts chemically with the workpiece material (TiAlN + Fe → TiFe + AlN), causing the coating to peel off and exacerbating wear.

(2) Machining of 6061 Aluminum Alloy:

The primary forms of wear are adhesive wear and diffusion wear.

Aluminum alloys have good plasticity and tend to adhere to the tool edge during cutting (adhesive wear), forming built-up edges; at high temperatures, aluminum diffuses into the tool substrate (WC-Co), causing a decrease in the strength of the tool edge and leading to signs of “chipping.”

• Measures to Extend Tool Life

Based on the mechanisms of wear, we propose three practical operational recommendations to further extend tool life.

(1) Optimization of Cooling Methods

For 45 steel machining, replace the original water-jet cooling with oil mist cooling.

Oil mist penetrates the cutting zone, lowering the cutting temperature (from 380°C to 250°C) and reducing chemical wear.

For aluminum alloy machining, replace the original no-cooling method with compressed air cooling to prevent corrosion caused by the reaction between cutting fluid and aluminum alloy, while simultaneously blowing away chips to reduce adhesive wear.

(2) Tool Edge Treatment

Before using new tools, chamfer the cutting edges with an 800-grit grinding wheel (chamfer width 0.1–0.2 mm) to prevent edge chipping; for tools used in 45 steel machining, lightly regrind the cutting edges with a diamond grinding wheel after every 20 parts to remove abrasive particles and restore cutting performance.

(3) Cutting Path Optimization

When machining multi-cavity parts, use a helical approach instead of the original vertical approach to reduce impact loads during tool engagement; for contour milling, use climb milling instead of the original down-milling to reduce friction resistance between the tool and workpiece and minimize abrasive wear.

After implementing these measures, the tool life for machining 45 steel increased from 42 parts per tool to 50 parts per tool, a 19.0% improvement; the tool life for machining aluminum alloy increased from 90 parts per tool to 105 parts per tool, a 16.7% improvement, further reducing machining costs.

• Standardization Plan for CNC Milling Processes

To ensure that optimization results are implemented in the enterprise over the long term and to prevent a return to “experience-based operations” after parameter optimization, we have developed standard process cards for CNC milling of various materials (see Table 5) based on the aforementioned research, thereby establishing a standardized and replicable process system.

The standard cards also include principles for parameter adjustment: when material hardness increases by 50 HB, the cutting speed should be reduced by 15%; when the part wall thickness is <5 mm, the depth of cut should be reduced by 20%.

This ensures that operators can quickly adjust parameters when faced with material variations or part changes, further enhancing process adaptability.

Following the implementation of the standard cards, the process standardization rate in the company’s CNC milling workshop increased from 35% to 90%, and the training period for new employees was reduced from one month to two weeks, completely resolving the issue of reliance on experience.

Table 5. Standard Process Cards for CNC Milling of Multiple Materials

Conclusion

Centered on industrial production practices, this paper has completed a study on high-efficiency cutting processes for CNC milling through experimental optimization and on-site validation.

The main findings are as follows:

1) Optimal cutting parameter combinations for 45 steel and 6061 aluminum alloy were determined, addressing the industry’s pain points of “low efficiency and high costs.”

2) The wear mechanisms of cutting tools for different materials were revealed, and supplementary optimization measures—combining “cooling methods, edge treatment, and cutting paths”—were proposed to further extend tool life, thereby establishing a dual optimization system of “parameters and operations.”

3) We developed standard process cards for CNC milling of multiple materials, reducing setup time for switching between product varieties, improving first-piece pass rates, and resolving adaptability issues in small-batch, high-variety production. These cards possess direct practical and promotional value.

Future research can be expanded to difficult-to-machine materials such as stainless steel and titanium alloys, further refining the process cards to provide enterprises with more comprehensive CNC milling process solutions.