Optimization of CNC Turning Operation Parameters for H-13 Tool Steel Towards Sustainable Manufacturing

Nickel-based H-13 tool steel is renowned for its heat resistance, particularly during the machining process. Optimizing turning parameters is crucial for enhancing efficiency, reducing waste, and minimizing environmental impacts across various industries. Many researchers have focused on Al6061 alloy, noting that speed and feed are the major influencing parameters in turning operations. The Taguchi experimental design has proven helpful in achieving a good surface finish at high cutting speeds [1]. Rao et al. [2] investigated Al6315 alloy, performing operations on a CNC machine by varying cutting velocity, feed, and depth of cut. They recorded responses such as machining rate and surface roughness, utilizing multi-objective optimization TOPSIS. Al7075 grade aluminum alloy was also studied to optimize material removal rate and machining time. Using a carbide tool for the turning operation, it was observed that speed is the most influential parameter compared to feed and depth of cut [3].

Sustainable machining practices, which consider the lifecycle implications of parameter choices, enable manufacturers to improve productivity and quality while maintaining environmental responsibility [4]. Turning operations are integral to manufacturing and prevalent in the automotive, aerospace, and agriculture industries. With evolving technologies and market requirements, it is imperative to optimize input parameters in turning operations to meet these demands. Sustainability in machining involves improvements in production rate, coolant consumption, tool life, power consumption, and emissions, along with achieving quality surface finish and dimensional accuracy. Optimized parameters are key to enhancing manufacturing performance and reducing emissions [5].

Sustainable manufacturing has grown widely owing to recent environmental issues. This study aims to develop a multi-objective, multi-pass turning optimization model to determine the optimal cutting parameters, including spindle rotation speed, feed rate, depth of cut, and number of roughing passes. The optimization model considers several criteria in the key metrics of sustainable manufacturing, i.e., energy consumption, carbon emissions, production time, and production cost. A numerical example is provided to show the application of the model, including sensitivity analysis, to study the effects of several cutting parameters on the objective functions [5]. The model can be used by manufacturing industries to improve their manufacturing process efficiency and simultaneously produce products that support sustainable manufacturing [6]. Researchers have extensively studied various materials and machining conditions to optimize parameters. For instance, the Al6061 alloy showed that speed and feed significantly influence turning operations, achieving a good surface finish at high cutting speeds through the Taguchi experimental design. Rao et al. [2] optimized machining rate and surface roughness for Al6315 alloy using CNC machines and multi-objective optimization TOPSIS by varying cutting velocity, feed, and depth of cut. For the Al7075 alloy, speed was identified as the most influential parameter in optimizing material removal rate and machining time using a carbide tool. In the automotive industry, EN-45 spring steel’s optimal parameters were determined using the Taguchi L9 method, and Kumar et al. [5] employed the Taguchi L16 orthogonal array and regression analysis for further optimization.

The aim of this research work is to optimize material removal process parameters using CNC turning operations to get the desired value of surface roughness. With an orthogonal array of L27, the Taguchi approach is used, where three levels of each parameter are taken into account, which are cutting speed, feed, and nose radius. The experiments were done on EN8D carbon steel, and a carbide insert was used for a total of 27 experiments. Using the surface roughness tester, the roughness values were obtained. An analysis of variance (ANOVA) was executed on Minitab software to recognize the impact of individual machining parameters on surface roughness. A regression model was developed from the experimental data to validate the findings of the random experiment [7]. The Taguchi technique minimizes manufacturing variations, while ANOVA studies relationships between variables. TOPSIS helps in decision-making in manufacturing processes. Palaniappan et al. [8] investigated optimal parameters for material removal rate (MRR) and surface roughness (Ra) in aluminum 6082 alloy, finding feed rate to be the dominant factor affecting surface roughness.

A novel method, TOPSIS, was recommended by Kumar and Singh [9] to optimize the turning operation parameters on GFRP composites due to the non-requirement of computing challenging modeling formulations or process simulations. The AHP and TOPSIS methods have been recommended for parameter optimization, aiding material selection decisions for hydroforming process experimentation. Turning machining deals with removing unwanted material from the workpiece in the form of chips to get the required dimension. Hence, industries face the inevitable challenge of reducing costs as well as optimizing the machining operation. The response characteristics, such as material removal rate (MRR), surface roughness (Ra), and tool tip temperature, are greatly influenced by the input cutting parameters like speed, feed rate, and depth of cut [10]. Industries must consider multiple performance characteristics simultaneously, as focusing on a single objective may appear as a loss for the rest of the objectives. Hence, multi-objective optimization techniques may be suitable for experimentation. H-13 is commonly used in industries to perform different types of work. Response surface methodology (RSM) was used to determine the optimal value of cutting parameters, and the significance of the cutting parameters was determined and calculated using analysis of variance (ANOVA) with Central Composite Design (CCD) [11].

Despite extensive research on parameter optimization for efficiency and productivity [12, 13], there’s a notable gap in assessing sustainability implications. Our study aims to bridge this gap by systematically evaluating the interplay between optimized turning parameters and sustainability metrics. This involves considering material usage, energy consumption, waste generation, and environmental impact. By integrating these aspects, we provide actionable insights for sustainable machining practices.

Surface finish [14] in manufacturing is critical for ensuring quality, avoiding secondary operations, and improving performance aspects like fatigue strength and corrosion resistance. Surface finish is influenced by input parameters such as speed, feed, and depth of cut. The heat generated during machining, primarily due to plastic deformation and friction, affects material properties and tool life. Quality machining products reduce manufacturing costs and enhance effectiveness. The cutting conditions, including speed, feed, and depth of cut, significantly impact performance characteristics. High production rates depend on optimized turning input parameters, with tools like PCD inserts achieving lower surface roughness [15]. Our study represents an advancement in sustainable machining by integrating parameter optimization with sustainability considerations. By addressing the often-overlooked relationship between temperature effects, parameter optimization, and sustainability, we empower manufacturers to make informed decisions balancing productivity with environmental responsibility. Temperature plays a crucial role, influencing material properties, tool life, and process performance. Excessive heat can degrade material integrity, increase tool wear, and necessitate frequent tool changes, thus raising production costs and environmental impact due to air and water pollution.

This work focuses on optimizing material removal process parameters for CNC turning operations, particularly studying surface roughness behavior. Multi-Criteria Decision Making (MCDM) methods, including the TOPSIS ranking method, are used for parameter selection. Employing an orthogonal L9 array with the Taguchi approach under wet conditions, we develop a regression model to validate findings through random experiments. This continuous need for parameter optimization in production departments enhances productivity and sustainability in manufacturing.

The selection of specific input and output parameters, as well as their ranges, for this study was guided by an extensive review of relevant literature. The input parameters were chosen based on their significant impact on the manufacturing process, as identified in previous research. For instance, studies have shown that parameters such as cutting speed, feed rate, and depth of cut are critical in influencing the quality and efficiency of machining operations.In Table 6, the ‘Machining Condition’ category, specifically ‘Wet,’ refers to the use of coolant during the cutting operation. The application of coolant is crucial for achieving a good surface finish, as it helps to reduce the temperature and friction between the cutting tool and the workpiece. This categorization ensures clarity and emphasizes the role of coolant in enhancing the machining process’s effectiveness and outcome quality.

The TNMG 160404 carbide cutting insert and the PTGRNR-25-25 M16 050 tool holder were chosen for their versatility, geometry, chip control, stability, and material compatibility. The TNMG 160404 insert is widely used for both roughing and finishing operations across various materials, including steel, stainless steel, and cast iron, thanks to its neutral rake angle and effective chipbreaker design, which enhance chip formation and evacuation. Its specific carbide grade and coating improve wear resistance for tougher materials. The PTGRNR-25-25 M16 050 tool holder provides excellent rigidity and stability, reducing vibration and chatter during machining, and accommodates various inserts, allowing for quick changes and flexible tooling options. Its potential coolant-through capability aids in effective chip evacuation and cooling, especially with heat-resistant materials. This tool holder’s compatibility with the TNMG insert style and the machining requirements ensures precise and efficient turning operations, resulting in consistent performance and extended tool life.

Table 6. Selection of input and output parameters

Each input parameter in Table 6 is selected to predict the output measures, Material Removal Rate (MRR) and Surface Roughness (Ra), based on their established effects in machining processes. Cutting speed typically influences both MRR and Ra; higher cutting speeds can increase MRR but may also lead to higher Ra if not optimized. Feed rate is another critical parameter, where an increase generally boosts MRR but can negatively impact Ra by causing rougher surfaces. Depth of cut directly affects MRR, with deeper cuts removing more material per pass, but it can also increase Ra due to greater tool engagement and potential vibrations. The use of coolant (‘Wet’ condition) is expected to improve Ra by reducing heat and friction, leading to a smoother surface finish. These input parameters were chosen based on extensive literature review and their known impact on machining efficiency and surface quality, and their effects are demonstrated in the results section.

The workpiece specimens were prepared with 20mm diameter and 100mm long as shown in Figure 1. The range for the spindle speed (20 to 4000 rpm) is based on the capabilities of the CNC machine used in the experiments, which allows for a broad spectrum of speeds to accommodate various machining conditions and material types. The spindle speed was precisely controlled and monitored through CNC programming to ensure accurate and consistent application during each machining operation. For the temperature range (10 to 500℃), this wide range reflects the potential temperatures that could be encountered during different machining scenarios. The temperature of the workpiece surface was measured immediately after machining using a high-precision infrared thermometer to capture accurate temperature readings, ensuring the consistency and reliability of the data collected.

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Figure 1. Workpiece specimens

The Taylor Hobson Surtronic-3 is a highly accurate and portable surface roughness tester used for measuring surface texture and roughness parameters such as Ra, Rz, and Rt, making it ideal for quality control in various industries. The device is cleaned and calibrated before use, and the surface is prepared to ensure accurate measurements. During operation, the probe scans the surface, displaying roughness parameters that can be further analyzed or documented. The K-Type Digital Thermocouple is employed for measuring the workpiece surface temperature immediately after machining, known for its wide temperature range and rapid response time. This thermocouple provides precise temperature readings by converting thermal potential differences into digital signals. Using these high-accuracy instruments ensures the reliability and consistency of the experimental data, enhancing the overall quality and credibility of the study’s findings.

The chemical composition as presented in Table 7 of H-13 material is highly relevant to the study as each element plays a crucial role in its machining properties and the quality of the final product. H-13 steel is widely used in industries due to its excellent combination of toughness, hardness, and resistance to thermal fatigue. Elements such as carbon contribute to hardness and strength, while chromium increases hardenability and corrosion resistance. Molybdenum and vanadium enhance toughness and high-temperature strength, and silicon improves the steel’s strength and resistance to oxidation. The precise balance of these elements affects the material’s machinability, wear resistance, and surface finish quality, making H-13 an ideal choice for applications requiring durable and reliable components. Understanding the chemical composition helps in optimizing machining parameters to achieve superior performance and product quality.

Table 7. Chemical composition of H-13 tool steel

A Macpower CNC machine was used for the experimentation, providing precise control over machining parameters to ensure accurate and reproducible results. The workpieces, specifically H-13 material specimens, were prepared using a Trob machine to achieve the required dimensions. These specimens were then securely mounted in the CNC machine’s jaws, ensuring stability and alignment before initiating the cutting process. This setup and preparation process is crucial for maintaining consistency and reliability in the machining operations, allowing for the reproducibility of the study. Each experimental condition was repeated three times to ensure statistical validity and to account for variability in the machining process. During the experiments, errors were carefully monitored and handled by conducting multiple trials and averaging the results to minimize the impact of any outliers or fluctuations. If any outlier results were identified, they were scrutinized to determine their cause, and if necessary, additional experiments were conducted to verify their validity. Ultimately, outlier results were treated with caution, and efforts were made to understand the underlying reasons for their occurrence to ensure the accuracy and reliability of the experimental data.