Evaluation of machining parameters on specific cutting energy consumption in the turning of Al 7075 alloy in dry conditions using Taguchi and desirability approach

Evaluation of machining parameters on specific cutting energy consumption in the turning of Al 7075 alloy in dry conditions using Taguchi and desirability approach

ABSTRACTFlail forage harvesting machines are disadvantageous compared to other silage machines because of the uneven length of the chop obtained, the low distribution of the appropriate chopping length in the entire chopped material, and the high energy consumption. This issue is tackled by employing artificial neural networks (ANN), which serve as versatile mathematical instruments capable of capturing data. In this article, six different knife peripheral speeds (KPS) and three different shear bar (SB) positions were tested to determine the combination of KPS and SB positions that increased the proportion of suitable chopping length (CL) for silage and decreased the specific cutting energy consumption (SCEC) among all chopped material. The results have shown that, depending on the increase in KPS, the average CL varied between 165.28, 127.30, 100.24, 83.55, 77.06, and 65.09 mm. Depending on no shear bar (), shear bar positioned parallel to the feed unit (), and shear bar positioned at an angle of 45° to the feed unit () the average CL was determined as 114.73, 99.65, and 94.88 mm, respectively. Depending on the increase in KPS, the SCEC values vary between 0.97, 1.49, 1.89, 2.20, 3.05, and 4.12 kWh t−1. Depending on , , and the SCEC values were determined as 1.64, 3.05, and 2.17 kWh t−1, respectively. The effects of KPS and SB positions on CL and SCEC were statistically very significant (p < 0.001). An ANN model with a 3‐(5‐5)‐1 architecture, utilizing a backpropagation learning algorithm, was developed to forecast SCEC. This model outperformed traditional statistical models and was constructed using data on KPS, SB, and CL. The ANN model exhibited the highest efficiency, outperforming the polynomial models. In this particular ANN model, key metrics such as coefficient of determination (R2), root mean square error (RMSE), and mean error (ε) (0.9970%, 0.0159%, and 3.86% respectively) are significantly positive. This research convincingly demonstrated the efficacy of ANN in accurately predicting SCEC, leveraging data on KPS, SB, and average CL.

The effects of various moisture conditions on the specific cutting energy of the longitudinal cut (90o-0o) in Eucalyptus urophylla S.T. Blake and Corymbia citriodora Hill & Johnson were studied. The boards were submitted, in triplicate, to three different moisture contents: 2% (kiln dried), 12% (equilibrium moisture content in an air conditioning room) and 75% (saturated in water submersion).The material was processed with a circular saw to obtain the specific cutting energy consumption of the longitudinal cut (90o-0o). The data obtained were submitted to analysis of variance, F test and Scott-Knott test, at 5% of significance. The results showed no influence of the species on the specific cutting energy. However, the moisture content significantly influences the specific cutting energy .There was a 44% increase in the specific cutting energy consumption with moisture content reduced from 75% (saturated condition) to 12% (equilibrium condition) and 54% reduction in cutting force when wood dried from 12% to 2% (dry condition) moisture.

Aiming at the complex nonlinear dynamic problem of cutting hard rock with diamond sawblades, the process of hard rock cutting with a diamond sawblade was studied based on ANSYS/LS‐DYNA. A numerical simulation model of a diamond sawblade cutting hard rock with different rotation speeds, feed speeds, and cutting depths was established to study the effects of the cutting parameters on the cutting force and specific cutting energy consumption and on the damage to the rock. The numerical simulation results demonstrated that the feed speed and cutting depth of the diamond saw are quadratically correlated with the cutting force, but the rotation speed is negatively linearly correlated. The damage region of the rock is positively correlated with the feed speed and cutting depth of the diamond sawblade and has a negative correlation with the rotation velocity. The cutting parameters have a great influence on specific cutting energy consumption. Analysis of the relationship among the cutting parameters and the specific cutting energy with multivariate linear fitting indicated that the cutting speed and cutting depth have a great influence on the cutting energy.

Performance of surface textured tools during machining of Al-Cu/TiB2 composite

A novel approach to predicting surface roughness based on specific cutting energy consumption when slot milling Al-7075

This study presents the development of a novel Specific Cutting Energy (SCE) based process map for turning of Al 6061 T6 alloy from conventional to high-speed machining range. The newly developed SCE map for turning process was compared with already published SCE maps for orthogonal machining. The comparison of maps revealed that SCE consumption trends observed in turning process are similar to those observed in orthogonal machining. Low values of SCE were observed at high cutting speeds and high feed rates that demonstrate the benefit of high-speed machining. Similar to the orthogonal machining SCE map, a high energy consumption zone named as “avoidance zone” was observed at high cutting speeds and low feed rates. Surface roughness analysis performed in the avoidance zone established the presence of built-up-edge on cutting inserts that not only resulted in high energy consumption but also deteriorated the surface finish of the machined part. Furthermore, statistical analysis of experimental data also revealed the significant effect of tool nose radius on SCE consumption in high-speed machining range. This significance of tool nose radius for SCE consumption has not been reported earlier in literature.

The major source of environment and economic impact of machine tools has been attributed to their energy consumption. This article, therefore, proposes a novel energy mapping approach to evaluate specific cutting energy consumption with respect to cutting parameters. Unlike the studies presented earlier, which are machine-tool-specific, this study focuses on the basic tool–workpiece interaction for energy consumption analysis. The presented energy map reveals different energy consumption regions at varying machining parameters (feed and speed) during orthogonal machining of Al 6061-T6 alloy. The chip formation analysis indicates a strong correlation with the different energy consumption regions identified on the energy map. It has been observed that feed is the major contributing factor towards shear plane angle during chip formation as compared to cutting speed. Therefore, increasing feed results in a higher shear angle and consequently lowering the specific cutting energy as indicated on the energy map. Selection of machining parameters corresponding to the lowest specific cutting energy consumption region, as identified on the energy map, can result in energy savings up to 27% per kg of material removed. The developed map can be used for selection of suitable energy-efficient cutting parameters.

Recent researches in machining have revealed that electricity consumption of machine tools accounts for 90% of their environmental impact. Therefore, minimization of energy consumption will not only enhance its economic viability but will also reduce CO2 emissions. Most energy consumption studies present in literature focus on machining at low speeds (up to 500 m/min), whereas the specific cutting energy and power consumption trends at higher speeds have not been thoroughly investigated. This study analyses energy consumption in the machining of aluminium alloy Al-6061 T6 at high cutting speeds (up to 1000m/min and feeds up to 0.4 mm/rev). Full factorial experiments with three replicates were performed for orthogonal machining of AL-6061 T6 alloy which is one of the widely used materials in aerospace, automobiles, defence, sports and biomedical industries. A strict power measurement protocol was followed in accordance with CO2PE! (Cooperative Effort in Process Emission) proposed taxonomy. All the experiments were performed by unused inserts, therefore tool wear effect was not considered for power and energy calculations. The results were analysed using ANOVA and the contribution of speed and feed on energy consumption were quantified. Energy consumption map was prepared for varied speeds and feeds that revealed the presence of the optimum energy zones.

Cooling rates in cast iron significantly impact its microstructure, leading to bainitic transformation instead of ferritic structures, resulting in microstructures with higher pearlite content and even cementite formation. Consequently, this transformation causes hardness values to vary between 160 and 320 HB, directly affecting the material’s machinability. Energy efficiency has become a critical focus in sustainable production techniques and cost-effective machining processes. This variation directly influences machinability, with higher hardness generally improving surface quality. Energy efficiency in machining is crucial for sustainable production, and Specific Cutting Energy Consumption (SCEC) has become a key metric in evaluating machinability. Using genetic algorithms (GA) and Response Surface Methodology (RSM), this study optimized machining parameters for energy consumption and surface finish. GA results indicated that a cutting speed of 200 m/min and a feed rate of 0.15 mm/rev minimized surface roughness to 1.359 Ra while reducing Specific Energy Consumption (SEC) from 3.25 to 2.83 Wh/mL. The lowest surface roughness (1.0 µm) was observed at a hardness of 320 HB, with the same cutting parameters. RSM analysis identified optimal parameters as a cutting speed of 150–200 m/min, a feed rate of 0.2 mm/rev, and a hardness of 220–245 HB, balancing energy efficiency and surface quality. ANOVA showed that cutting speed and feed rate contributed to 30% of the surface roughness variability and 45% of the energy consumption variability.

PurposeThe aim of the study is to optimize the cutting parameters (cutting tool diameter, cutting speed and feed) to minimize energy consumption and surface roughness in the slot milling process of AISI 316 stainless steel on CNC milling machine.Design/methodology/approachGrowing environmental concerns and cost reduction efforts around the world have made energy efficiency in manufacturing processes a priority goal. Improving energy efficiency in the machining sector is one of the biggest challenges in this area, and slot milling is a critical manufacturing process that directly affects energy consumption. Cutting power, cutting force and surface roughness values were measured during the experimental process. In addition, energy performance of the process was evaluated by calculating specific energy consumption (SEC) and specific cutting energy consumption (SCEC). Experimental data were modeled using machine learning methods of regression analysis and artificial neural networks (ANN).FindingsAs a result, the lowest SEC and SCEC values, that is the highest energy efficiency, were obtained at 12 mm tool diameter, 75 m/min cutting speed and 0.25 mm/tooth feed. In addition, the optimum cutting parameters for different machining scenarios (roughing and finishing) were determined taking into account the purposes of the machining process (max. or min of energy efficiency, machining time, surface quality, etc.). The optimum cutting parameters for general purpose slot milling and acceptable machining purposes were found to be 12 mm tool diameter, 150 m/min cutting speed and 0.15 mm/tooth feed.Originality/valueThis study emphasizes the critical importance of energy efficiency and the correct selection of machining parameters for sustainable manufacturing practices.HighlightsSlot milling cutting performance of AISI 316Measurement of cutting power, cutting force and surface roughnessPrediction with Regression and ANN methods

Modern technologies for continuous carbonization of Eucalyptus sp. require special care in wood cutting procedures. Choosing the right tool, cutting speeds and feed rates is important to manage time and energy consumption, both of which being critical factors in optimizing production. The objective of this work is to examine the influence of machining parameters on the specific cutting energy consumption of Eucalyptus sp. stands MN 463 and VM 01, owned by V&M Florestal. Tests were performed at the Wood Machining Laboratory of the Federal University of Lavras (DCF/UFLA). Moist logs 1.70m in length were used. The experiment was set up using a 3 x 3 x 4 x 2 factorial design (cutting speed x feed rate x number of teeth x tree stand). Results were subjected to analysis of variance and means were compared by the Tukey test at the 5% significance level. Greater cutting speeds, lower feed rates and the 40 teeth circular saw consumed more specific energy. Stand MN 463 consumed more specific energy. The combination of cutting speed 46 m.s-1, feed rate 17 m.min-1 and 24 teeth circular saw produced better specific energy consumption results for stand MN 463. As for stand VM 01, the combination of cutting speed 46 m.s-1, feed rate 17 m.min-1 and 20 teeth circular saw resulted in lower specific energy consumption.

Investigation of effect on energy consumption of surface roughness in X-axis and spindle servo motors in slot milling operation

This study focuses on optimizing the machinability of Al-Fe-Cu (8000 series) alloys by developing new compositions with varying Fe and Cu contents and evaluating their mechanical, microstructural, and energy performance. For this purpose, 6061 Al alloy was melted in an induction furnace and cast into molds, and samples containing 2.5% and 5% Fe were produced. Microstructural features were analyzed using Python-based image processing, while Specific Energy Consumption (SEC) theory was applied to assess machining efficiency. An alloy with 2.5% Fe and 2.64% Cu showed superior mechanical properties and the lowest energy consumption. Increasing cutting speed and depth of cut notably decreased SEC. Machine learning (ML) analysis confirmed strong predictive capability, with R2 values above 0.80 for all models. Decision Tree (DT) achieved the highest accuracy for SEC prediction (R2 = 0.98634, MAE = 0.02209, MSE = 0.00104), whereas XGBoost (XGB) performed best for SCEC (R2 = 0.96533, MAE = 0.25578, MSE = 0.10178). Response Surface Methodology (RSM) optimization further validated the significant influence of machining parameters on SEC and specific cutting energy consumption (SCEC). Overall, the integration of machine learning (ML), response surface methodology (RSM), and energy equations provides a comprehensive approach to improve the machinability and energy efficiency of 8000 series alloys, offering practical insights for industrial applications.

Specific cutting energy consumption in high-speed orthogonal machining of Al 6061-T6 alloy has been analyzed in this work. The evaluated values of specific cutting energy are presented as an energy map developed over a cutting speed-undeformed chip thickness grid. Different regions characterized by energy consumption have been defined on the developed map. Very low values of specific cutting energy (up to 0.32 J/mm3) were observed for Al 6061-T6 alloy while machining over the cutting speed of 1500 m/min. Such low energy values have not been reported earlier in literature and they demonstrate another benefit of high-speed machining along with better surface finish, low cutting forces, and high production rate. The developed energy map revealed the presence of a very high energy zone in the midst of a comparatively low energy consumption region. A detailed analysis was performed to investigate the formation of this high energy zone or “avoidance zone.” The analysis of results revealed excessive built-up edge formation within this zone.