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Sustainable machining processes are efficiently achieved using the selection of optimal parameters. In this study, the minimum quantity lubrication-assisted multi-roller burnishing (MQLAMRB) operation is proposed and optimized to reduce the total energy consumption (TE), mean roughness depth (MR), and roundness deviation (RN). Burnishing parameters are the burnishing speed (BS), depth of penetration (DOP), the quantity consumed of the lubricant (QO), and the pressure value of the compressed air (PA). The embodied energy of the lubricant (Eel) and burnishing tool (Eeb) are developed and integrated into the TE model. The artificial neural network (ANN) model of the energy consumption in the burnishing time (Ebo), MR, and RN is proposed regarding the MQLAMRB parameters. The best-selected solution is determined using an efficient glowworm swarm optimization (GSO) algorithm and the TOPSIS. The results indicated that the 4–25-21-25-3 ANN structure effectively used to construct the MQLAMRB performances. The optimal outcomes of the BS, DOP, QO, and PA are 94 m/min, 0.12 mm, 130 ml/h, and 0.7 MPa, respectively. Moreover, the TE, MR, and RN are decreased by 12.2%, 14.2%, and 42.5%, respectively. The reductions in the MR and RN of the burnished surface are 90.23% and 88.18%, respectively, as compared to the pre-machined conditions.
Trung-Thanh Nguyen; Truong-An Nguyen; Quang-Hung Trinh; Xuan-Ba Le. Multi-performance optimization of multi-roller burnishing process in sustainable lubrication condition. Materials and Manufacturing Processes 2021, 1 -21.
AMA StyleTrung-Thanh Nguyen, Truong-An Nguyen, Quang-Hung Trinh, Xuan-Ba Le. Multi-performance optimization of multi-roller burnishing process in sustainable lubrication condition. Materials and Manufacturing Processes. 2021; ():1-21.
Chicago/Turabian StyleTrung-Thanh Nguyen; Truong-An Nguyen; Quang-Hung Trinh; Xuan-Ba Le. 2021. "Multi-performance optimization of multi-roller burnishing process in sustainable lubrication condition." Materials and Manufacturing Processes , no. : 1-21.
Boosting machining quality is a prominent solution to save production costs for burnishing operations. In this work, a machining condition-based optimization has been performed to decrease surface roughness (SR) and enhance Vickers hardness (VH) of the minimum quantity lubrication-assisted burnishing operation (MQLABO). The burnishing factors are the spindle speed (S), depth of penetration (D), and the air pressure (P). The burnishing trails of the hardened material labeled 40X have been conducted on a milling machine. The adaptive neuro-based-fuzzy inference system (ANFIS) was used to construct the correlations between the process inputs and MQLABO responses. The non-dominated sorting genetic algorithm-II (NSGA-II) is utilized to determine the optimal parameters. The scientific outcomes revealed that the optimal values of the S, D, and P are 800 RPM, 0.09 mm, and 4.0 Bar, respectively. The SR is decreased by 53.8%, while the VH is enhanced by 3.1%, respectively, as coBarred to the initial values.
Khanh Can Xuan; Ba Le Xuan; An Nguyen Truong; Hung Trinh Quang; Thanh Nguyen Trung. An intelligence-based optimization of the internal burnishing operation for surface roughness and vicker hardness. Transport and Communications Science Journal 2021, 72, 395 -410.
AMA StyleKhanh Can Xuan, Ba Le Xuan, An Nguyen Truong, Hung Trinh Quang, Thanh Nguyen Trung. An intelligence-based optimization of the internal burnishing operation for surface roughness and vicker hardness. Transport and Communications Science Journal. 2021; 72 (4):395-410.
Chicago/Turabian StyleKhanh Can Xuan; Ba Le Xuan; An Nguyen Truong; Hung Trinh Quang; Thanh Nguyen Trung. 2021. "An intelligence-based optimization of the internal burnishing operation for surface roughness and vicker hardness." Transport and Communications Science Journal 72, no. 4: 395-410.
Boosting energy efficiency and machining quality are prominent solutions to achieve sustainable production for burnishing operations. In this work, an effective optimization has been performed to enhance the energy efficiency (EFb) and decrease the machining noise (MN) as well as surface roughness (SR) of the internal burnishing operation. The burnishing factors are the spindle speed (S), burnishing feed (f), burnishing depth (D), and the number of rollers (N). The burnishing trails of the hardened material labeled SCr440 have been conducted on a CNC milling machine. The adaptive neuro-based-fuzzy inference system (ANFIS) was used to construct the correlations between the process inputs and burnishing responses. The entropy approach is employed to calculate the weight of each technical objective. The non-dominated sorting particle swarm optimization (NSPSO) is utilized to determine the optimal parameters. A comprehensive model of the production cost is developed to check the effectiveness of the proposed approach. The scientific outcomes revealed that the optimal values of the S, f, D, and N are 1645 RPM, 260 mm/min, 0.08 mm, and 4, respectively. The improvements in the EFb, SR, and MN are 6.98%, 25.00%, and 2.23%, as compared to the initial values. The machining cost is saved by 6.2% at the optimal solution. Moreover, the scientific finding is a potent technical solution to enhance machining performances for the burnishing process of various components having internal holes.
Trung-Thanh Nguyen; Minh-Thai Le. Optimization of internal burnishing operation for energy efficiency, machined quality, and noise emission. The International Journal of Advanced Manufacturing Technology 2021, 114, 2115 -2139.
AMA StyleTrung-Thanh Nguyen, Minh-Thai Le. Optimization of internal burnishing operation for energy efficiency, machined quality, and noise emission. The International Journal of Advanced Manufacturing Technology. 2021; 114 (7-8):2115-2139.
Chicago/Turabian StyleTrung-Thanh Nguyen; Minh-Thai Le. 2021. "Optimization of internal burnishing operation for energy efficiency, machined quality, and noise emission." The International Journal of Advanced Manufacturing Technology 114, no. 7-8: 2115-2139.
The current work has been performed an effective optimization to decrease the total energy consumption (Etotal) and total machining time (Ttotal) with the constraint of the average roughness for the actively driven rotary turning (ADRT) of the material labeled SKD11. The optimizing factors are the tool rotational speed (vt), depth of cut (a), feed rate (f), and workpiece speed (vw). The analytical approach was used to construct the models of the Etotal and Ttotal. The weightage principal component analysis (WPCA) was applied in conjunction with the non-dominated sorting particle swarm optimization (NSPSO) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to determine the weight values of machining responses and select the best optimal solution. The scientific findings revealed that the optimal values of the vt, a, f, and vw were 78 m/min, 0.21 mm, 0.44 mm/rev., and 98 m/min, respectively. The reductions in the Etotal and Ttotal were 16.99% and 17.78%, respectively. Moreover, the proposed models of the Etotal and Ttotal were significant and could be used to predict technical performances with acceptable accuracy. The optimization technique comprising the analytical method, NSPSO, WPCA, and TOPSIS was named as a powerful approach to obtain optimal outcomes.
Trung-Thanh Nguyen. Analytical approach-based optimization of the actively driven rotary turning for environmental and economic metrics considering energy footprint of materials. Neural Computing and Applications 2021, 33, 11937 -11950.
AMA StyleTrung-Thanh Nguyen. Analytical approach-based optimization of the actively driven rotary turning for environmental and economic metrics considering energy footprint of materials. Neural Computing and Applications. 2021; 33 (18):11937-11950.
Chicago/Turabian StyleTrung-Thanh Nguyen. 2021. "Analytical approach-based optimization of the actively driven rotary turning for environmental and economic metrics considering energy footprint of materials." Neural Computing and Applications 33, no. 18: 11937-11950.
Boosting energy efficiency and machining quality are prominent solutions to achieve sustainable production for turning operations. In this work, a machining condition-based optimization has been performed to decrease the total specific energy (SEC), carbon emission (CE), and average roughness (AR) of the actively driven rotary turning (ADRT) process. The processing factors are the tool rotational speed (Tv), depth of cut (a), feed rate (fr), and workpiece speed (Wv). The turning experiments of the mold material labeled SKD11 have been conducted on a CNC lathe. The regression method is employed to develop comprehensive models of the total specific energy, carbon emissions, and average roughness. The entropy approach is then applied to drive out the weight value of each ADRT response. Finally, the non-dominated sorting particle swarm optimization (NSPSO) is utilized to determine the optimal parameters. The findings indicated that the optimal values of the Tv, a, fr, and Wv are 77 m/min, 0.32 mm, 0.25 mm/rev., and 128 m/min, respectively. The SEC, AR, and CE are decreased by 18.07%, 10.46%, and 5.02%, respectively, as compared to the initial approach. Moreover, the developed active rotary turning operation can be considered as an effective technical solution to boost the machining efficiency of hardened steels.
Trung-Thanh Nguyen; Quoc-Dung Duong; Mozammel Mia. Multi-response optimization of the actively driven rotary turning for energy efficiency, carbon emissions, and machining quality. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 2021, 1 .
AMA StyleTrung-Thanh Nguyen, Quoc-Dung Duong, Mozammel Mia. Multi-response optimization of the actively driven rotary turning for energy efficiency, carbon emissions, and machining quality. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. 2021; ():1.
Chicago/Turabian StyleTrung-Thanh Nguyen; Quoc-Dung Duong; Mozammel Mia. 2021. "Multi-response optimization of the actively driven rotary turning for energy efficiency, carbon emissions, and machining quality." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture , no. : 1.
In this paper, the investigation of chip formation of aluminum alloy in different machining strategies (i.e., micro and macro cutting) is performed to develop a holistic view of the chip formation phenomenon. The study of chip morphology is useful to understand the mechanics of surface generation in machining. Experiments were carried out to evaluate the feed rate response (FRR) in both ultra-precision micro and conventional macro machining processes. A comprehensive study was carried out to explore the material removal mechanics with both experimental findings and theoretical insights. The results of the variation of chip morphology showed the dependence on feed rate in orthogonal turning. The transformation of discontinuous to continuous chip production—a remarkable phenomenon in micro machining—has been identified for the conventional macro machining of Al alloy. This is validated by the surface crevice formation in the transition region. Variation of the surface morphology confirms the phenomenology (transformation mechanics) of chip formation.
M. Azizur Rahman; Shahnewaz Bhuiyan; Sourav Sharma; Mohammad Saeed Kamal; M. M. Musabbir Imtiaz; Abdullah AlFaify; Trung-Thanh Nguyen; Navneet Khanna; Shubham Sharma; Munish Kumar Gupta; Saqib Anwar; Mozammel Mia. Influence of Feed Rate Response (FRR) on Chip Formation in Micro and Macro Machining of Al Alloy. Metals 2021, 11, 159 .
AMA StyleM. Azizur Rahman, Shahnewaz Bhuiyan, Sourav Sharma, Mohammad Saeed Kamal, M. M. Musabbir Imtiaz, Abdullah AlFaify, Trung-Thanh Nguyen, Navneet Khanna, Shubham Sharma, Munish Kumar Gupta, Saqib Anwar, Mozammel Mia. Influence of Feed Rate Response (FRR) on Chip Formation in Micro and Macro Machining of Al Alloy. Metals. 2021; 11 (1):159.
Chicago/Turabian StyleM. Azizur Rahman; Shahnewaz Bhuiyan; Sourav Sharma; Mohammad Saeed Kamal; M. M. Musabbir Imtiaz; Abdullah AlFaify; Trung-Thanh Nguyen; Navneet Khanna; Shubham Sharma; Munish Kumar Gupta; Saqib Anwar; Mozammel Mia. 2021. "Influence of Feed Rate Response (FRR) on Chip Formation in Micro and Macro Machining of Al Alloy." Metals 11, no. 1: 159.
The burnishing process is used to enhance the machining quality via improving the surface finish, surface hardness, wear-resistance, fatigue, and corrosion resistance, and it is mostly used in aerospace, biomedical, and automotive industries to improve reliability and performance of the component. The combined turning and burnishing process is therefore considered as an effective solution to enhance both machining quality and productivity. However, the trade-off analysis between energy consumption, surface characteristics, and production costs has not been well-addressed and investigated. This study presents an optimization of the compressed air assisted-turning-burnishing (CATB) process for aluminum alloy 6061, aimed to decrease the energy consumption as well as surface roughness and to enhance the Vicker hardness of the machined surface. The machining parameters for consideration include the machining speed, feed rate, depth of cut, burnishing force, and the ball diameter. The improved Kriging models were used to construct the relations between machining parameters and the technological response characteristics of the machined surface. The optimal machining parameters were obtained utilizing the desirability approach. The energy based-cost model was developed to assess the effectiveness of the proposed CATB process. The findings showed that the selected optimal outcomes of the depth of cut, burnishing force, diameter, feed rate, and machining speed are 0.66 mm, 196.3 N, 8.0 mm, 0.112 mm/rev, and 110.0 m/min, respectively. The energy consumption and surface roughness are decreased by 20.15% and 65.38%, respectively, while the surface hardness is improved by 30.05%. The production cost is decreased by 17.19% at the optimal solution. Finally, the proposed CATB process shows a great potential to replace the traditional techniques which are used to machine non-ferrous metals.
Trung-Thanh Nguyen; Chi-Hieu Le. Optimization of compressed air assisted-turning-burnishing process for improving machining quality, energy reduction and cost-effectiveness. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 2020, 235, 1179 -1196.
AMA StyleTrung-Thanh Nguyen, Chi-Hieu Le. Optimization of compressed air assisted-turning-burnishing process for improving machining quality, energy reduction and cost-effectiveness. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. 2020; 235 (6-7):1179-1196.
Chicago/Turabian StyleTrung-Thanh Nguyen; Chi-Hieu Le. 2020. "Optimization of compressed air assisted-turning-burnishing process for improving machining quality, energy reduction and cost-effectiveness." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 235, no. 6-7: 1179-1196.
The finishing honing process is an effective machining to enhance surface properties. The objective of this work is to optimize the machining parameters, including the tangential speed (H), linear speed (L), and grit size (G) for minimizing the average roughness (Ra), maximum height roughness (Ry), and machining time (TM). The honing experiments were performed with the aids of an industrial machine and the Box–Behnken experimental matrix. The nonlinear relationships between machining parameters and honing responses were developed using response surface method models. Subsequently, two optimization techniques, including the desirability approach and non-dominated sorting genetic algorithm II (NSGA II), were used to solve the trade-off analysis among three technological responses and find the optimal factors. Finally, the machining time reductions were assessed in consideration of constrained roughness properties. The obtained results showed that surface roughness and machining time were strongly influenced by abrasive grit size, followed by the tangential speed and linear speed. The optimal values of the H, L, and G were 36.0 m/min, 9.5 m/min, and 220 FEPA, respectively. The reductions in the average roughness, maximum height roughness, and machining time are 53.13%, 8.93%, and 13.95%, respectively, as compared to common values used. Moreover, the genetic algorithm-based approach could be employed to produce reliable values in comparison with the desirability approach. The outcome is expected as a technical solution to enhance the surface properties and productivity of the finishing honing process.
Trung-Thanh Nguyen; The-Chien Vu; Quoc-Dung Duong. Multi-responses optimization of finishing honing process for surface quality and production rate. Journal of the Brazilian Society of Mechanical Sciences and Engineering 2020, 42, 1 -17.
AMA StyleTrung-Thanh Nguyen, The-Chien Vu, Quoc-Dung Duong. Multi-responses optimization of finishing honing process for surface quality and production rate. Journal of the Brazilian Society of Mechanical Sciences and Engineering. 2020; 42 (11):1-17.
Chicago/Turabian StyleTrung-Thanh Nguyen; The-Chien Vu; Quoc-Dung Duong. 2020. "Multi-responses optimization of finishing honing process for surface quality and production rate." Journal of the Brazilian Society of Mechanical Sciences and Engineering 42, no. 11: 1-17.
The combination of the turning and burnishing process is an efficient approach to improve machined quality and productivity. This paper aims to optimize energy efficiency (EF), improved hardness ratio (IHR), and decreased roughness ratio (DRR) of a new hybrid turning-burnishing process. The machining parameters are the feed rate (f), turning speed (v), depth of cut (a), burnishing pressure (p), and the diameter of the compressing ball (d). A new turning-burnishing tool using compressed air has been designed and fabricated. A set of experiments for Aluminum Alloy 5083 were performed using the Taguchi method. The weightage principal component analysis (WPCA) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) were applied to obtain the weight values and optimal outcomes. The results indicated that optimum values of the depth of cut, pressure, diameter, feed rate, and speed are 1.00 mm, 0.4 MPa, 16.00 mm, 0.084 mm/rev, and 120 m/min, respectively. The improvements in the EF and IHR are by 20.75% and 8.23% respectively, while the DDR is decreased by 19.05%, as compared to common values.
Trung-Thanh Nguyen; Mozammel Mia. Modeling and Evaluation of Energy Efficiency of New Hybrid Turning-Burnishing Process in Terms of Surface Properties. Energies 2020, 13, 4929 .
AMA StyleTrung-Thanh Nguyen, Mozammel Mia. Modeling and Evaluation of Energy Efficiency of New Hybrid Turning-Burnishing Process in Terms of Surface Properties. Energies. 2020; 13 (18):4929.
Chicago/Turabian StyleTrung-Thanh Nguyen; Mozammel Mia. 2020. "Modeling and Evaluation of Energy Efficiency of New Hybrid Turning-Burnishing Process in Terms of Surface Properties." Energies 13, no. 18: 4929.
The rotary turning is an effective manufacturing method to machine hardened metals due to longer tool life, higher production rate, and acceptable quality. However, sustainability-based optimization of the rotary turning has not been thoroughly considered because of the huge efforts. This study presents an optimization to enhance the energy efficiency (EFR), turning cost (CT), average roughness (Ra), and the operational safety (POS) for the rotary turning of the hardened steel. Four key process parameters considered are the inclined angle (α), depth of cut (ap), feed rate (f), and cutting speed (vc). The improved Kriging (IK) models were used to construct the relations between the parameters and performances. The optimum varied factors were obtained utilizing the neighborhood cultivation genetic algorithm (NCGA). The findings revealed that the performance models are primarily affected by the feed rate, depth of cut, speed, and inclined angle, respectively. The optimal values of the α, ap, f, and vc are 26°, 0.44 mm, 0.37 mm/rev, and 200 mm/min, respectively. The improvements in energy efficiency, average roughness, and cost are 8.91%, 20.00%, and 14.75%, as compared to the initial values. Moreover, the NCGA may perform an efficient operation to obtain the optimal outcomes, as compared to conventional algorithms.
Trung-Thanh Nguyen; Quoc-Dung Duong; Mozammel Mia. Sustainability-Based Optimization of the Rotary Turning of the Hardened Steel. Metals 2020, 10, 939 .
AMA StyleTrung-Thanh Nguyen, Quoc-Dung Duong, Mozammel Mia. Sustainability-Based Optimization of the Rotary Turning of the Hardened Steel. Metals. 2020; 10 (7):939.
Chicago/Turabian StyleTrung-Thanh Nguyen; Quoc-Dung Duong; Mozammel Mia. 2020. "Sustainability-Based Optimization of the Rotary Turning of the Hardened Steel." Metals 10, no. 7: 939.
The turning operation using a self-propelled rotary tool (SPRT) is efficient manufacturing for hard machining. However, optimization-based energy saving of the rotary turning has not presented because of expensive implementation. This study addresses a parameter optimization to enhance the machining rate (MR) and decrease the energy consumption (ET) as well as the machined roughness (R) for a hard turning using SPRT. The process inputs are the inclined angle (α), depth of cut (a), feed rate (f), and cutting speed (V). The hard turning runs were performed using the experimental plan generated by the Taguchi approach. The adaptive neuro-fuzzy inference system (ANFIS) was used to construct the correlations between the process inputs and responses. The analytic hierarchy process technique was adopted to explore the weight values of the outputs, and the optimum solution was obtained utilizing the adaptive simulated annealing. Moreover, an integrative approach using the response surface method and utilizing the desirability approach was employed to select the optimal outcomes and compare with the proposed technique. The findings revealed that the proposed ANFIS models minimize the predictive error in comparison with the traditional one. The accurate weights may help to select reliable optimal results. The optimal values of the α, a, f, and V are 18°, 0.15 mm, 0.40 mm/rev, and 200 mm/min, respectively. Moreover, ET and roughness are decreased by 50.29% and 19.77%, while the MR is enhanced by 33.16%, respectively, as compared to the general process.
Trung-Thanh Nguyen. An energy-efficient optimization of the hard turning using rotary tool. Neural Computing and Applications 2020, 33, 2621 -2644.
AMA StyleTrung-Thanh Nguyen. An energy-efficient optimization of the hard turning using rotary tool. Neural Computing and Applications. 2020; 33 (7):2621-2644.
Chicago/Turabian StyleTrung-Thanh Nguyen. 2020. "An energy-efficient optimization of the hard turning using rotary tool." Neural Computing and Applications 33, no. 7: 2621-2644.
The electrical discharge drilling (EDD) process is an effective machining approach to produce various holes in difficult-to-cut materials. However, the energy efficiency of the EDD operation has not thoroughly been considered in published works. The aim of the current work is to optimize varied parameters for enhancing the material removal rate (MRR), saving drilled energy (ED), and decreasing the expansion of the hole (HE) for the EDD process. Three advanced factors, including the gap voltage adjustor (GAP), capacitance parallel connection (CAP), and servo sensitivity selection (SV), are considered. The predictive models of the performances were proposed with the support of the adaptive neuro-based fuzzy inference system (ANFIS). An integrative approach combining the analytic hierarchy process (AHP) and the neighborhood cultivation genetic algorithm (NCGA) was employed to select optimal factors. The findings revealed the optimal values of the CAP, GAP, and SV were 6, 5, and 4, respectively. Moreover, the ED and HE were decreased by 16.78% and 28.68%, while the MRR was enhanced by 89.72%, respectively, as compared to the common setting values. The explored outcome can be employed as a technical solution to enhance the energy efficiency, drilled quality, and productivity of the EDD operation.
Trung-Thanh Nguyen; Van-Tuan Tran; Mozammel Mia. Multi-Response Optimization of Electrical Discharge Drilling Process of SS304 for Energy Efficiency, Product Quality, and Productivity. Materials 2020, 13, 2897 .
AMA StyleTrung-Thanh Nguyen, Van-Tuan Tran, Mozammel Mia. Multi-Response Optimization of Electrical Discharge Drilling Process of SS304 for Energy Efficiency, Product Quality, and Productivity. Materials. 2020; 13 (13):2897.
Chicago/Turabian StyleTrung-Thanh Nguyen; Van-Tuan Tran; Mozammel Mia. 2020. "Multi-Response Optimization of Electrical Discharge Drilling Process of SS304 for Energy Efficiency, Product Quality, and Productivity." Materials 13, no. 13: 2897.
Enhancing energy efficiency and product quality by means of optimal inputs is a cost-effective solution, as compared to the drastic investment. This paper aims to optimize the machining inputs to enhance energy efficiency (EF) as well as the power factor (PO) and decrease the surface roughness (Ra) for the milling process. The factors considered are the feed (f), depth of cut (a), milling speed (V), and tool radius (r). The machining operations were executed on the vertical milling under the dry condition for the stainless steel 304. A type of neutral network entitled the radius basic function (RBF) was used to render the relationships between milling inputs and performances measured. The adaptive simulated annealing (ASA) algorithm was applied to obtain the optimal values. The outcomes indicated that the milling responses are primarily influenced by a, f, V, and r, respectively. The reduction in Ra is approximately 39.18%, while the improvements in EF and PO are around 22.61% and 26.47%, respectively, as compared to the initial parameter settings. The explored findings are expected as a prominent solution for the industrial application of the dry machining. The combination of the RBF models and ASA could be considered as an efficient approach for modeling dry machining processes and generating reliable as well as feasible optimal results.
Trung-Thanh Nguyen; Truong-An Nguyen; Quang-Hung Trinh. Optimization of Milling Parameters for Energy Savings and Surface Quality. Arabian Journal for Science and Engineering 2020, 45, 9111 -9125.
AMA StyleTrung-Thanh Nguyen, Truong-An Nguyen, Quang-Hung Trinh. Optimization of Milling Parameters for Energy Savings and Surface Quality. Arabian Journal for Science and Engineering. 2020; 45 (11):9111-9125.
Chicago/Turabian StyleTrung-Thanh Nguyen; Truong-An Nguyen; Quang-Hung Trinh. 2020. "Optimization of Milling Parameters for Energy Savings and Surface Quality." Arabian Journal for Science and Engineering 45, no. 11: 9111-9125.
In the current work, the optimal factors are selected to achieve the improvements in the energy consumption (EB), power factor (PB), decreased roughness (DR) and improved surface hardness (IH) for the roller burnishing operation. The process inputs are the burnishing speed (V), the feed (f), and the depth (d). A hybrid approach comprising the principal component analysis and Technique for Order of Preference by Similarity to Ideal Solution was used to explore the weight values of burnishing performances and select the optimum parameters. Moreover, another optimization technique employing the response surface method and archive-based micro-genetic algorithm was adopted to identify the optimal outcomes in the continuous domain. The main findings showed the performances measured are primarily affected by the burnishing feed, depth and speed, respectively. The energy consumption and roughness are approximately decreased by 31.46% and 7.41%, while the power factor and hardness are improved by 17.47% and 43.09%, respectively, as compared to the general process. The outcomes and findings of the investigated work can be used for further research in sustainable design and manufacturing as well as directly used in the knowledge-based and expert systems for burnishing applications in industrial practices.
Trung-Thanh Nguyen; Le-Hai Cao. Optimization of the Burnishing Process for Energy Responses and Surface Properties. International Journal of Precision Engineering and Manufacturing 2020, 21, 1143 -1152.
AMA StyleTrung-Thanh Nguyen, Le-Hai Cao. Optimization of the Burnishing Process for Energy Responses and Surface Properties. International Journal of Precision Engineering and Manufacturing. 2020; 21 (6):1143-1152.
Chicago/Turabian StyleTrung-Thanh Nguyen; Le-Hai Cao. 2020. "Optimization of the Burnishing Process for Energy Responses and Surface Properties." International Journal of Precision Engineering and Manufacturing 21, no. 6: 1143-1152.
The burnishing process is an efficient finishing operation which is widely used to enhance the surface properties of the machined components. The published works mainly focused on the parameters optimization of the burnishing process in which objective functions are relative to burnished surface qualities. Because of natural resource exhaustion and the rising energy prices, the reduction in energy consumption is an urgent demand in the manufacturing industry. This paper presented an efficient optimization to simultaneously decrease energy consumption as well as the mean roughness depth and improve the Brinell hardness for the burnished surface of H13 steel. The burnishing speed, feed rate, depth of penetration, and the number of rollers were the input parameters. The burnishing processes were carried out on a CNC milling machine. The mathematical relations between inputs and outputs were developed using the radius basis function models. The multi-objective particle swarm optimization and the technique for order of preference by similarity to ideal solution were used to generate the Pareto fronts and to determine the best solution. The results show that energy consumption and surface roughness are reduced by 39.50% and 7.83%, respectively. The Brinell hardness is improved by 29.61% compared to the initial values. The radial basis function models can be effectively used to render the approximations and to predict the response's values. The proposed method can be considered as a sufficient approach for modeling and optimizing the burnishing process.
Trung-Thanh Nguyen; Le-Hai Cao; Truong-An Nguyen; Xuan-Phuong Dang. Multi-response optimization of the roller burnishing process in terms of energy consumption and product quality. Journal of Cleaner Production 2019, 245, 119328 .
AMA StyleTrung-Thanh Nguyen, Le-Hai Cao, Truong-An Nguyen, Xuan-Phuong Dang. Multi-response optimization of the roller burnishing process in terms of energy consumption and product quality. Journal of Cleaner Production. 2019; 245 ():119328.
Chicago/Turabian StyleTrung-Thanh Nguyen; Le-Hai Cao; Truong-An Nguyen; Xuan-Phuong Dang. 2019. "Multi-response optimization of the roller burnishing process in terms of energy consumption and product quality." Journal of Cleaner Production 245, no. : 119328.
Dry machining represents an eco-friendly method that reduces the environmental impacts, saves energy costs, and protects operator health. This article presents a multi-response optimization which aims to enhance the power factor and decrease the energy consumption as well as the surface roughness for the dry machining of a stainless steel 304. The cutting speed ( V), depth of cut ( a), feed rate ( f), and nose radius ( r) were the processing conditions. The outputs of the optimization are the power factor, energy consumption, and surface roughness. The relationships between inputs and outputs were established using the radial basis function models. The experimental data were normalized, with the use of the Grey relational analysis. The principal component analysis is applied to calculate the weight values of technical responses. The desirability approach is used to observe the optimal values. The results showed that the technical outputs are primarily influenced by the feed rate and cutting speed. The reductions of energy consumption and surface roughness are approximately 34.85% and 57.65%, respectively, and the power factor improves around 28.83%, compared to the initial process parameter settings. The outcomes and findings of the investigated work can be used for further research in sustainable design and manufacturing as well as directly used in the knowledge-based and expert systems for dry milling applications in industrial practices.
Trung-Thanh Nguyen; Mozammel Mia; Xuan-Phuong Dang; Chi-Hieu Le; Michael S Packianather. Green machining for the dry milling process of stainless steel 304. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 2019, 234, 881 -899.
AMA StyleTrung-Thanh Nguyen, Mozammel Mia, Xuan-Phuong Dang, Chi-Hieu Le, Michael S Packianather. Green machining for the dry milling process of stainless steel 304. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. 2019; 234 (5):881-899.
Chicago/Turabian StyleTrung-Thanh Nguyen; Mozammel Mia; Xuan-Phuong Dang; Chi-Hieu Le; Michael S Packianather. 2019. "Green machining for the dry milling process of stainless steel 304." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 234, no. 5: 881-899.
The burnishing process is an impressive solution in order to improve the surface integrity. However, energy-efficient optimization of the burnishing process is rarely considered due to the high efforts required. This paper presented an input factor-based optimization to simultaneously enhance the power factor (PFB), the improvement of the Brinell hardness (KBH), and the reduction of the average roughness (KRa), while energy consumption (ECB) aims to decrease for the burnishing process of SKD61 steel. The burnishing speed (V), the feed (f), and the depth of penetration (d) were considered as the processing factors. The trials were conducted using the matrix generated by Taguchi. The principal component analysis (PCA) was applied to calculate the weight values of responses. The optimal parameters were determined using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The results showed that the optimal values of the V, f, and d are 700 RPM, 500 mm/min, and 0.13 mm, respectively. The technical outputs are primarily influenced by the feed rate and depth of penetration. The reductions of energy consumption and surface roughness are approximately 49.48% and 13.79%, while the power factor and Brinell hardness improve around 21.80% and 56.02%, respectively, as compared to the worst case.
Trung-Thanh Nguyen; Le-Hai Cao; Xuan-Phuong Dang; Truong-An Nguyen; Quang-Hung Trinh. Multi-objective optimization of the flat burnishing process for energy efficiency and surface characteristics. Materials and Manufacturing Processes 2019, 34, 1888 -1901.
AMA StyleTrung-Thanh Nguyen, Le-Hai Cao, Xuan-Phuong Dang, Truong-An Nguyen, Quang-Hung Trinh. Multi-objective optimization of the flat burnishing process for energy efficiency and surface characteristics. Materials and Manufacturing Processes. 2019; 34 (16):1888-1901.
Chicago/Turabian StyleTrung-Thanh Nguyen; Le-Hai Cao; Xuan-Phuong Dang; Truong-An Nguyen; Quang-Hung Trinh. 2019. "Multi-objective optimization of the flat burnishing process for energy efficiency and surface characteristics." Materials and Manufacturing Processes 34, no. 16: 1888-1901.
Improving milling performances is an effective solution to decrease the costs required. This paper addressed a multi-response optimization to simultaneously decrease the machining power consumed Pm, arithmetical roughness Ra, and...
Trung Thanh Nguyen; Quoc-Hoang Pham; Xuan-Phuong Dang; Tat-Khoa Doan; Xuan-Hung Le. Optimization Parameters of Milling Process of Mould Material for Decreasing Machining Power and Surface Roughness Criteria. Tehnicki vjesnik - Technical Gazette 2019, 26, 1297 -1304.
AMA StyleTrung Thanh Nguyen, Quoc-Hoang Pham, Xuan-Phuong Dang, Tat-Khoa Doan, Xuan-Hung Le. Optimization Parameters of Milling Process of Mould Material for Decreasing Machining Power and Surface Roughness Criteria. Tehnicki vjesnik - Technical Gazette. 2019; 26 (5):1297-1304.
Chicago/Turabian StyleTrung Thanh Nguyen; Quoc-Hoang Pham; Xuan-Phuong Dang; Tat-Khoa Doan; Xuan-Hung Le. 2019. "Optimization Parameters of Milling Process of Mould Material for Decreasing Machining Power and Surface Roughness Criteria." Tehnicki vjesnik - Technical Gazette 26, no. 5: 1297-1304.
Improving the technical performance of the wire electro-discharge machining (WEDM) process is an effective solution to decrease manufacturing costs. This paper addresses a multi-response optimization to simultaneously improve the cutting area rate CAR and decrease the kerf width AKW, while the average surface roughness ASR is predefined as a constraint. The processing conditions considered include the pulse-on time Ton, the current I, the voltage V and the wire speed S. A WEDM machine was adopted in conjunction with the Box–Behnken matrix to conduct experimental trials for machining of SKD61 steel. Highly nonlinear relationships between machining parameters and technological outputs were developed using the Kriging models. Finally, an archive-based micro-genetic algorithm (AMGA) was used to resolve the trade-off analysis among three responses and determine the optimal values of the processing factors. The results showed that a set of feasible solutions can be determined for the low kerf width as well as the surface roughness and the high cutting area rate. The selection of optimum parameters could help the WEDM operators to save the machining costs and time. The combination of the Kriging model and AMGA could be considered as an intelligent approach for modelling WEDM processes and predicting optimal results.
Trung-Thanh Nguyen; Quoc-Dung Duong. Optimization of WEDM process of mould material using Kriging model to improve technological performances. Sādhanā 2019, 44, 1 -16.
AMA StyleTrung-Thanh Nguyen, Quoc-Dung Duong. Optimization of WEDM process of mould material using Kriging model to improve technological performances. Sādhanā. 2019; 44 (6):1-16.
Chicago/Turabian StyleTrung-Thanh Nguyen; Quoc-Dung Duong. 2019. "Optimization of WEDM process of mould material using Kriging model to improve technological performances." Sādhanā 44, no. 6: 1-16.
The objective of this work is to investigate the influences of three machining factors (burnishing speed V, feed rate f, and depth of penetration a) on the improved rate of arithmetic average roughness Δ Ra, improved rate of maximum height roughness Δ Ry, and improved rates of surface hardness Δ SH. The internal roller burnishing experiments were conducted with the aid of the computer numerical control machining center and Box–Behnken experimental design. The Kriging models were used to render the highly nonlinear relationships between inputs and outputs. An integrative approach combining a Non-dominated Sorting Genetic Algorithm II and Technique for Order Preference by Similarity to Ideal Solution was adopted to generate a set of feasible optimal solutions and determine the best machining conditions. The scanning electron microscopy images were depicted to investigate the surface morphology at the different conditions. The X-ray diffraction was applied to measure the compressive stresses at the external surface. The results showed that the predicted values of the objectives have good agreement with the experimental ones. High surface quality is characterized by an improved average roughness of 95.80%, an enhancement in the maximum roughness of 91.98%, and an improvement in surface hardness of 45.44%, compared to pre-machined surfaces. The selection of optimum process parameters could help the burnishing operators to save the machining costs and time. The combination of Kriging model, Non-dominated Sorting Genetic Algorithm II, and Technique for Order Preference by Similarity to Ideal Solution is considered as an intelligent approach for modeling and optimization of burnishing processes.
Trung-Thanh Nguyen; Xuan-Ba Le. Optimization of roller burnishing process using Kriging model to improve surface properties. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 2019, 233, 2264 -2282.
AMA StyleTrung-Thanh Nguyen, Xuan-Ba Le. Optimization of roller burnishing process using Kriging model to improve surface properties. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. 2019; 233 (12):2264-2282.
Chicago/Turabian StyleTrung-Thanh Nguyen; Xuan-Ba Le. 2019. "Optimization of roller burnishing process using Kriging model to improve surface properties." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 233, no. 12: 2264-2282.