Tool tip cutting specific energy prediction model and the influence of machining parameters and tool wear in milling

Zhao, Guoyong; Su, Yu; Zheng, Guangming; Zhao, Yugang; Li, Chunxiao

doi:10.1177/0954405420911298

Cited by 12 publications

(8 citation statements)

References 21 publications

(37 reference statements)

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“…where: k and e-the number of parameters considered and approximate error; b i , b ii , and b ij -the regression coefficients. The total specific energy (SEC) is defined as the ratio of the total energy consumption to the material removal volume and computed as 27,28 :…”

Section: Optimization Targetsmentioning

confidence: 99%

Multi-response optimization of the actively driven rotary turning for energy efficiency, carbon emissions, and machining quality

Nguyen

Duong

Mia

2021

Proceedings of the Institution of Mechanical Engineers, Part B:

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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.

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Section: Optimization Targetsmentioning

confidence: 99%

Multi-response optimization of the actively driven rotary turning for energy efficiency, carbon emissions, and machining quality

Nguyen

Duong

Mia

2021

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…The results of this experiment showed that the specific cutting energy ranged from 4.1 to 8.6 W s/mm 3 , which is comparable with earlier studies shown in Table 5. [11][12][13][14][15][16] Recent work by Guo et al 17 was not focused on similar workpiece material class. This increasing body of evidence at tool wear driving increase in specific energy needs to be considering in modelling so that machining optimisation is more realistic and does not assume sharp tools.…”

Section: Effect Of Tool Wear On Cutting Powermentioning

confidence: 99%

Extended model for selection of optimum turning conditions based on minimum energy considerations

Alswat

Mativenga

2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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Reducing energy consumption in manufacturing is essential for using energy effectively and minimising carbon footprint. In this study, the equation for selecting optimum cutting conditions to minimise energy footprint was extended by considering the fact that as tool wear increases, power and specific energy also increase. This new model enables selecting optimum conditions for energy-smart machining by considering energy footprint, cutting tool utilisation and the volume of material to be removed. This timely research improves the integrity of energy models in machining and their suitability and impact in practical machining conditions.

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“…Though these models can accurately predict energy consumption, they fail to consider the influence of factors other than cutting parameters on energy consumption, such as tool parameters or tool wear. Based on this consideration, Zhao et al [8] analyzed the effect of tool wear on machine tool energy consumption in 45# steel semi-finishing milling, and developed a machine power model with MRR and tool wear as the inputs. The study showed that cutting energy consumption can be reduced by monitoring and controlling tool wear.…”

Section: Introductionmentioning

confidence: 99%

Prediction models for energy consumption and surface quality in stainless steel milling

Zhao

et al. 2021

Int J Adv Manuf Technol

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Stainless steel is a kind of difficult-to-machine material, and the work hardening in milling easily leads to high energy consumption and poor surface quality. Thus, the influence of machined surface hardness on energy consumption and surface quality cannot be ignored. To solve this problem, the prediction models for machine tool specific energy consumption and surface roughness are developed with tool wear and machined surface hardness considered firstly. Then, the validity of the models is verified through AISI 304 stainless steel milling experiments. The results show that the prediction accuracy of the machine tool specific energy consumption model can reach 98.7%, and the roughness model can reach 96.8%. Later, according to the developed prediction models, the influence of milling parameters, surface hardness, and tool wear on the machine specific energy consumption and surface roughness is studied. Results show that in stainless steel milling, the most significant parameters for surface roughness is the machined surface hardness, while that for energy consumption is the feed per tooth. The machine specific energy consumption increases linearly with the increase of the tool wear and the machined surface hardness gradually. The proposed models are helpful to optimize the process parameters for high efficiency and high quality machining of stainless steel.

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Tool tip cutting specific energy prediction model and the influence of machining parameters and tool wear in milling

Cited by 12 publications

References 21 publications

Multi-response optimization of the actively driven rotary turning for energy efficiency, carbon emissions, and machining quality

Multi-response optimization of the actively driven rotary turning for energy efficiency, carbon emissions, and machining quality

Extended model for selection of optimum turning conditions based on minimum energy considerations

Prediction models for energy consumption and surface quality in stainless steel milling

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