Optimization of cutting parameters for energy saving

Li, Jianguang; Lü, Yong; Zhao, Hang; Li, Peng; Yao, Yingxue

doi:10.1007/s00170-013-5227-z

Cited by 51 publications

(26 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The values of C F c , x F c , y F c , n F c , and K F c can be obtained by experimental research. A similar model has been applied in the processes of turning [46], grinding [47], milling [48,49], etc. The data in Table 11 are used to fit the values of the four coefficients by a statistical regression and result in the model of P=39.25v 0.928 f 0.655 a p 0.902 with R 2 = 0.988.…”

Section: Evaluation On Calculation Models Of E Spindle Mmentioning

confidence: 99%

Evaluation on models of calculating energy consumption in metal cutting processes: a case of external turning process

Zhong

Tang

Lv³

et al. 2015

Int J Adv Manuf Technol

View full text Add to dashboard Cite

The manufacturing industry has been focusing on calculating energy consumption because of increased sustainability and environmental consciousness, resulting in a large number of calculation models. However, evaluation on these existing models is missing, so that practitioners often have trouble selecting the right energy models. To identify the most appropriate, cost-effective, and easy-to-implement energy models, three tasks were conducted in this paper. First, various experimental or empirical energy consumption models of spindle acceleration, spindle rotation, feed and material removal, and those of machine tool during material removal processes are reviewed. Then, five evaluation criteria, applicability, accuracy, computational efforts, complexity of fitting coefficients, and ease of data collection, are proposed. These criteria are identified based on the general steps to run the calculation models. Finally, the evaluation criteria are applied with the supporting experimental data of external turning to rank the models. This work is expected to assist practitioners in selecting models to calculate energy consumption in metal cutting processes for a particular situation and purpose.

show abstract

Section: Evaluation On Calculation Models Of E Spindle Mmentioning

confidence: 99%

Evaluation on models of calculating energy consumption in metal cutting processes: a case of external turning process

Zhong

Tang

Lv³

et al. 2015

Int J Adv Manuf Technol

View full text Add to dashboard Cite

show abstract

“…Unfortunately, energy balance in hard machining resulting from the specific action of the cutting edge has not been investigated in a satisfactory manner, but it seems to be an important objective of hard machining research. Moreover, more work is needed to optimize energy usage in metal cutting besides economic objectives because it is a process with large energy consumption and low energy efficiency [6,7]. In case of hard machining, the energy consumption increases distinctly due to extreme high hardness of the material machined and high negative rake angle of the CBN cutting tool used.…”

Section: Introductionmentioning

confidence: 99%

Energy consumption characterization in precision hard machining using CBN cutting tools

Grzesik

Denkena

Żak

et al. 2015

Int J Adv Manuf Technol

View full text Add to dashboard Cite

In this paper, the contribution of tool wear to the energy balance was determined for precision hard turning using chamfered CBN cutting tools. The tool nose wear VB C and the corresponding changes of component forces F c , F f and F p resulting from tool wear evolution were continuously measured during wear tests. Based on the cutting mechanics, specific cutting and ploughing energies were calculated for a number of tool wear states. In particular, changes of energy balance due to tool wear under variable feed rate, depth of cut and tool nose radius were discussed. A distinction between material removal conditions resulting from precision cutting and grinding at a very low uncut chip thickness is considered.

show abstract

“…However, the whole approach is designed exclusively for CNC micromilling process, and it cannot be applied for other processes. Similarly, Li et al (2014) used GA to optimise the cutting parameters in order to reduce energy consumption and improve production rate in machining process. In order to perform process simulation and optimisation of laser tube bending, the finite element (FE) simulation was integrated with the genetic algorithm (Guan et al 2013).…”

Section: Multiresponse Optimisation Based On Genetic Algorithmmentioning

confidence: 99%

Review of Multiresponse Process Optimisation Methods

Šibalija

Majstorovic

2015

Advanced Multiresponse Process Optimisation

View full text Add to dashboard Cite

The review of methods used for multiresponse process parameter design and similar multiobjective optimisation problems is discussed in this chapter, implying the following classification: (1) conventional methods based on statistical or mathematical techniques: (i) experimental design techniques (response surface methodology, Taguchi's robust parameter design and related approaches), and (ii) iterative mathematical search techniques; (2) non-conventional methods based on artificial intelligence techniques: (i) fuzzy logic, (ii) artificial neural networks, (iii) metaheuristic search techniques (genetic algorithm, simulated annealing, particle swarm optimisation, ant colony optimisation, tabu search, and recent evolutionary algorithms such as artificial bee colony algorithm, biogeography-based optimisation, and teaching-learning-based optimisation), and (iv) expert systems.

show abstract

Optimization of cutting parameters for energy saving

Cited by 51 publications

References 7 publications

Evaluation on models of calculating energy consumption in metal cutting processes: a case of external turning process

Evaluation on models of calculating energy consumption in metal cutting processes: a case of external turning process

Energy consumption characterization in precision hard machining using CBN cutting tools

Review of Multiresponse Process Optimisation Methods

Contact Info

Product

Resources

About