An investigation into reducing the spindle acceleration energy consumption of machine tools

Lv, Jingxiang; Tang, Renzhong; Tang, Wangchujun; Liu, Ying; Zhang, Yingfeng; Jia, Shi

doi:10.1016/j.jclepro.2016.12.045

Cited by 72 publications

(25 citation statements)

References 23 publications

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“…This can be expressed as a piecewise function as Equation 4, where C rA1 , C rA2 , C rB1 , C rB2 , C rC1 , C rC2 are the coefficients of the three linear functions. n BA M and n l M are the turning points of this function, which has three sections according to [31]. The values of these coefficients can be obtained by power measurement and linear regression of the obtained data.…”

Section: Modeling Of Energy Consumption During Machining Processmentioning

confidence: 99%

“…Equations (25)- (30) are all subject to constrains expressed by Equation (31), where n tMax , v tMax , f tMax and a ptMax are the maximum values of the parameters the turning machine can bear. d is the total distance machining allowance of the part to be tooled, and it is determined by the design scheme.…”

Section: Determining Rules and Parameters Of Basic Genetic Algorithmmentioning

confidence: 99%

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Analysis of Multi-Objective Optimization of Machining Allowance Distribution and Parameters for Energy Saving Strategy

Hong

Tang

et al. 2020

Sustainability

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Machining allowance distribution and related parameter optimization of machining processes have been well-discussed. However, for energy saving purposes, the optimization priorities of different machining phases should be different. There are often significant incoherencies between the existing research and real applications. This paper presents an improved method to optimize machining allowance distribution and parameters comprehensively, considering energy-saving strategy and other multi-objectives of different phases. The empirical parametric models of different machining phases were established, with the allowance distribution problem properly addressed. Based on previous analysis work of algorithm performance, non-dominated sorting genetic algorithm II and multi-objective evolutionary algorithm based on decomposition were chosen to obtain Pareto solutions. Algorithm performances were compared based on the efficiency of finding the Pareto fronts. Two case studies of a cylindrical turning and a face milling were carried out. Results demonstrate that the proposed method is effective in trading-off and finding precise application scopes of machining allowances and parameters used in real production. Cutting tool life and surface roughness can be greatly improved for turning. Energy consumption of rough milling can be greatly reduced to around 20% of traditional methods. The optimum algorithm of each case is also recognized. The proposed method can be easily extended to other machining scenarios and can be used as guidance of process planning for meeting various engineering demands.

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Section: Modeling Of Energy Consumption During Machining Processmentioning

confidence: 99%

Section: Determining Rules and Parameters Of Basic Genetic Algorithmmentioning

confidence: 99%

Analysis of Multi-Objective Optimization of Machining Allowance Distribution and Parameters for Energy Saving Strategy

Hong

Tang

et al. 2020

Sustainability

Self Cite

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“…d) The maximum acceleration power, which is consisted of power for spindle rotation and the power for overcoming the inertia of mechanical transmission system (Lv et al, 2017), should not exceed the rated power of spindle motor. 15…”

Section: Constraintsmentioning

confidence: 99%

Design optimization of lathe spindle system for optimum energy efficiency

et al. 2020

Journal of Cleaner Production

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Spindle system is the major mechanical component in machine tools, and its performance is responsible for a significant portion of the total consumed energy of machine tools. Conventional design optimization of spindle system is partially focused on parameter optimization of spindle motor or transmission system, contributing to an increase of the motor efficiency. Given that concurrent interactions among them is complex, very little efforts has been done to conduct integration optimization for optimum energy efficiency. To this end, a new approach of spindle system design is presented with consideration of the above two aspects adequately, to achieve the maximum energy and material efficiency. Firstly, the energy characteristic of spindle system is explicitly modeled on the basis of energy flow analysis. Then, a multi-objective optimization model for parameter optimization of spindle motor and transmission system is developed to take the both maximum energy efficiency and minimum volume as objectives, which is subjecting to a set of constraints with related to the cutting parameters boundary, processing requirements and shifting power losses. Finally, a multi-objective improved teaching-learning based optimization (MO-ITLBO) algorithm is presented to solve the developed optimization model. The performance of the proposed design approach of lathe spindle system is demonstrated through different working conditions. The experimental results indicate that the design of energy and material efficient machine tools can be achieved.

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“…SR(N,f,a p ,a e ) = − 8.9 − 5.56 × 10 −4 × (N) + 0.043 × (f ) + 37.67 × (a p ) − 0.06 × (a e ) + 1.0 × 10 −6 × (N 2 ) − 5.8 × 10 −5 × (f 2 ) − 32.44 × (a p 2 ) + 0.015 × (a e 2 ) − 5. 16…”

Section: Development Of Rsm Models and Analysis Of Variancementioning

confidence: 99%

Multi-Objective Optimization of Energy Consumption and Surface Quality in Nanofluid SQCL Assisted Face Milling

et al. 2019

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Considering the significance of improving the energy efficiency, surface quality and material removal quantity of machining processes, the present study is conducted in the form of an experimental investigation and a multi-objective optimization. The experiments were conducted by face milling AISI 1045 steel on a Computer Numerical Controlled (CNC) milling machine using a carbide cutting tool. The Cu-nano-fluid, dispersed in distilled water, was impinged in small quantity cooling lubrication (SQCL) spray applied to the cutting zone. The data of surface roughness and active cutting energy were measured while the material removal rate was calculated. A multi-objective optimization was performed by the integration of the Taguchi method, Grey Relational Analysis (GRA), and the Non-Dominated Sorting Genetic Algorithm (NSGA-II). The optimum results calculated were a cutting speed of 1200 rev/min, a feed rate of 320 mm/min, a depth of cut of 0.5 mm, and a width of cut of 15 mm. It was also endowed with a 20.7% reduction in energy consumption. Furthermore, the use of SQCL promoted sustainable manufacturing. The novelty of the work is in reducing energy consumption under nano fluid assisted machining while paying adequate attention to material removal quantity and the product’s surface quality.

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An investigation into reducing the spindle acceleration energy consumption of machine tools

Cited by 72 publications

References 23 publications

Analysis of Multi-Objective Optimization of Machining Allowance Distribution and Parameters for Energy Saving Strategy

Analysis of Multi-Objective Optimization of Machining Allowance Distribution and Parameters for Energy Saving Strategy

Design optimization of lathe spindle system for optimum energy efficiency

Multi-Objective Optimization of Energy Consumption and Surface Quality in Nanofluid SQCL Assisted Face Milling

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