A quantitative approach to analyze carbon emissions of CNC-based machining systems

Li, Congbo; Tang, Ying; Cui, Longguo; Li, Pengyu

doi:10.1007/s10845-013-0812-4

Cited by 145 publications

(63 citation statements)

References 27 publications

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“…the set of combinations of job i, n the set of jobs, M ij the set of available machines for O ij , R ih the set that contains the operations belonging to the hth combination of job i, T ij the set of available cutting tools of operation O ij , TAD ij the set of TADs of operation O ij , LO ih the number of operations of of the hth combination of job i, e.g., LO ih = |R ih |, PW k the power of machine k, t ijk the processing time of operation O ij on machine k, mc ijk the machine cost of operation O ij processed on machine k, mc ij f the cost of the f th tool to process operation O ij , MCCI the machine change cost index, TCCI the tool change cost index, SCCI the set-up change cost index, C EF,ele the electricity carbon emission factor; it takes the value 0.665 (kgCO 2 /kwh) according to the average of grids' emission factors in China [28], T c k the replacement cycle of cutting fluid; its value usually falls in the range [1,3] months [29], In this paper, this value is set to two months, C oil EF the carbon emission factor for the production of cutting fluid (kgCO 2 /L); according to [30], it takes the value 2.85 kgCO 2 …”

Section: Sets and Parametersmentioning

confidence: 99%

“…A manufacturing system often involves more than one machines, and machine tools are the particular source of carbon emissions since the amount of electricity that powers the machines is generated through power plants. Relative research [28,30] has already shown that carbon emission is induced by a wide range of activities. To reflect the nature of carbon emission in machining process, quantitative measures of carbon emissions are considered in the second objective.…”

Section: Objectivesmentioning

confidence: 99%

“…The carbon emission factor of cutting tools, C t EF , takes the value 104.6 kgCO 2 /kg. Finally, according to [28,32], the average mass of a tool cutter is 9.5 g.…”

Section: Objectivesmentioning

confidence: 99%

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Mathematical Modeling and a Hybrid NSGA-II Algorithm for Process Planning Problem Considering Machining Cost and Carbon Emission

2017

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Abstract:Process planning is an important function in a manufacturing system; it specifies the manufacturing requirements and details for the shop floor to convert a part from raw material to the finished form. However, considering only economical criterion with technological constraints is not enough in sustainable manufacturing practice; formerly, criteria about low carbon emission awareness have seldom been taken into account in process planning optimization. In this paper, a mathematical model that considers both machining costs reduction as well as carbon emission reduction is established for the process planning problem. However, due to various flexibilities together with complex precedence constraints between operations, the process planning problem is a non-deterministic polynomial-time (NP) hard problem. Aiming at the distinctive feature of the multi-objectives process planning optimization, we then developed a hybrid non-dominated sorting genetic algorithm (NSGA)-II to tackle this problem. A local search method that considers both the total cost criterion and the carbon emission criterion are introduced into the proposed algorithm to avoid being trapped into local optima. Moreover, the technique for order preference by similarity to an ideal solution (TOPSIS) method is also adopted to determine the best solution from the Pareto front. Experiments have been conducted using Kim's benchmark. Computational results show that process plan schemes with low carbon emission can be captured, and, more importantly, the proposed hybrid NSGA-II algorithm can obtain more promising optimal Pareto front than the plain NSGA-II algorithm. Meanwhile, according to the computational results of Kim's benchmark, we find that both of the total machining cost and carbon emission are roughly proportional to the number of operations, and a process plan with less operation may be more satisfactory. This study will draw references for the further research on green manufacturing in the process planning level.

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Section: Sets and Parametersmentioning

confidence: 99%

Section: Objectivesmentioning

confidence: 99%

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Mathematical Modeling and a Hybrid NSGA-II Algorithm for Process Planning Problem Considering Machining Cost and Carbon Emission

2017

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“…where b m k is the additional load-loss coefficient, and its value can be adjusted between 0.10 and 0.20 according to the state of lathe k [43]. In the cutting process, the linear velocity of v c ikl can be calculated by Equation (8) [37].…”

Section: Analysis Of Energy Consumptionmentioning

confidence: 99%

Estimation of Distribution Algorithm for Energy-Efficient Scheduling in Turning Processes

et al. 2016

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Abstract:With the increasing concern for the environment, energy-efficient scheduling of the manufacturing industry is becoming urgent and popular. In turning processes, both spindle speed and processing time affect the final energy consumption and thus the spindle speed and scheduling scheme need to be optimized simultaneously. Since the turning workshop can be regarded as the flexible flow shop, this paper formulates a mixed integer nonlinear programming model for the energy-efficient scheduling of the flexible flow shop. Accordingly, a new decoding method is developed for the optimization of both spindle speed and scheduling scheme simultaneously, and an estimation of the distribution algorithm adopting the new decoding method is proposed to solve large-size problems. The parameters of this algorithm are determined by statistics from a simplified practical case. Validation results of the proposed method show that the makespan is shortened to a large extent, and the consumed energy is significantly saved. These results demonstrate the effectiveness of the proposed mathematical model and algorithm.

show abstract

“…where k m b is the additional load-loss coefficient, and its value can be adjusted between 0.10 and 0.20 according to the state of lathe k [43]. In the cutting process, the linear velocity of c ikl v can be calculated by Equation (8) [37].…”

Section: Klmentioning

confidence: 99%

Estimation of Distribution Algorithm for Energy-Efficient Scheduling in Turning Processes

Wang

Rao

Zhang

et al. 2016

Preprint

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With the increasing concern for the environment, energy-efficient scheduling of the manufacturing industry is becoming urgent and popular. In turning processes, both spindle speed and processing time affect the final energy consumption and thus the spindle speed and scheduling scheme need to be optimized simultaneously. Since the turning workshop can be regarded as the flexible flow shop, this paper formulates a mixed integer nonlinear programming model for the energy-efficient scheduling of the flexible flow shop. Accordingly, a new decoding method is developed for the optimization of both spindle speed and scheduling scheme simultaneously, and an estimation of the distribution algorithm adopting the new decoding method is proposed to solve large-size problems. The parameters of this algorithm are determined by statistics from a simplified practical case. Validation results of the proposed method show that the makespan is shortened to a large extent, and the consumed energy is significantly saved. These results demonstrate the effectiveness of the proposed mathematical model and algorithm.

show abstract

A quantitative approach to analyze carbon emissions of CNC-based machining systems

Cited by 145 publications

References 27 publications

Mathematical Modeling and a Hybrid NSGA-II Algorithm for Process Planning Problem Considering Machining Cost and Carbon Emission

Mathematical Modeling and a Hybrid NSGA-II Algorithm for Process Planning Problem Considering Machining Cost and Carbon Emission

Estimation of Distribution Algorithm for Energy-Efficient Scheduling in Turning Processes

Estimation of Distribution Algorithm for Energy-Efficient Scheduling in Turning Processes

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