Feature-based carbon emission quantitation strategy for the part machining process

Zhou, Guanghui; Zhou, Ce; Lu, Qi; Tian, Changle; Xiao, Zhongdong

doi:10.1080/0951192x.2017.1328561

Cited by 21 publications

(7 citation statements)

References 28 publications

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“…There are many parameters to evaluate resource allocation schemes, such as processing time, processing cost, processing quality, carbon emissions, and loading rate (Zhang et al ., 2017 a ; Zhang and Li, 2018; Zhou et al ., 2018 a , 2018 b ). Taking into account the characteristics of cloud manufacturing, processing time T, processing cost C , qualified rate Q , and resource utilization rate H are selected as the optimized parameters.…”

Section: Methodsmentioning

confidence: 99%

Autonomous resource allocation of smart workshop for cloud machining orders

Jing

Lei

Ding

et al. 2020

AIEDAM

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In order to realize the online allocation of collaborative processing resource of smart workshop in the context of cloud manufacturing, a multi-objective optimization model of workshop collaborative resources (MOM-WCR) was proposed. Considering the optimization objectives of processing time, processing cost, product qualification rate, and resource utilization, MOM-WCR was constructed. Based on the time sequence of workshop processing tasks, the workshop collaborative manufacturing resource was integrated in MOM-WCR. Fuzzy analytic hierarchy process (FAHP) was adopted to simplified the multi-objective problem into the single-objective problem. Then, the improved firefly algorithm which integrated the particle swarm algorithm (IFA-PSA) was used to solve MOM-WCR. Finally, a group of connecting rod processing experiments were used to verify the model proposed in this paper. The results show that the model is feasible in the application of workshop-level resource allocation in the context of cloud manufacturing, and the improved firefly algorithm shows good performance in solving the multi-objective resource allocation problem.

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Section: Methodsmentioning

confidence: 99%

Autonomous resource allocation of smart workshop for cloud machining orders

Jing

Lei

Ding

et al. 2020

AIEDAM

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“…In particular, this study discussed the breakdown of the processes that contribute to the overall carbon emissions of a CNC-based machining system, such as electricity, cutting fluid, wear and tear of cutting tools, material consumption and disposal of chips, etc. In addition, Zhou et al proposed a carbon emission quantitation strategy to quantify the overall carbon emissions of a part machining process [21].…”

Section: Literature Review a Manufacturing Carbon Emission Evalumentioning

confidence: 99%

Big Data Analysis Approach for Real-Time Carbon Efficiency Evaluation of Discrete Manufacturing Workshops

Zhang

2019

IEEE Access

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Due to the huge consumption of materials and energy during machining processes, reduction of manufacturing carbon emission is an essential key to decrease the environmental burden of various manufacturing systems. To achieve this target, one critical step is to calculate and evaluate the carbon emissions of machining processes. However, this step is a little difficult for discrete manufacturing processes, because they are always complex and the data sources are diverse. Considering the complexity of discrete manufacturing workshops, a Big Data analysis approach for real-time carbon efficiency evaluation of discrete manufacturing workshops is proposed in an internet of things-enabled ubiquitous environment. Firstly, the deployment of data acquisition devices is introduced to create a ubiquitous manufacturing workshop, and data modeling of production state and carbon emission is described to realize data acquisition and storage. Then, a data-driven multi-level carbon efficiency evaluation of manufacturing workshop is established based on Big Data analysis approaches. Finally, an auto parts manufacturing workshop is studied to verify the feasibility and applicability of the proposed methods. This method realizes the combination of manufacturing Big Data and low-carbon production. Meanwhile, the evaluation method can be used in other production information systems and then assist the production decision-making. INDEX TERMS Big data analysis, data acquisition network, carbon emission, carbon efficiency evaluation, discrete manufacturing workshops.

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“…Liu et al (2016) developed a numerical model to optimize cutting parameters to minimize the carbon emission cost considering carbon tax policies as well as production time. Zhou et al (2017) developed a model to determine carbon emissions in a machining process generated from raw materials, energy consumption, and waste. Widhiarso and Rosyidi (2018) developed a multi-objective optimization model to minimize production costs and environmental impact.…”

Section: Introductionmentioning

confidence: 99%

“…(2016) Multi-pass turning ✓ ✗ ✓ ✓ ✓ ✗ ✗ ✓ ✗ ✗ ✓ Liu et al. (2016) Single-pass turning ✓ ✗ ✓ ✗ ✗ ✗ ✓ ✗ ✗ ✓ ✗ Zhou et al. (2017) Turning, milling & drilling ✓ ✗ ✓ ✗ ✗ ✗ ✓ ✗ ✗ ✗ ✗ Widhiarsio and Rosyidi (2018) Single-pass turning ✓ ✗ ✓ ✗ ✗ ✗ ✓ ✗ ✓ ✗ ✗ Tian et al.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective cutting parameter optimization model of multi-pass turning in CNC machines for sustainable manufacturing

Pangestu

Pujiyanto

Rosyidi

2021

Heliyon

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Sustainable manufacturing has grown widely owing to recent environmental issues. This study aims to develop a multi-objective multi-pass turning optimization model to determine the optimal cutting parameters, including spindle rotation speed, feed rate, depth of cut, and number of roughing passes. The optimization model considers several criteria in the key metrics of sustainable manufacturing, i.e., energy consumption, carbon emissions, production time, and production cost. A numerical example is provided to show the application of the model, including sensitivity analysis, to study the effects of several cutting parameters on the objective functions. The model can be used by manufacturing industries to improve their manufacturing process efficiency and simultaneously produce products that support sustainable manufacturing.

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Feature-based carbon emission quantitation strategy for the part machining process

Cited by 21 publications

References 28 publications

Autonomous resource allocation of smart workshop for cloud machining orders

Autonomous resource allocation of smart workshop for cloud machining orders

Big Data Analysis Approach for Real-Time Carbon Efficiency Evaluation of Discrete Manufacturing Workshops

Multi-objective cutting parameter optimization model of multi-pass turning in CNC machines for sustainable manufacturing

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