Cutting Parameter Optimization for Reducing Carbon Emissions Using Digital Twin

Zhao, Lili; Fang, Yilin; Lou, Ping; Yan, Junwei; Xiao, Angran

doi:10.1007/s12541-021-00486-1

Cited by 43 publications

(20 citation statements)

References 41 publications

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“…On the other hand, calculation and visualization in a magnitude of 1 s are achievable but require great computing effort [16]. The reduction of this latency towards the IPO cycle of the NC is the subject of current research [17] and will certainly decrease. The pure consideration of physical effects in the context of specific models (Figure 2), on the other hand, does not inevitably correlate with the machining result because singular effects are frequently super positioning.…”

Section: Specific Calculation and Process Modelsmentioning

confidence: 99%

“…On the other hand, calculation and visualization in a magnitude of 1 s are achievable but require great computing effort [16]. The reduction of this latency towards the IPO cycle of the NC is the subject of current research [17] and will certainly decrease. The models implemented here, however, aim to describe physical effects in a causeeffect relationship such as the calculation of the cutting force (cf.…”

Section: Specific Calculation and Process Modelsmentioning

confidence: 99%

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Digital Twins for High-Tech Machining Applications—A Model-Based Analytics-Ready Approach

Hänel

Seidel

Frieß

et al. 2021

JMMP

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This paper presents a brief introduction to competition-driven digital transformation in the machining sector. On this basis, the creation of a digital twin for machining processes is approached firstly using a basic digital twin structure. The latter is sub-grouped into information and data models, specific calculation and process models, all seen from an application-oriented perspective. Moreover, digital shadow and digital twin are embedded in this framework, being discussed in the context of a state-of-the-art literature review. The main part of this paper addresses models for machine and path inaccuracies, material removal and tool engagement, cutting force, process stability, thermal behavior, workpiece and surface properties. Furthermore, these models are superimposed towards an integral digital twin. In addition, the overall context is expanded towards an integral software architecture of a digital twin providing information system. The information system, in turn, ties in with existing forward-oriented planning from operational practice, leading to a significant expansion of the initially presented basic structure for a digital twin. Consequently, a time-stratified data layer platform is introduced to prepare for the resulting shadow-twin transformation loop. Finally, subtasks are defined to assure functional interfaces, model integrability and feedback measures.

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Section: Specific Calculation and Process Modelsmentioning

confidence: 99%

“…On the other hand, calculation and visualization in a magnitude of 1 s are achievable but require great computing effort [16]. The reduction of this latency towards the IPO cycle of the NC is the subject of current research [17] and will certainly decrease. The models implemented here, however, aim to describe physical effects in a causeeffect relationship such as the calculation of the cutting force (cf.…”

Section: Specific Calculation and Process Modelsmentioning

confidence: 99%

Digital Twins for High-Tech Machining Applications—A Model-Based Analytics-Ready Approach

Hänel

Seidel

Frieß

et al. 2021

JMMP

View full text Add to dashboard Cite

show abstract

“…The orthogonal experiment factors and levels of the energy consumption characteristics of G00 and commands are shown in Tables 3 and 4, respectively. Therefore, the mixed horizontal orthogonal experiment schemes are L 49 7 × 5 3 and L 64 7 × 3 × 4 × 5 6 . The experimental material is aluminum alloy.…”

Section: Feed Ratementioning

confidence: 99%

“…According to the calculation of empirical formula (12), the number of hidden layer neurons of the neural network corresponding to the G00 instruction is [3,13], and G01 is [4,14]. Randomly take 80% of each data set (39 G00 data, 51 G01 data) to determine the optimal neural network structure through cross-validation.…”

Section: Ipbpnn Training and Testingmentioning

confidence: 99%

“…As the basic energy-consuming equipment of the manufacturing industry, CNC machine tools have the characteristics of large quantity and wide range, large total energy consumption, and low efficiency, etc., and have great energy-saving potential. Therefore, fast and accurate prediction of its machining energy consumption is an effective way to achieve optimal management of energy consumption and achieve carbon peak and carbon neutrality [3]. However, CNC machine tools are a complex multi-source energy consumption system.…”

Section: Introductionmentioning

confidence: 99%

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A Novel CNC Milling Energy Consumption Prediction Method Based on Program Parsing and Parallel Neural Network

et al. 2021

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Accurate and rapid prediction of the energy consumption of CNC machining is an effective means to realize the lean management of CNC machine tools energy consumption as well as to achieve the sustainable development of the manufacturing industry. Aiming at the drawbacks of existing CNC milling energy consumption prediction methods in terms of efficiency and precision, a novel milling energy consumption prediction method based on program parsing and parallel neural network is proposed. Firstly, the relationship between CNC program and energy consumption of CNC machine tool is analyzed. Based on the structural characteristics of the CNC program, an automatic parsing algorithm for the CNC program is proposed. Moreover, based on the improved parallel neural network, the mapping relationship between the energy consumption parameters of each CNC instruction and the milling energy consumption is constructed. Finally, the proposed method is compared with the literature to verify the superiority of the proposed method in terms of prediction efficiency and accuracy, and the practicability of the method is verified through the case study. The proposed method lays the foundation for efficient and low-consumption process planning and energy efficiency improvement of machine tools and is conducive to the sustainable development of the environment.

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Evolution of Digital Twin in Manufacturing Application: Definition, Architecture, Applications, and Tools

Iyer,

Sangwan,

Dhiraj

2024

Springer Series in Advanced Manufacturing

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Cutting Parameter Optimization for Reducing Carbon Emissions Using Digital Twin

Cited by 43 publications

References 41 publications

Digital Twins for High-Tech Machining Applications—A Model-Based Analytics-Ready Approach

Digital Twins for High-Tech Machining Applications—A Model-Based Analytics-Ready Approach

A Novel CNC Milling Energy Consumption Prediction Method Based on Program Parsing and Parallel Neural Network

Evolution of Digital Twin in Manufacturing Application: Definition, Architecture, Applications, and Tools

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