A life cycle approach to characterizing carbon efficiency of cutting tools

Li, Benjie; Cao, Huajun; Yan, Jun; Jafar, Salman

doi:10.1007/s00170-017-0728-9

Cited by 17 publications

(10 citation statements)

References 26 publications

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“…Therefore, CO 2 -equivalent emission for manufacturing, usage and the recovery phase should be considered separately. In the tracking of carbon footprint, cutting tools also are studied in reference [56] in order to identify the impact of cutting tools of MT.…”

Section: Carbon Emissionmentioning

confidence: 99%

The Analysis of Key Technologies for Sustainable Machine Tools Design

Feng

Shi

2020

Applied Sciences

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Machine tools as indispensable tools for manufacturing products are typical high-energy, high-carbon manufacturing systems due to their larger mass, longer life cycles and huge resources and energy consumption. The current research trend of sustainable machine tools aims to reduce cost, energy consumption and increase sustainability without compromising their functionality, usability, productivity, accuracy, etc. However, there is a lack of systematic reviews about what are the key technologies for sustainable machine tools design. Thus, a comprehensive literature review of machine tool design for sustainability is needed in order to make clear how to design and evaluate sustainable machine tools from the viewpoint of life cycle sustainability analysis. The aim of this paper is to review the sustainable design and assessment aspects of machine tool design from partial goals to integrated aims according to whole sustainability dimensions such as the environment, economy and society, as well as involving key techniques in different life cycle stages. Recent research and study on improving directly or indirectly sustainability performance of machine tools according to focus points could be summarized as: design optimization of components such as lightweight using topology and bionic methods; structure design with modular design and layout; reducing cutting fluids and lubricant oil by employing minimum quantity lubrication, dry and cryogenic machining; reducing energy consumption; waste reduction by reusing, remanufacturing and recycling; sustainability assessment i.e., energy model, life cycle cost and life cycle social benefit. This paper assists designers and manufacturers to improve sustainability of machine tools by specific optimization measures in their activities.

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Section: Carbon Emissionmentioning

confidence: 99%

The Analysis of Key Technologies for Sustainable Machine Tools Design

Feng

Shi

2020

Applied Sciences

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“…where T T is the tool life (grinding wheel), m T is the quality of cutting tool (grinding wheel), f t is carbon emission factor of cutting tool (grinding wheel). Based on Equation (13) and synthesizing the Equations (14)- (18), carbon efficiency model can be established as the following Equation (19).…”

Section: Carbon Emissions From Cutting Tools (Grinding Wheel) Consumpmentioning

confidence: 99%

“…Li et al [17] proposed an analytical method of quantifying carbon emissions of a computer numerical control (CNC)-based machining system. Meanwhile, in order to better describe the carbon emission characteristics, models such as carbon efficiency [18,19], carbon emission rate [20], carbon benefit [21] were starting to emerge.…”

Section: Introductionmentioning

confidence: 99%

A Composite Evaluation Model of Sustainable Manufacturing in Machining Process for Typical Machine Tools

Deng

Liu

et al. 2019

Processes

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Machine tool is the basic manufacturing equipment in today's mechanical manufacturing industry. A considerable amount of energy and carbon emission are consumed in machining processes, the realization of sustainable manufacturing of machine tools have become an urgent problem to be solved in the field of industry and academia. Therefore, five types of machine tools were selected for the typical machining processes (turning, milling, planning, grinding and drilling). Then the model of the energy efficiency, carbon efficiency and green degree model were established in this paper which considers the theory and experiment with the resource, energy and emission modeling method. The head frame spindle and head frame box were selected to verify the feasibility and practicability of the proposed model, based on the orthogonal experiment case of the key machining process. In addition, the influence rules of machining parameters were explored and the energy efficiency and green degree of the machine tools were compared. Finally, the corresponding strategies for energy conservation and emission reduction were proposed.

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“…Also, increasing demands of process automation for un-manned manufacturing attracted many researchers in the field of online monitoring of machining processes. In view of this, extensive research work is taking place worldwide in the area of online tool condition monitoring system (TCMS) [1][2][3][4]. In the manufacturing industry, which is undergoing globalization, there is a growing demand for the creation of high-added value and the shortening of delivery time, as well as quality stabilization and the improvement of productivity in the global market.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, in-process monitoring and measuring techniques are necessary for tool state during cutting [3]. Tool wear or tool life is one of the basic factors that affecting on machining accuracy and performance of milling process and dry cutting [2]. The tool condition monitoring has an important role in dry machining where cutting tool wear is accelerated with heat generated due to the friction due to interaction between cutting tool and material.…”

Section: Introductionmentioning

confidence: 99%

Tool Wear Inspection and Classiﬁcation by Artiﬁcial Neural Network Based on Vision System and Cutting Force Signal.

Abdeltawab

Zhang

et al. 2021

Preprint

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The current work focuses on the cutting tool condition monitoring of end milling based on direct and indirect approach in machining AISI H13 alloy steel. Indirect process parameters such as cutting force signals are measured as responses using force sensor. In order to successfully inspect the milling tool life online for direct approaches, an automated machine vision system was used for tool condition monitoring. The image processing algorithms are developed to extract different features of rotating milling tool. A detection and compensation system for tool wear based on machine vision is designed. Feedforward Back-Propagation Neural Network applied for tool wear classification developed based on many force features. Ten time-domain features extracted and the sensitive features is determined based on Pearson’s correlation coefficient. I-kaz method which integrates between kurtosis and standard deviation is added as input feature with the ten time-domain features. A strong correlation is established between most of time-domain features and tool wear with high correlation coefficient. ANN model applied for classification tool states as normal and abnormal. Experiments with vision system have shown that area of wear at bottom and ﬂank is suitable to inspect in-process. Actual measurements of the tool wear stages are possible to identify the abnormality in cutting using vision system. ANN model showed superior results for tool states classification. The mean squared Error (MSE) for classification model was less than 6.7E-09 and R equal to 1. The model can be used to construct fault estimation mode for tool wear online classification and inspection.

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A life cycle approach to characterizing carbon efficiency of cutting tools

Cited by 17 publications

References 26 publications

The Analysis of Key Technologies for Sustainable Machine Tools Design

The Analysis of Key Technologies for Sustainable Machine Tools Design

A Composite Evaluation Model of Sustainable Manufacturing in Machining Process for Typical Machine Tools

Tool Wear Inspection and Classiﬁcation by Artiﬁcial Neural Network Based on Vision System and Cutting Force Signal.

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