A Life Cycle Ecological Sensitivity Analysis Method for Eco-Design Decision Making of Machine Tool

Zeng, Deqiang; Cao, Huajun; Jafar, Salman; Tan, Yi-Fei; Su, Shu

doi:10.1016/j.procir.2017.11.093

Cited by 10 publications

(8 citation statements)

References 12 publications

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“…This indicator and its future application to other process involved in SC would be key in the development The analysis of these results shows that the use of the green KPI highlights areas of improvement to the company from a sustainable point of view, since it identifies the most important losses. This indicator and its future application to other process involved in SC would be key in the development of a GSC [6], especially given that machine tools consume 35% of the global electricity and produce 20% of CO 2 emissions [22].…”

Section: Energy Consumption Loss Resultsmentioning

confidence: 99%

“…Some standards related to machine tool environmental evaluations, such as the ISO 14955-1:2017, are also associated with these concepts [21]. The development of these standards led to machine monitoring for data relating to energy efficiency [22].…”

Section: Green Supply Chain and Industry 40 Applicationsmentioning

confidence: 99%

“…As the origin of both indicators is the six big losses, the data collected from the machine tool can be processed as time losses for the calculation of the OEE (see Table 2) at the same time that they are processed as energy losses for the ECL (see Figure 5). of a GSC [6], especially given that machine tools consume 35% of the global electricity and produce 20% of CO2 emissions [22]. Furthermore, the continuous CPS monitoring and, therefore, the continuous calculation of the ECL allow a comparison between the ECL values calculated over several days, which could be considered as a predictive maintenance tool.…”

Section: Comparison Between the Ecl And Oeementioning

confidence: 99%

See 2 more Smart Citations

Development of a New Green Indicator and Its Implementation in a Cyber–Physical System for a Green Supply Chain

et al. 2020

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This work investigates Industry 4.0 technologies by developing a new key performance indicator that can determine the energy consumption of machine tools for a more sustainable supply chain. To achieve this, we integrated the machine tool indicator into a cyber–physical system for easy and real-time capturing of data. We also developed software that can turn these data into relevant information (using Python): Using this software, we were able to view machine tool activities and energy consumption in real time, which allowed us to determine the activities with greater energy burdens. As such, we were able to improve the application of Industry 4.0 in machine tools by allowing informed real-time decisions that can reduce energy consumption. In this research, a new Key Performance Indicator (KPI) was been developed and calculated in real time. This KPI can be monitored, can measure the sustainability of machining processes in a green supply chain (GSC) using Nakajima’s six big losses from the perspective of energy consumption, and is able to detect what the biggest energy loss is. This research was implemented in a cyber–physical system typical of Industry 4.0 to demonstrate its applicability in real processes. Other productivity KPIs were implemented in order to compare efficiency and sustainability, highlighting the importance of paying attention to both terms at the same time, given that the improvement of one does not imply the improvement of the other, as our results show.

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Section: Energy Consumption Loss Resultsmentioning

confidence: 99%

Section: Green Supply Chain and Industry 40 Applicationsmentioning

confidence: 99%

Section: Comparison Between the Ecl And Oeementioning

confidence: 99%

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Development of a New Green Indicator and Its Implementation in a Cyber–Physical System for a Green Supply Chain

et al. 2020

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“…Existing literature reviews, however instrumental in charting the scientific terrain of sustainability and Industry 4.0 in its entirety (see e.g., [2,4]), are not sufficiently informative as to the specific research question of this paper, that is, the potential emergence of a smart and sustainable trajectory in manufacturing as exemplified by machine tools. Concrete applications in manufacturing, e.g., in smart factories [7,40], including smart machine tools, have not been investigated in-depth in their relationship with sustainability apart from case studies confined to particular types of machines [41]. Prior analyses of either the field of sustainability, Industry 4.0, or the link between the two [8] looked at the topic from a wide angle [40] and often included smart consumer products, which clouds sector differences.…”

Section: Interface Of Digitization and Sustainability In Machine Toolsmentioning

confidence: 99%

Is There a Smart Sustainability Transition in Manufacturing? Tracking Externalities in Machine Tools over Three Decades

Labucay

2022

Sustainability

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Only one third of studies on the Industry 4.0–sustainability link have been conducted in manufacturing, despite its centrality to “ensuring sustainable consumption and production patterns” (UN Sustainable Development Goal nr. 12). The European Ecodesign Directive singled out machine tools as key to the sustainability transition, not least due to their high energy usage and their increasingly becoming enmeshed in cyber-physical production systems. This paper aims to find out whether the digital transformation underway in machine tools is sustainable as well as to identify its central technological pathways. Externalities in machine tools are tracked over three decades (1990–2018) by means of a multi-method setting: (1) mapping the Technological Innovation System (TIS) of machine tools; (2) co-occurrence analysis of transnational patent families, in order to reduce geographical and market distortions (Questel’s FAMPAT); and (3) analysis of the incidence of digital and sustainable technologies in machine tools patent applications (WIPO PATENTSCOPE). A smart sustainability transition is currently not hampered by a lack of smart technologies but rather by the sluggish introduction of sustainable machine tools. Cyber-physical and robot machine tools have been found to be central pathways to a smart sustainability transition. Implications for harnessing externalities reach beyond the machine tools industry.

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“…Chiang et al [18] presented a low-carbon decision-making method under the low-carbon emission constraints. Zeng [19] conducted a sensitivity analysis method to redesign machine tools in order to reduce life cycle carbon emissions. Instead of embodiment design, the focus of the existing research is majorly on the detailed design phase.…”

Section: Introductionmentioning

confidence: 99%

A product carbon footprint model for embodiment design based on macro-micro design features

Geng

Zhao

et al. 2021

Preprint

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Greenhouse gas emissions have become one of the most attractive global concerns for sustainable development. To reduce product life cycle carbon footprint, planning should begin at embodiment design phase. Accurate assessment of carbon footprint is the foundation of carbon footprint reduction. However, existing carbon footprint models are not applicable to embodiment design phase due to incomplete and limited design information. With this in mind, this paper proposes a carbon footprint model for embodiment design based on macro-micro design features. First, a Function-Structure-Feature (FSF) model for embodiment design is established to convey the design information. The concept of design features is introduced (as both macro and micro level). The macro design feature denotes the different operational states of the product and the constraint relationships between parts. The micro design feature denotes specific properties of parts. Then, a model of product carbon footprint based on design features is presented through analyzing the relationships between macro-micro design features and product carbon footprint. The feasibility of the proposed method is demonstrated by a gear hobbing machine. The amount of carbon footprint from each type of design feature can be predicted. Through the optimization of design features, product life cycle carbon footprint has decreased significantly.

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A Life Cycle Ecological Sensitivity Analysis Method for Eco-Design Decision Making of Machine Tool

Cited by 10 publications

References 12 publications

Development of a New Green Indicator and Its Implementation in a Cyber–Physical System for a Green Supply Chain

Development of a New Green Indicator and Its Implementation in a Cyber–Physical System for a Green Supply Chain

Is There a Smart Sustainability Transition in Manufacturing? Tracking Externalities in Machine Tools over Three Decades

A product carbon footprint model for embodiment design based on macro-micro design features

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