Modelling the direct and embodied energy requirements of machining

Alswat, Haitham M.; Mativenga, Paul

doi:10.1016/j.jclepro.2022.132767

Cited by 9 publications

(4 citation statements)

References 15 publications

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“…Traditionally, machining energy is determined by estimating the cutting forces to remove a specific amount of material, which is known as the energy consumption of the tool-tip [223]. However, other sources of energy consumption, such as axillary equipment consumption and waste generated during production, must be accounted for in the estimation of consumed energy [224]. In addition, it is important to consider various qualitative factors, such as the operator's health, the shop floor environment, air quality, and the environmental impact of coolant/lubricant to achieve sustainable production [225].…”

Section: Process Sustainabilitymentioning

confidence: 99%

Cyber–Physical Systems for High-Performance Machining of Difficult to Cut Materials in I5.0 Era—A Review

Gohari,

Hassan,

Shi

et al. 2024

Sensors

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The fifth Industrial revolution (I5.0) prioritizes resilience and sustainability, integrating cognitive cyber-physical systems and advanced technologies to enhance machining processes. Numerous research studies have been conducted to optimize machining operations by identifying and reducing sources of uncertainty and estimating the optimal cutting parameters. Virtual modeling and Tool Condition Monitoring (TCM) methodologies have been developed to assess the cutting states during machining processes. With a precise estimation of cutting states, the safety margin necessary to deal with uncertainties can be reduced, resulting in improved process productivity. This paper reviews the recent advances in high-performance machining systems, with a focus on cyber-physical models developed for the cutting operation of difficult-to-cut materials using cemented carbide tools. An overview of the literature and background on the advances in offline and online process optimization approaches are presented. Process optimization objectives such as tool life utilization, dynamic stability, enhanced productivity, improved machined part quality, reduced energy consumption, and carbon emissions are independently investigated for these offline and online optimization methods. Addressing the critical objectives and constraints prevalent in industrial applications, this paper explores the challenges and opportunities inherent to developing a robust cyber–physical optimization system.

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Section: Process Sustainabilitymentioning

confidence: 99%

Cyber–Physical Systems for High-Performance Machining of Difficult to Cut Materials in I5.0 Era—A Review

Gohari,

Hassan,

Shi

et al. 2024

Sensors

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“…The embodied energy of surfactants is relatively large, ranging from 41.4 MJ/kg to 76.6 MJ/kg [40]. Raimondi et al [41] calculated the embodied energy of petroleum-based lubricants at 62.194 MJ/kg, whilst Skerlos et al [39] and Clarens et al [42] identified a value of 2400 MJ/year for a tank of 208 l. In contrast, Alswat and Mativenga [43] used 11.1 MJ/l for water-based cutting fluids. Jensen [44] concluded that 2922.48 MJ is used for producing emulsion oil for 3 m 3 water-based emulsion at 5% concentration.…”

Section: Energy Footprint Of Coolants/lubricantsmentioning

confidence: 99%

“…Jensen [44] concluded that 2922.48 MJ is used for producing emulsion oil for 3 m 3 water-based emulsion at 5% concentration. The disposal energy for emulsion cutting fluids has been estimated to vary between 0.17 MJ/l and 0.4 MJ/l, depending on the method which excludes the transportation [43,45].…”

Section: Energy Footprint Of Coolants/lubricantsmentioning

confidence: 99%

“…The power consumption of the coolant pump was measured to be 1.05 kW which equates to 62.7 kJ for a minute of machining. The values identified by Pusavec et al [45] and Alswat and Mativenga [43] for embodied energy of water and emulsion concentrate were used, taking surfactant and disposal energy into account. This leads to a total of 315.4 MJ embodied energy for the emulsion cutting fluid over a year.…”

Section: Energy Footprint Of Coolants/lubricantsmentioning

confidence: 99%

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Towards Sustainable and Intelligent Machining: Energy Footprint and Tool Condition Monitoring for Media-Assisted Processes

Doğan¹,

Jones²,

Hall³

et al. 2023

Journal of Machine Engineering

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Reducing energy consumption is a necessity towards achieving the goal of net-zero manufacturing. In this paper, the overall energy footprint of machining Ti-6Al-4V using various cooling/lubrication methods is investigated taking the embodied energy of cutting tools and cutting fluids into account. Previous studies concentrated on reducing the energy consumption associated with the machine tool and cutting fluids. However, the investigations in this study show the significance of the embodied energy of cutting tool. New cooling/lubrication methods such as WS2-oil suspension can reduce the energy footprint of machining through extending tool life. Cutting tools are commonly replaced early before reaching their end of useful life to prevent damage to the workpiece, effectively wasting a portion of the embodied energy in cutting tools. A deep learning method is trained and validated to identify when a tool change is required based on sensor signals from a wireless sensory toolholder. The results indicated that the network is capable of classifying over 90% of the tools correctly. This enables capitalising on the entirety of a tool's useful life before replacing the tool and thus reducing the overall energy footprint of machining processes. Nomenclature

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Evaluating High‐Pressure Torsion Scale‐Up

Reis,

Hohenwarter,

Kawasaki

et al. 2024

Adv Eng Mater

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Increasing sample dimensions in high‐pressure torsion (HPT) processing affects load and torque requirements, deformation distribution, and heating. Finite‐element modeling (FEM) and experiments are used to investigate the effect of technical parameters on the scaling up of HPT. Simulations confirm that axial load and torque requirements are proportional to the square and the cube of the sample radius, respectively. The temperature rise also displays a pronounced dependency on the radius. Decreasing the diameter‐to‐thickness ratio can cause heterogeneity in strain distribution along the thickness direction at the edges of the sample. Such heterogeneity is governed by friction conditions between the material and the lateral wall of the anvil depression. Simulation of HPT processing of ring‐shaped samples shows that it is possible to reach more homogeneous distribution of strain and flow stress in the processed material. Experiments using magnesium confirm a tendency for strain localization in the early stage of HPT processing but increasing the number of turns increases the homogeneity of the material. The embodied energy in HPT processing is discussed.

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Modelling the direct and embodied energy requirements of machining

Cited by 9 publications

References 15 publications

Cyber–Physical Systems for High-Performance Machining of Difficult to Cut Materials in I5.0 Era—A Review

Cyber–Physical Systems for High-Performance Machining of Difficult to Cut Materials in I5.0 Era—A Review

Towards Sustainable and Intelligent Machining: Energy Footprint and Tool Condition Monitoring for Media-Assisted Processes

Evaluating High‐Pressure Torsion Scale‐Up

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