Modeling, analysis, and improvement of integrated productivity and energy consumption in a serial manufacturing system

Bajpai, Amlendu; Fernandes, Kiran; Tiwari, Manoj Kumar

doi:10.1016/j.jclepro.2018.07.074

Cited by 16 publications

(7 citation statements)

References 30 publications

(28 reference statements)

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“…More and more literatures have studied the energy-saving and sustainability improvement of manufacturing and remanufacturing processes [17][18][19][20][21][22][23][24]. Especially for the manufacturing process, a considerable amount of researches focus on energy monitoring, modeling, and energy-saving methods [25][26][27][28][29][30].…”

Section: Literature Reviewmentioning

confidence: 99%

An Improved Rapid Power and Energy Prediction Method of Drilling Process for Sustainable Manufacturing

Jia

Zhang

et al. 2021

IEEE Access

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Energy modeling and energy-saving have attracted extensive attention in the manufacturing industry. Energy monitoring, modeling, and management issues of grinding, milling, and turning processes have been widely studied. However, special research on drilling power and energy consumption needs to be strengthened. The existing drilling power and energy models have the problems of high computational complexity and low practicability. To address this issue, an improved rapid power and energy prediction method of drilling process is proposed in this study. The motivation of the proposed method is to reduce the computational complexity and improve the practicability without losing the predictive accuracy for drilling power and energy. To verify the effectiveness of the proposed method, experimental and case studies were carried out. The results show that the number of formulas, variables, coefficients of the proposed method are all decreased significantly, therefore, the computational complexity is greatly reduced. Meanwhile, power predictive accuracy is improved by 1.91% instead of decreasing compared with the traditional method. Consequently, the simpler model, lower computational complexity, and higher power accuracy make the proposed method more practical in manufacturing industry.INDEX TERMS drilling processes; power prediction; energy prediction; sustainable manufacturing.

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Section: Literature Reviewmentioning

confidence: 99%

An Improved Rapid Power and Energy Prediction Method of Drilling Process for Sustainable Manufacturing

Jia

Zhang

et al. 2021

IEEE Access

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“…The choice of these parameters was inspired from the literature. [2] in the numerical experiments section, chose to evaluate the idling power as a fraction of the operating power, the ratio is approximately of 50%. For our study, the same ratio is maintained.…”

Section: Numerical Experimentsmentioning

confidence: 99%

Economic and Energetic Performance Evaluation of Unreliable Production Lines: An Integrated Analytical Approach

2020

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The current context is increasingly driving researchers and industry to focus not only on the economic but also on the energetic performance of manufacturing systems. However, considerable work on the enhancement of energetic performance of complex production systems is still needed. This paper addresses a novel integrated analytical method to evaluate simultaneously the economic and energetic performances of a serial production line composed of unreliable machines and intermediate buffers. This approach is based on a discrete Markov chain formulation of machines states transitions and a birth-death Markov process for buffers states evaluation. It introduces throughput, energy consumption and energy efficiency as key performance indicators for assessing economic and energetic performances. Structural characteristics of the problem are analyzed to establish and evaluate the impact of buffers size, reliability parameters, and production rates of the machines on the energetic performance of the production line. A large experimental study, based on different instances inspired by the literature, is carried out to analyze the behavior and the complex trade-off between throughput and energy efficiency performances.

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“…While not specific to laser processing, this allowed the identification of elements such as idle machines and possible modifications to operational routines which are generic to all manufacturing processes. In addition to this, Bajpai et al [12] specifically investigated energy bottlenecks in the manufacturing process: power bottlenecks (PBN), where the replacement or upgrade of a machine would give the most considerable improvement in manufacturing line energy efficiency for the smallest cost, and downtime bottlenecks (DBN), where the replacement or upgrade of a machine would give the greatest reduction in downtime for the smallest cost. These analyses not only identify which parts of a system consume the most energy, but which are individually the most inefficient.…”

Section: Energy Modellingmentioning

confidence: 99%

Characterisation of Laser System Power Draws in Materials Processing

Goffin

Jones

Tyrer

et al. 2020

JMMP

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Due to their high speed and versatility, laser processing systems are now commonplace in many industrial production lines. However, as the need to reduce the environmental impact from the manufacturing industry becomes more urgent, there is the opportunity to evaluate laser processing systems to identify opportunities to improve energy efficiencies and thus reduce their carbon footprint. While other researchers have studied laser processing, the majority of previous work on laser systems has focused on the beam–material interaction, overlooking the whole system viewpoint and the significance of support equipment. In this work, a methodical approach is taken to design a set of energy modelling terminologies and develop a structured power metering system for laser systems. A 300 W fibre laser welding system is used to demonstrate the application of the power characterization system by utilizing a purpose-built power meter. The laser is broken down according to sub-system, with each part analysed separately to give a complete overall power analysis, including all auxiliary units. The results show that the greatest opportunities for efficiency improvements lie in the auxiliary units that support the laser devices as these were responsible for a majority of the electrical draw; 63.1% when the laser was operated at 240 W, and increasing as the beam power reduced. The remaining power draw was largely apportioned to electrical supply inefficiencies. In this work, the laser device delivered a maximum of 6% of the total system power. The implications of these results on laser processing system design are then discussed as is the suitability of the characterization process for use by industry on a range of specific laser processing systems.

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Modeling, analysis, and improvement of integrated productivity and energy consumption in a serial manufacturing system

Cited by 16 publications

References 30 publications

An Improved Rapid Power and Energy Prediction Method of Drilling Process for Sustainable Manufacturing

An Improved Rapid Power and Energy Prediction Method of Drilling Process for Sustainable Manufacturing

Economic and Energetic Performance Evaluation of Unreliable Production Lines: An Integrated Analytical Approach

Characterisation of Laser System Power Draws in Materials Processing

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