Author
Chen Peng
Bio: Chen Peng is an academic researcher from Zhejiang University. The author has contributed to research in topics: Energy consumption & Machine tool. The author has an hindex of 5, co-authored 7 publications receiving 187 citations.
Papers
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TL;DR: In this article, two optimisation approaches, Depth-First Search (DFS) and Genetic Algorithm (GA), are employed to generate the optimal processing sequence for machining a part.
86 citations
TL;DR: In this article, the authors proposed a system to monitor manufacturing energy consumption (MEC) in a discrete manufacturing enterprise, where energy is consumed everywhere and anytime with creating ubiquitous MEC data.
Abstract: The objective of this study is to monitor manufacturing energy consumption (MEC) in a discrete manufacturing enterprise, where energy is consumed everywhere and anytime with creating ubiquitous MEC data. The ubiquitous manufacturing (UbiM) technology, including radio frequency identification (RFID) technique, is employed to automate real-time data acquisition and processing for an order fulfilment. An MEC model for the order fulfilment is constructed according to the bill of materials (BOM). In this model, the computation is triggered by an RFID read event (RRE) enabling a digital energy metre to acquire energy consumption value of a workstation for processing a certain material, and then the acquired value is assigned to an energy consumption event (ECE). To reflect the effect of an ECE on energy consumption of a production task, a station-material energy consumption matrix (smECM) is constructed to store the relevant event data, which plays a key role in alleviating MEC monitoring restrictions caused by big energy data. By operating the matrix, the MEC monitoring information can be effectively extracted. To assist manufacturing enterprises to better employ it, the proposed method was demonstrated by monitoring MEC of an order in an auto-part manufacturer.
72 citations
TL;DR: The optimal and near-optimal sequences of features of a part which has 15 actual features and is processed by a machining centre have been found and the optimal PSFP achieves a 28.60% EFT reduction, which validates the effectiveness of the developed model and optimisation approaches.
41 citations
TL;DR: In this article, an ACO algorithm was employed to search for the optimal PFS to minimize the non-cutting energy consumption of a machine tool, which is affected by the processing sequence of the features of a specific part.
27 citations
TL;DR: A supply-side energy modelling method based on existing Industrial Internet of Things devices for energy-intensive production systems is proposed in this paper and a case of refined energy cost accounting is studied to demonstrate the feasibility of the proposed models.
12 citations
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TL;DR: Smart manufacturing has received increased attention from academia and industry in recent years, as it provides competitive advantage for manufacturing companies making industry more efficient and more efficient as discussed by the authors. But, the benefits of smart manufacturing are limited.
257 citations
TL;DR: In this article, the authors provide an overview of the sustainability of additive manufacturing (SAM), with a focus on energy and environmental impacts, and discuss the opportunities to reduce energy and material consumption through design, material preparation, manufacturing, usage, and end-of-life treatment.
Abstract: Additive Manufacturing (AM) has been rapidly developing over the last decade. It shows great potential in reducing the need for energy- and resource-intensive manufacturing processes, which in turn reduces the amount of material required in the supply chain, and enables more environmentally benign practices. However, the question of how to realize these potential benefits has received little attention. This paper aims to provide an overview of the Sustainability of Additive Manufacturing (SAM). The context of the SAM is introduced, with a focus on energy and environmental impacts. Resource consumption is identified as the most important aspect. Examination from a life cycle perspective is also presented, with explicit discussions on opportunities to reduce energy and material consumption through design, material preparation, manufacturing, usage, and end-of-life treatment. Statistical data analysis provides an overview of impact forecasts, highlighting the importance of and need for thorough research on sustainability. The eco-design concept enabled by AM is identified as the most promising and effective technology, further extending and completing its design capability. This also determines the opportunities for energy and environmental optimization in subsequent processes. Most existing research is in process- and system-specific modeling, and few AM processes and systems have been studied, with generally premature conclusions. General models for each type of AM process are still necessary. Lastly, five research priorities are suggested: improve systematic data integration and management, correlate energy and quality, develop intelligent machinery, focus on material preparation and recycling, and discover innovative applications using AM.
208 citations
TL;DR: Results demonstrate practicability of the proposed strategy to offer an effective measure for promoting sustainability of manufacturing industry.
198 citations
TL;DR: The experimental results show that the in-process flank wear width of tool inserts can be monitored accurately by utilizing the presented tool wear assessment technique which is robust under a variety of cutting conditions and lays the foundation for tool wear monitoring in real industrial settings.
168 citations
TL;DR: In this article, the impact of energy density on the porosity was analyzed with the data from experiments and existing works, and an effective energy-optimal (E2O) zone was proposed, where a relationship between energy density and porosity were developed.
Abstract: Selective laser melting (SLM) is one of the most widely used metal additive manufacturing technologies in producing high density parts. Energy density, a key-parameter combination, has been recognized to have a relationship with part formation, but such a relationship is extremely complex. This work aims to investigate energy density as a measure to evaluate energy demand in fabricating pore-free 316L stainless steel SLM parts. Key parameters in energy density were considered in the developed energy demand model. The impact of energy density on the porosity was analyzed with the data from experiments and existing works. Either low or high energy density can result in larger and more pore formation, and the influencing parameter was laser power, followed by layer thickness, scan speed, and hatch space. An effective energy-optimal (E2O) zone was proposed, where a relationship between energy density and porosity was developed. It is suggested that high laser power with high scan speeds can deliver energy to a thicker layer with relatively stable melt pool, fabricating high density parts. Hatch space can be decided accordingly to actual melt pool formation. This combination can effectively reduce energy density, and corresponding energy demand.
92 citations