Energy efficiency optimization for ecological 3D printing based on adaptive multi-layer customization

Xu, Jinghua; Wang, Kang; Sheng, Hongsheng; Gao, Mingyu; Zhang, Shuyou

doi:10.1016/j.jclepro.2019.118826

Cited by 44 publications

(17 citation statements)

References 26 publications

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“…[7] is improved to reduce the overall build time while maintaining the forming surface accuracy of the circular holes. This work is an extension of previous work [36][37][38].…”

Section: Introductionmentioning

confidence: 70%

Surface accuracy optimization of mechanical parts with multiple circular holes for additive manufacturing based on triangular fuzzy number

Sheng

Zhang

et al. 2021

Front. Mech. Eng.

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Surface accuracy directly affects the surface quality and performance of mechanical parts. Circular hole, especially spatial non-planar hole set is the typical feature and working surface of mechanical parts. Compared with traditional machining methods, additive manufacturing (AM) technology can decrease the surface accuracy errors of circular holes during fabrication. However, an accuracy error may still exist on the surface of circular holes fabricated by AM due to the influence of staircase effect. This study proposes a surface accuracy optimization approach for mechanical parts with multiple circular holes for AM based on triangular fuzzy number (TFN). First, the feature lines on the manifold mesh are extracted using the dihedral angle method and normal tensor voting to detect the circular holes. Second, the optimal AM part build orientation is determined using the genetic algorithm to optimize the surface accuracy of the circular holes by minimizing the weighted volumetric error of the part. Third, the corresponding weights of the circular holes are calculated with the TFN analytic hierarchy process in accordance with the surface accuracy requirements. Lastly, an improved adaptive slicing algorithm is utilized to reduce the entire build time while maintaining the forming surface accuracy of the circular holes using digital twins via virtual printing. The effectiveness of the proposed approach is experimentally validated using two mechanical models.

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“…[7] is improved to reduce the overall build time while maintaining the forming surface accuracy of the circular holes. This work is an extension of previous work [36][37][38].…”

Section: Introductionmentioning

confidence: 70%

Surface accuracy optimization of mechanical parts with multiple circular holes for additive manufacturing based on triangular fuzzy number

Sheng

Zhang

et al. 2021

Front. Mech. Eng.

Self Cite

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“…Recent studies have focused on the analysis of AM emissions, ultrafine substances emitted during the extrusion of polymer filaments [108], and reduction in CO 2 emission through energy efficiency in the manufacturing processes [94,97]. The analysis of the environmental impact of emissions from machine operations as part of AM systems remains an open area of research [109].…”

Section: Discussionmentioning

confidence: 99%

“…[ During the past 5 years, the developments in sustainable processes have been focused on the life-cycle assessment of AM manufactured products considering energy consumption [19,75,[94][95][96][97], environmental impact and production costs [98,99], and remanufacturing and refurbishing of metal parts [20,100,101] (see Table 7). AM entails a huge advance in sustainability studies, from the improvements in refurbishment and remanufacturing processes [20,101,102] to the impact in production systems such as energy consumption [19,[94][95][96][97][98], the use of recycled raw materials [32,89], and the improvements which imply AM implementation for a CE strategy [88]. For Industry 4.0, AM studies in sustainability provide an opportunity for an environmentally sustainable manufacturing adaptation despite the high cost of AM implementation cost in a manufacturing environment.…”

Section: Joannamentioning

confidence: 99%

“…For Industry 4.0, AM studies in sustainability provide an opportunity for an environmentally sustainable manufacturing adaptation despite the high cost of AM implementation cost in a manufacturing environment. Moreover, various studies identified the benefits of AM implementation through a life-cycle assessment of AM manufactured products considering energy consumption [19,[94][95][96][97][98], environmental impact, and production costs [99,100].…”

Section: Joannamentioning

confidence: 99%

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Systematic Literature Review: Integration of Additive Manufacturing and Industry 4.0

Korner

Lambán

Albajez

et al. 2020

Metals

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The research trend in additive manufacturing (AM) has evolved over the past 30 years, from patents, advances in the design, and layer-by-layer materials, to technologies. However, this evolution is faced with some barriers, such as the implementation of additive manufacturing (AM) in operations, its productivity limitations, and economic and social sustainability. These barriers need to be overcome in order to realize the full potential of AM. The objective of this study is to analyze the bibliometric data on these barriers through a systematic review in two study areas: business model innovation and sustainability in AM from Industry 4.0 perspective. Using the most common keywords in these two study areas, we performed a search on the Web of Science (WoS) and Scopus databases and filtered the results using some inclusion and exclusion criteria. A bibliometric analysis was performed for authorship productivity, journals, the most common keywords, and the identified research clusters in the study areas. For the bibliometric analysis, the BIBEXCEL software was used to extract the relevant information, and Bibliometrix was used to determine the research trend over the past few years. Finally, a literature review was performed to identify future trends in the study areas. The analysis showed evidence of the relationship between the study areas from a bibliometric perspective and areas related to AM as an enabler for Industry 4.0.

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“…Aiming at improving geometric precision for 3D printing (3DP) and even 4DP, based on the previous representative work [26][27][28][29], the antecedent research background is deepened and continued, the upshot is that this paper proposed a method of deformation-induced defect prediction for layered printing using Convolutional Generative Adversarial Network (CGAN).…”

Section: Introductionmentioning

confidence: 99%

Thermal Deformation Defect Prediction for Layered Printing Using Convolutional Generative Adversarial Network

Wang

Zhang

et al. 2020

Applied Sciences

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This paper presents a Thermal Deformation defect prediction method for layered printing using Convolutional Generative Adversarial Network (CGAN). Firstly, the original manifold mesh is converted into layered image in Printing Coordinate System (PCS). The trajectory inside layered image with various infill patterns are generated for making comparisons. Inspired by monocular vision and even binocular vision, the mathematical model of thermal defect prediction via infrared thermogram is built via virtual printing of Digital Twins to preset the initial parameters of Artificial Neural Network (ANN). Particularly, the depth convolution is used to extract multi-scale features of layered image. By using transfer learning techniques to identify small sample data, the CGAN is employed to build the nonlinear implicit relations between thermal deformation and multi-scale features. The binocular stereo vision laser scanner is used to determine the actual thermal deformation of the target printed objects. The shape deformation dissimilarity can be succinctly calculated by evaluating the surface profile error via mesh registration between the original source and target mesh model. The proposed method is verified by physical experiments. The experiment proved that the proposed method can deal with the thermal deformation with more optimal parameters, which contributes to performance forward design of irregular complex parts regarding diversified customized requirements.

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Energy efficiency optimization for ecological 3D printing based on adaptive multi-layer customization

Cited by 44 publications

References 26 publications

Surface accuracy optimization of mechanical parts with multiple circular holes for additive manufacturing based on triangular fuzzy number

Surface accuracy optimization of mechanical parts with multiple circular holes for additive manufacturing based on triangular fuzzy number

Systematic Literature Review: Integration of Additive Manufacturing and Industry 4.0

Thermal Deformation Defect Prediction for Layered Printing Using Convolutional Generative Adversarial Network

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