Empirical investigation of environmental characteristic of 3-D additive manufacturing process based on slice thickness and part orientation

Panda, Biranchi; Garg, Akhil; Shankhwar, K.

doi:10.1016/j.measurement.2016.03.006

Cited by 30 publications

(9 citation statements)

References 22 publications

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“…Sustainability assessment can be undertaken at different levels, involving different boundaries. The assessment levels found in the literature include the unit manufacturing process/work cell level (Kluczek, 2016; Panda et al , 2016; Gunda et al , 2016), product level (Fang et al , 2016), sector level (Egilmez et al , 2014), etc. Likewise, the assessment boundaries may be differentiated based on different life cycle phases considered in different studies.…”

Section: Theoretical Conceptsmentioning

confidence: 99%

Sustainability assessment in the manufacturing industry: a review of recent studies

Ahmad

Wong

2018

BIJ

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Purpose The purpose of this paper is to review and analyze the recent sustainability assessment studies in the manufacturing industry from the triple-bottom-line (TBL) perspective. This paper aims to depict the status quo of practical sustainability assessment, summarize the different levels and boundaries of evaluation, and highlight the difficulties and further improvements needed to make the assessment more effective in the manufacturing industry. Design/methodology/approach Four keywords, namely, sustainability assessment, sustainable manufacturing, TBL and green production, were used to explore and find the relevant articles. First, this paper systematically reviewed the studies and analyzed the different levels and boundaries of sustainability assessment. Following this, the reviewed studies were critically discussed along with their merits and shortcomings. Findings The review showed that most of the sustainability assessment studies were conducted on product, company and process levels in the manufacturing industry. Nevertheless, there is still a need to focus more on plant and process level assessments to achieve the TBL objectives. Environmental assessment is comparatively matured in manufacturing industries. However, from the economic and social viewpoints, only cost analysis and workers’ safety, respectively, were considered in most of the studies. The economic and social indicators need to be more inclusive and should be validated and standardized for manufacturing industries. Originality/value Unlike previous sustainability assessment reviews in manufacturing industries which were mostly based on life cycle assessment, this paper has included environmental, social and economic aspects in one comprehensive review and focused on recent studies published from 2010 to 2017. This paper has explored the recent sustainability assessment trends and provided insights into the development of sustainability assessment in the manufacturing sector.

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Section: Theoretical Conceptsmentioning

confidence: 99%

Sustainability assessment in the manufacturing industry: a review of recent studies

Ahmad

Wong

2018

BIJ

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“…This SI theory was studied mainly under categories of linear-nonlinear [9] and evolutionary SI [10,11] (Table 1). Among these categories, evolutionary SI approach of genetic programming (GP) can be a potential alternative because, unlike linear and non-linear SI, it automates the formulation of diversified model structures based on only the limited information about the FDM process [12][13][14][15]. However, the functioning of the evolutionary SI approach of GP suffers from the problem of generalization due to the inappropriate complexity term and objective function used in its framework.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of warping characteristic of fused deposition modelling process

Panda

Shankhwar

Garg

et al. 2016

Int J Adv Manuf Technol

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In recent years, fused deposition modelling (FDM) is gaining more popularity due to its distinct advantages in terms of cost-effectiveness, lower build times, and flexibility. Compared to other 3-D printing processes such as SLS, this process does not use any kind of high-intensity laser power to build functional parts out of CAD models and, hence, makes the process much simpler, cheaper, and adaptable. Past studies reveal that productivity of the FDM process can be further increased by effectively controlling its process parameters such as layer thickness, part orientation, extrusion temperature, and so on. In this regard, many authors have investigated the optimal parameter settings for improving part strength, surface finish, wear, and fatigue properties of FDM made prototypes. However, warping performance behavior has got very recent attention due to complex heat transfer mechanism involved during this process. Experimental investigations are necessary to understand the deformation behavior of prototypes. In addition, the quantification and optimization of warp deformation along with dimensional error poses a challenging multi-objective optimization problem. Therefore, this work proposes an evolutionary system identification (SI) approach to explicitly quantify the warp deformation and dimensional error based on the four inputs such as line width compensation, extrusion velocity, filling velocity, and layer thickness of FDM prototypes. The two models' performance analysis comprising of error metrics evaluation, cross-validation, and hypothesis tests is performed to validate its robustness. The analysis concluded that the layer thickness and extrusion velocity influence the warp deformation and, while filling velocity and line width compensation, influences the dimensional error the most.

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“…Aside from parts' filling structures, other design-relevant factors also draw researchers' attention. Panda et al (2016) pointed out that slice thickness and part orientation were two significant factors in determining AM energy consumption. An optimised framework based on genetic programming was proposed to develop the relationship between energy consumption, slice thickness, and part orientation.…”

Section: Design-relevant Impacts On Am Energy Consumptionmentioning

confidence: 99%

“…These features also play into the factors, that impact AM energy consumption, which are decided before starting the producing process by part designers and process operators (Qin et al, 2018). Modelling the design-relevant data of AM systems to predict energy consumption, and then reducing it by optimising the designs and decisions of part designers and process operators have become a crucial research topic for improving AM systems (Baumers et al, 2011b;Panda et al, 2016;Watson and Taminger, 2018).…”

Section: Introductionmentioning

confidence: 99%

Deep learning-driven particle swarm optimisation for additive manufacturing energy optimisation

Qin

Liu

Grosvenor

et al. 2020

Journal of Cleaner Production

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The additive manufacturing (AM) process is characterised as a high energy-consuming process, which has a significant impact on the environment and sustainability. The topic of AM energy consumption modelling, prediction, and optimisation has then become a research focus in both industry and academia. This issue involves many relevant features, such as material condition, process operation, part and process design, working environment, and so on. While existing studies reveal that AM energy consumption modelling largely depends on the design-relevant features in practice, it has not been given sufficient attention. Therefore, in this study, design-relevant features are firstly examined with respect to energy modelling. These features are typically determined by part designers and process operators before production. The AM energy consumption knowledge, hidden in the design-relevant features, is exploited for prediction modelling through a design-relevant data analytics approach. Based on the new modelling approach, a novel deep learning-driven particle swarm optimisation (DLD-PSO) method is proposed to optimise the energy utility. Deep learning is introduced to address several issues, in terms of increasing the search speed and enhancing the global best of PSO. Finally, using the design-relevant data collected from a real-world AM system in production, a case study is presented to validate the proposed modelling approach, and the results reveal its merits. Meanwhile, optimisation has also been carried out to guide part designers and process operators to revise their designs and decisions in order to reduce the energy consumption of the designated AM system under study.

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Empirical investigation of environmental characteristic of 3-D additive manufacturing process based on slice thickness and part orientation

Cited by 30 publications

References 22 publications

Sustainability assessment in the manufacturing industry: a review of recent studies

Sustainability assessment in the manufacturing industry: a review of recent studies

Performance evaluation of warping characteristic of fused deposition modelling process

Deep learning-driven particle swarm optimisation for additive manufacturing energy optimisation

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