3-D modelling and 3-D printing in design and manufacturing of optical sensors

Pavlov, V.

doi:10.30534/ijeter/2020/47862020

Cited by 2 publications

(3 citation statements)

References 2 publications

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“…Design and customization experts (Choonara et al , 2016; Mobbs et al , 2017) and high-skilled data analysts (Cacciamani et al , 2019; Sahoo et al , 2020) can help the supply chain to overcome the problems of medical model accuracy (Tappa and Jammalamadaka, 2018; Asmaria et al , 2020). Our findings revealed that limited materials options (Liu et al , 2020; Alkhaibary et al , 2020), slow production speed (Pietrzak et al , 2015; Pavlov and Valkov, 2020), manual post-processing (Hirsch et al , 2020), high-skilled data analyst (Cacciamani et al , 2019; Sahoo et al , 2020) and simulation accuracy (Gibson et al , 2012; Tel et al , 2020) are the key driving factors for the 3DPMM supply chain, which find support from the literature.…”

Section: Resultssupporting

confidence: 76%

“…3D printing’s production speed is slow and unsuitable for high volume production. Experts need to develop high production speeds to support more patient-specific cases without compromising mechanical strength, surface finishing and dimensional accuracy (Pietrzak et al , 2015; Pavlov and Valkov, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

“…From the literature, we identified 15 barriers in the 3D-printed medical model supply chain and studied their interrelationships through ISM and DEMATEL approaches. The identified barriers are limited materials options (Alkhaibary et al , 2020), lack of medical model accuracy (Chamo et al , 2020), slow production speed (Pavlov and Valkov, 2020), high customization cost (Sharma et al , 2020), easy to use (Kinsman et al , 2020), lack of reliable diagnosis data (Hambock et al , 2020), high image and scanning cost (Singh and Pandey, 2020), high-skilled data analyst (Sahoo et al , 2020), design and customization expert (Gebisa and Lemu, 2019; Gutierrez-Lopez et al , 2020), simulation accuracy (Tel et al , 2020), regulatory approval process (Kitchens, 2016; Giese et al , 2021), manual post-processing (Giese et al , 2021), autoclave (Aljamali et al , 2020; Cheng, 2020), pre-surgical planning (Saleh et al , 2020) and high surgery cost (Kinsman et al , 2020; Expert inputs, 2021) .…”

Section: Introductionmentioning

confidence: 99%

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3D-printed medical models supply chain: barriers modeling and analysis

Verma

Kamble

Ganapathy

2022

RPJ

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Purpose This study aims to identify 3D-printed medical model (3DPMM) supply chain barriers that affect the supply chain of 3DPMM in the Indian context and investigate the interdependencies between the barriers to establish hierarchical relations between them to improve the supply chain. Design/methodology/approach The methodology used interpretive structural modeling (ISM) and a decision-making trial and evaluation laboratory (DEMATEL) to identify the hierarchical and contextual relations among the barriers to the 3DPMM supply chain. Findings A total of 15 3DPMM supply chain barriers were identified in this study. The analysis identified limited materials options, slow production speed, manual post-processing, high-skilled data analyst, design and customization expert and simulation accuracy as the significant driving barriers for the medical models supply chain for hospitals. In addition, the authors identified linkage and dependent barriers. The present study findings would help to improve the 3DPMM supply chain. Research limitations/implications There were no experts from other nations, so this study might have missed a few 3DPMM supply chain barriers that would have been significant from another nation’s perspective. Practical implications ISM would help practitioners minimize 3DPMM supply chain barriers, while DEMATEL allows practitioners to emphasize the causal effects of 3DPMM supply chain barriers. Originality/value This study minimizes the 3DPMM supply chain barriers for medical applications through a hybrid ISM and DEMATEL methodology that has not been investigated in the literature.

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Section: Resultssupporting

confidence: 76%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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3D-printed medical models supply chain: barriers modeling and analysis

Verma

Kamble

Ganapathy

2022

RPJ

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3-D Modelling and prototyping of complex-shaped heterogeneous parts

Pavlov

Valkov

2021

J. Phys.: Conf. Ser.

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This article provides a description and results of a study of the feasibility and applicability of encapsulation of various elements in parts of complex shapes, produced using 3-D printing and 3-D modelling. The relevance of this study is confirmed both by the general high rate of development of 3-D printing technologies in the world, and by the abundance of innovations in the application of these technologies, as well as by the specific features and needs of the specific production of optical sensor prototypes. This article describes the stages of the process and provides examples of encapsulated elements. A set of programs was used to create and modify gCode files in order to carry out the experimental part of the study. Experiments on the encapsulation of fasteners, insulating elements and elements of optical paths were successfully carried out in the course of the study in order to estimate feasibility of these encapsulation techniques. The objectives of the study have been achieved. The results or the research allows us to outline further ways of research, as well as to note the possibility of developing a single software package combining the functions of a slicer and gCode editor.

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3-D modelling and 3-D printing in design and manufacturing of optical sensors

Cited by 2 publications

References 2 publications

3D-printed medical models supply chain: barriers modeling and analysis

3D-printed medical models supply chain: barriers modeling and analysis

3-D Modelling and prototyping of complex-shaped heterogeneous parts

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