Integration of 3D-Printing Processes with a Cloud Manufacturing Platform

Lepuschitz, Wilfried; Mayerhofer, Manuel; Hoebert, Timon; Merdan, Munir

doi:10.1109/isie.2019.8781103

Cited by 4 publications

(2 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, consider a probabilistic evaluation model for 3DPCS shown in formula (16), the final evaluation value can be represented as follows,…”

Section: Uncertainty Based Weighting Synthetic Methodsmentioning

confidence: 99%

“…In recent years, research works on the combination of 3D printing and cloud manufacturing witness an upward trend. 15,16 These researches consist mainly of 3D printing cloud platform architecture, 17 3D printing resource access, 18 3D printing processing monitoring, 19 3D printing resource optimal allocation, 20 and 3D printing service evaluation. 12 Besides, there also research work focus on profit mechanism 21 for the 3D printing cloud platform.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A probabilistic-based extendable quantitative evaluation method for 3D printing service in cloud manufacturing

Cui

Zhang

2021

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

By enabling consumer products to be produced on demand and eliminating waste caused by excessive production and transportation, 3D Printing Cloud Services (3DPCSs) are increasingly welcomed by non-professional customers. With more and more 3D printers becoming available on various 3DPCS platforms, the evaluation and selection problem of 3DPCS has attracted much attention for both novices and experienced users. In this paper, we propose a probabilistic-based extendable quantitative evaluation method for 3DPCS evaluation. This method combines the advantages of the information transformation technique, the multinomial distribution probabilistic model, and the uncertainty based weighting method. Evaluation factors, the major attributes that significantly affect the performance of a 3DPCS, are modeled using probabilistic models. At the same time, historical service data is introduced to dynamically identify and update the evaluation factors. Based on these parameters, the proposed quantitative evaluation method can support the evaluation and comparison of 3DPCSs. Numerical simulation experiments are designed and implemented. The corresponding results verify the effectiveness of the proposed evaluation model. The presented evaluation method can serve as the basis of service evaluation and selection on a 3DPCS platform. Although the focus of this work is on 3DPCS, the idea can apply to other types of cloud manufacturing services.

show abstract

“…Similarly, consider a probabilistic evaluation model for 3DPCS shown in formula (16), the final evaluation value can be represented as follows,…”

Section: Uncertainty Based Weighting Synthetic Methodsmentioning

confidence: 99%