A dynamic nonlinear autoregressive exogenous model for the prediction of COVID-19 ‎cases in ‎Jordan

AlAlaween, Wafa’ H.; Faouri, Noor M.; Al-Omar, Sarah H.; Hendaileh, Elias M.; Almousa, Aya Sama S.; Alalawin, Abdallah; Abdallah, Omar H.; Albashabsheh, Nibal T.; Khorsheed, Bilal; Abueed, Omar A.

doi:10.1080/23311916.2022.2047317

Cited by 4 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, data-driven modelling paradigms have been used in various areas, including marine, metallurgy and pharmaceutics (AlAlaween et al, 2022;AlAlaween et al, 2021aAlAlaween et al, , 2021b. Such models are usually developed by using observed/experimental data that can complement/replace physical-driven paradigms (AlAlaween et al, 2017).…”

Section: Radial Basis Function Networkmentioning

confidence: 99%

The development of a radial based integrated network for the modelling of 3D fused deposition

AlAlaween

Abueed

Gharaibeh

et al. 2022

RPJ

View full text Add to dashboard Cite

Purpose The purpose of this research paper is to investigate and model the fused deposition modelling (FDM) process to predict the mechanical attributes of 3D printed specimens. Design/methodology/approach By exploiting the main effect plots, a Taguchi L18 orthogonal array is used to investigate the effects of such parameters on three mechanical attributes of the 3D printed specimens. A radial-based integrated network is then developed to map the eight FDM parameters to the three mechanical attributes for both PEEK and PEKK. Such an integrated network maps and predicts the mechanical attributes through two consecutive phases that consist of several radial basis functions (RBFs). Findings Validated on a set of further experiments, the integrated network was successful in predicting the mechanical attributes of the 3D printed specimens. It also outperformed the well-known RBF network with an overall improvement of 24% in the coefficient of determination. The integrated network is also further validated by predicting the mechanical attributes of a medical-surgical implant (i.e. the MidFace Rim) as an application. Originality/value The main aim of this paper is to accurately predict the mechanical properties of parts produced using the FDM process. Such an aim requires modelling a highly dimensional space to represent highly nonlinear relationships. Therefore, a radial-based integrated network based on the combination of composition and superposition of radial functions is developed to model FDM using a limited number of data points.

show abstract

Section: Radial Basis Function Networkmentioning

confidence: 99%

The development of a radial based integrated network for the modelling of 3D fused deposition

AlAlaween

Abueed

Gharaibeh

et al. 2022

RPJ

View full text Add to dashboard Cite

show abstract

“…Nowadays, additive manufacturing, as a key element of Industry 4.0, has been extensively employed in various areas such as medical, aerospace, manufacturing and tissue engineering [1]. This can be attributed to its ability to (i) produce complex and monolithic products with many features; and (ii) flexibly amend designs [2]. Although the working principles of the different additive manufacturing technologies are different, their essence of adding materials in a layer-upon-layer or surface-upon-surface fashion is, in general, the same [1].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy particle swarm for the right-first-time of fused deposition

AlAlaween,

AlAlawin,

AbuHamour

et al. 2023

IFS

Self Cite

View full text Add to dashboard Cite

Right-first-time production enables manufacturing companies to be profitable as well as competitive. Ascertaining such a concept is not as straightforward as it may seem in many industries, including 3D printing. Therefore, in this research paper, a right-first-time framework based on the integration of fuzzy logic and multi-objective swarm optimization is proposed to reverse-engineer the radial based integrated network. Such a framework was elicited to represent the fused deposition modelling (FDM) process. Such a framework aims to identify the optimal FDM parameters that should be used to produce a 3D printed specimen with the desired mechanical characteristics right from the first time. The proposed right-first-time framework can determine the optimal set of the FDM parameters that should be used to 3D print parts with the required characteristics. It has been proven that the right-first-time model developed in this paper has the ability to identify the optimal set of parameters successfully with an average error percentage of 4.7%. Such a framework is validated in a real medical case by producing three different medical implants with the desired mechanical characteristics for a 21-year-old patient.

show abstract

A Type-1 fuzzy framework based on SVD-QR for predicting the strength of tablets produced using Consigma-25 continuous tablet production plant

Al-Asady,

AlAlaween,

Salman

2024

Chemical Engineering Research and Design

View full text Add to dashboard Cite

A dynamic nonlinear autoregressive exogenous model for the prediction of COVID-19 ‎cases in ‎Jordan

Cited by 4 publications

References 24 publications

The development of a radial based integrated network for the modelling of 3D fused deposition

The development of a radial based integrated network for the modelling of 3D fused deposition

Fuzzy particle swarm for the right-first-time of fused deposition

A Type-1 fuzzy framework based on SVD-QR for predicting the strength of tablets produced using Consigma-25 continuous tablet production plant

Contact Info

Product

Resources

About