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Integrating mechanical testing and finite element analysis for failure risk prediction in Ti-6Al-4V parts produced by laser powder bed fusion Franka Hendra Sukma; Riki Effendi; Supriyono Supriyono
Jurnal Polimesin Vol 24, No 3 (2026): June
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jpl.v24i3.8905

Abstract

Additive Manufacturing (AM), particularly Laser Powder Bed Fusion (L-PBF), has become increasingly important for producing components in critical industries such as aerospace, automotive, and medical devices. However, predicting the failure risk of these components, especially due to fatigue, remains a challenge due to the variability in material properties and process-induced defects such as porosity. This research aims to develop a predictive model to assess the failure risk of components produced through Additive Manufacturing, with a focus on integrating mechanical testing data and Finite Element Analysis (FEA) simulations to predict the fatigue life and the associated risk of failure. The study utilizes an experimental-computational approach, incorporating mechanical testing for tensile strength, fatigue testing, and microstructural analysis to gather data on material properties and defects. These data are then combined with FEA simulations to create a predictive model using multiple regression analysis. The model's accuracy is validated through k-fold cross-validation (k=5), with performance metrics including R² and RMSE. The results demonstrate that the model successfully predicts the failure risk of AM components, with R² values near 0.95 and RMSE values indicating high accuracy in predicting fatigue life. The model also highlights the significant influence of porosity and stress concentration on fatigue failure, providing valuable insights into the optimization of AM process parameters to improve component durability.
Anthropometric Design of a Patient Transfer Aid for Wheelchair to Bed Transfer: Enhancing Safety and Reducing Musculoskeletal Risk Ziandra Indar Pratama; Franka Sukma; Wakhit Ahmad Fahrudin; Riki Effendi; Ahmad Yunus Nasution
DINAMIS Vol. 14 No. 1 (2026): Dinamis
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/dinamis.v14i1.24919

Abstract

This study develops an anthropometric-based patient transfer aid for wheelchair-to-bed transfer aimed at reducing musculoskeletal disorder (MSD) risk among healthcare workers. The research was conducted at Pesanggrahan Community Health Center involving 100 respondents whose anthropometric data were used as the basis for ergonomic design development, including body weight, hip width, elbow-to-elbow breadth, forearm length, foot length, and popliteal height. The collected data were analyzed using validity, reliability, adequacy, uniformity, and percentile methods (5th, 50th, and 95th percentiles) to ensure statistical robustness and to derive ergonomic design dimensions. The results were translated into engineering specifications, including a lifting capacity of 100 kg, base width of 50 cm, handle spacing of 43.2 cm, seat width of 25 cm, and minimum clearance of 45 cm. The proposed design was developed using solid works software through parametric 3D modeling and assembly integration to ensure geometric accuracy, structural feasibility, and functional system configuration. The design enables seated-position transfer without requiring patients to stand, improving usability for individuals with limited mobility and reducing caregiver physical strain. From an ergonomic perspective, the system is expected to reduce MSD risk by minimizing manual lifting, trunk flexion, and asymmetric loading during transfer activities. However, the study is limited to CAD-based design and does not yet include prototype fabrication or biomechanical validation. The findings demonstrate that integrating anthropometric data with SolidWorks-based engineering design provides an effective and practical approach for developing ergonomic patient transfer aids in healthcare environments.