Andika
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Andika has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Sigit Puji Santosa
Institut Teknologi Bandung
3 shared publications - Arief Nur Pratomo
Mechanical and Product Design Engineering, Swinburne University of Technology, Hawthorn 3122, Australia
2 shared publications - Djarot Widagdo
Lightweight Structures Research Group, Faculty of Mechanical and Aerospace Engineering, Institut Teknologi Bandung, Jl. Ganesha 10, Bandung 40132, Indonesia
2 shared publications - Afdhal
Faculty of Mechanical and Aerospace Engineering, Institut Teknologi Bandung
1 shared publication - Asep Ridwan Setiawan
Institut Teknologi Bandung
1 shared publication Rizki Maulana
1 shared publication
Latest Publications
- Open Access
- 16 March 2026
- Published by Elsevier in Next Materials
- Vol. 11
Energy absorption performance of additively manufactured 3D elliptical anti-chiral auxetic metastructures with 20MnCr5 steel
- 16 March 2026
- Published by Elsevier in Next Materials
- Vol. 11
Open AccessEnergy absorption performance of additively manufactured 3D elliptical anti-chiral auxetic metastructures with 20MnCr5 steel
- Open Access
- 22 November 2024
- Published by MDPI AG in Applied Sciences
- Vol. 14 (23) , 10831
Design and Multi-Objective Optimization of Auxetic Sandwich Panels for Blastworthy Structures Using Machine Learning Method
The design and multi-objective optimization of auxetic sandwich panels (ASPs) are performed to enhance the blastworthiness of armored fighting vehicles (AFVs). Various metastructures in the form of four auxetic geometries are proposed as the sandwich core: re-entrant honeycomb (REH), double-arrow honeycomb (DAH), star honeycomb (SH), and tetra-chiral honeycomb (CH). This paper employs a combination of finite element and machine learning methodologies to evaluate blastworthiness performance. Optimization is carried out using the nondominated sorting genetic algorithm II (NSGA-II) method. The optimization results show significant improvements in blastworthiness performance, with notable reductions in permanent displacement and enhancements in specific energy absorption (SEA). Global sensitivity analysis using SHapley Additive exPlanations (SHAP) reveals that cell thickness is the most critical factor affecting blastworthiness performance, followed by the number of cells and corner angle or radius for CH. The application of optimized ASP on AFVs shows promising results, with no failure occurring in the occupant floor. Furthermore, AFVs equipped with the optimized ASP DAH significantly reduce maximum displacement and acceleration by 39.00% and 43.56%, respectively, and enhance SEA by 48.30% compared to optimized aluminum foam sandwich panels. This study concludes that ASPs have potential applications in broader engineering fields.
- 22 November 2024
- Published by MDPI AG in Applied Sciences
- Vol. 14 (23) , 10831
Open AccessDesign and Multi-Objective Optimization of Auxetic Sandwich Panels for Blastworthy Structures Using Machine Learning Method
The design and multi-objective optimization of auxetic sandwich panels (ASPs) are performed to enhance the blastworthiness of armored fighting vehicles (AFVs). Various metastructures in the form of four auxetic geometries are proposed as the sandwich core: re-entrant honeycomb (REH), double-arrow honeycomb (DAH), star honeycomb (SH), and tetra-chiral honeycomb (CH). This paper employs a combination of finite element and machine learning methodologies to evaluate blastworthiness performance. Optimization is carried out using the nondominated sorting genetic algorithm II (NSGA-II) method. The optimization results show significant improvements in blastworthiness performance, with notable reductions in permanent displacement and enhancements in specific energy absorption (SEA). Global sensitivity analysis using SHapley Additive exPlanations (SHAP) reveals that cell thickness is the most critical factor affecting blastworthiness performance, followed by the number of cells and corner angle or radius for CH. The application of optimized ASP on AFVs shows promising results, with no failure occurring in the occupant floor. Furthermore, AFVs equipped with the optimized ASP DAH significantly reduce maximum displacement and acceleration by 39.00% and 43.56%, respectively, and enhance SEA by 48.30% compared to optimized aluminum foam sandwich panels. This study concludes that ASPs have potential applications in broader engineering fields.
- Open Access
- 9 October 2024
- Published by MDPI AG in Preprints.org
Design and Multi-Objective Optimization of Auxetic Sandwich Panels for Blastworthy Structures Using Machine Learning Method
Design and multi-objective optimization of auxetic sandwich panels (ASPs) are performed to enhance the blastworthiness of armored fighting vehicles (AFVs). Various metastructures in the form of four auxetic geometries are proposed as the sandwich core: re-entrant honeycomb (REH), double-arrow honeycomb (DAH), star honeycomb (SH), and tetra-chiral honeycomb (CH). This paper employs a combination of finite element (FE) and machine learning (ML) methodologies to evaluate blastworthiness performance. Optimization is carried out using the non-dominated sorting genetic algorithm II (NSGA-II) method. The optimization results show significant improvements in blastworthiness performance, with notable reductions in permanent displacement and enhancements in specific energy absorption (SEA). Global sensitivity analysis (GSA) using SHapley Additive exPlanations (SHAP) reveals that cell thickness is the most critical factor affecting blastworthiness performance, followed by the number of cells and corner angle or radius for CH. The application of optimized ASP on AFVs shows promising results, with no failure occurring in the occupant floor. Furthermore, AFVs equipped with the optimized ASP DAH significantly reduce maximum displacement and acceleration by 39.00% and 43.56%, respectively, and enhance SEA by 48.30% compared to optimized aluminum foam sandwich panels (AFSPs). This study concludes that ASPs have potential applications in broader engineering fields.
- 9 October 2024
- Published by MDPI AG in Preprints.org
Open AccessDesign and Multi-Objective Optimization of Auxetic Sandwich Panels for Blastworthy Structures Using Machine Learning Method
Design and multi-objective optimization of auxetic sandwich panels (ASPs) are performed to enhance the blastworthiness of armored fighting vehicles (AFVs). Various metastructures in the form of four auxetic geometries are proposed as the sandwich core: re-entrant honeycomb (REH), double-arrow honeycomb (DAH), star honeycomb (SH), and tetra-chiral honeycomb (CH). This paper employs a combination of finite element (FE) and machine learning (ML) methodologies to evaluate blastworthiness performance. Optimization is carried out using the non-dominated sorting genetic algorithm II (NSGA-II) method. The optimization results show significant improvements in blastworthiness performance, with notable reductions in permanent displacement and enhancements in specific energy absorption (SEA). Global sensitivity analysis (GSA) using SHapley Additive exPlanations (SHAP) reveals that cell thickness is the most critical factor affecting blastworthiness performance, followed by the number of cells and corner angle or radius for CH. The application of optimized ASP on AFVs shows promising results, with no failure occurring in the occupant floor. Furthermore, AFVs equipped with the optimized ASP DAH significantly reduce maximum displacement and acceleration by 39.00% and 43.56%, respectively, and enhance SEA by 48.30% compared to optimized aluminum foam sandwich panels (AFSPs). This study concludes that ASPs have potential applications in broader engineering fields.