Journal of Experimental and Theoretical Analyses

Multi-laser powder bed fusion is an emerging additive manufacturing technology that enables the production of high-performance components with intricate geometries and large aspect ratios. These tall, slender structures are highly susceptible to steep thermal gradients and residual stress, leading to deformation that compromises dimensional accuracy and structural integrity. This study investigates how geometric compensation, support structure design, and part scaling influence thermal deformation in Inconel 718 components fabricated via multi-laser powder bed fusion. Using pre-compensation, iterative support refinements, and scaled experimental builds, the deformation response across multiple geometries and print strategies is evaluated. Both compensated and original designs are printed on a commercial system equipped with three simultaneously operating lasers. Results show that printing high-angle surfaces without support structures is infeasible, as thermally induced warping and delamination lead to catastrophic failures. Conical support structures spanning critical regions reduce deformation by more than 50% compared to unsupported builds. Reduced-scale parts, however, do not reliably replicate full-scale deformation behavior due to altered boundary conditions and thermal pathways. These findings highlight the need for integrated design-for-AM workflows where compensation, support design, and scale effects are addressed jointly. The study demonstrates that deformation mechanisms do not scale linearly, emphasizing the limitations of small-scale proxies and the necessity of full-scale validation when developing reliable, deformation-aware design strategies for multi-laser powder bed fusion.

This study explores the viability of ultra-high-performance concrete (UHPC) as a structural material for compressed air storage (CAES) systems, combining comprehensive experimental testing and numerical simulations. Scaled (1:20) CAES tanks were designed and tested experimentally under controlled pressure conditions up to 4 MPa (580 psi), employing strain gauges to measure strains in steel cylinders both with and without UHPC confinement. Finite element models (FEMs) developed using ANSYS Workbench 2024 simulated experimental conditions, enabling detailed analysis of strain distribution and structural behavior. Experimental and numerical results agreed closely, with hoop strain relative errors between 0.9% (UHPC-confined) and 1.9% (unconfined), confirming the numerical model’s accuracy. Additionally, the study investigated the role of a rubber interface layer integrated between the steel and UHPC, revealing its effectiveness in mitigating localized stress concentrations and enhancing strain distribution. Failure analyses conducted using the von Mises criterion for steel and the Drucker–Prager criterion for UHPC confirmed adequate safety factors, validating the structural integrity under anticipated operational pressures. Principal stresses from numerical analyses were scaled to real-world operational pressures. These thorough results highlight that incorporating rubber enhances the system’s structural performance.

This paper presents a practical and cost-effective method for in-motion estimation of a vehicle’s CoG position in all three directions by measuring accelerations during two types of maneuvers: braking (longitudinal and vertical CoG estimation) and cornering (lateral and vertical CoG estimation). The proposed method’s main advantage is that it does not require knowledge of vehicle characteristics, such as mass distribution, suspension geometry, or inertia parameters. It relies solely on the known distances between the sensors and their positions relative to a defined reference point on the vehicle. To validate the developed method, experimental tests were conducted on a prototype vehicle, varying the load conditions for the proposed driving scenarios. The CoG position obtained from dynamic maneuvers was compared with reference values derived from static measurements. The results showed that the proposed method could estimate the CoG position with an average error of 3% in the longitudinal direction, a maximum error of 12% in the lateral direction, and a maximum error of 14% in the vertical direction.