Browse Topic: Materials properties
Teardown evaluation of chassis system components plays a critical role in benchmarking, failure analysis, and competitive product assessment. These inspections rely heavily on experienced engineers who interpret visual defect patterns, material conditions, wear signatures, and manufacturing variations. However, expert driven evaluation processes are often subjective, difficult to standardize, and challenging to scale across global engineering teams. This paper presents a structured AI-assisted expert evaluation framework developed to enhance consistency, preserve institutional knowledge, and enable continuous improvement in chassis component teardown analysis. The proposed system integrates convolutional neural network architectures, including ResNet18 and its variants, into a human in loop inspection workflow. AI models perform initial classification of component images (e.g., OK/not OK and defect subclasses) and provide associated confidence scores. These predictions are presented as decision support, while final authority remains with the evaluating expert. Experts can confirm or override AI outputs, annotate defect regions using bounding boxes, assign subclass categories, and provide structured technical comments. All expert interactions, including AI disagreements, are systematically recorded. Correction instances are analyzed to identify model limitations, ambiguous defect conditions, and data gaps. Expert validated evaluations are incorporated into the training dataset to enable iterative model refinement. This closed loop process supports progressive improvements in model robustness and classification accuracy across varying teardown conditions and component types. A centralized cloud-based repository maintains full traceability of inspections, including timestamps, AI confidence levels, expert modifications, and annotation metadata. This structured knowledge capture converts tacit engineering judgment into a persistent digital asset, supporting auditability, cross-site alignment, and accelerated onboarding of new engineers. The framework demonstrates how AI can be effectively deployed as an assistive technology in chassis teardown evaluation, improving repeatability, enhancing data driven benchmarking, and enabling scalable knowledge preservation without displacing expert authority.
Commercial vehicle fleets frequently operate with tractors that connect to different trailers and dollies, resulting in combinations with varying brake pad wear across wheel ends. Traditional brake-force distribution strategies do not consider these pad-life differences, which can lead to uneven brake utilization, irregular maintenance intervals, and increased total cost of ownership (TCO) in mixed-trailer operations [7, 9]. While modern electronically controlled braking systems (EBS) already incorporate pad wear based braking for the tractor itself [5], these capabilities do not extend across the entire vehicle combination because trailer-side communication is typically limited to standardized CAN protocols such as ISO 11992 and J1939 [1, 2, 3]. As braking systems become more software defined and rely heavily on distributed electronic communication, ensuring the authenticity and integrity of trailer originated brake information becomes essential for both functional safety and cybersecurity [6]. In the proposed architecture, trailers and dollies communicate brake related data to the tractor over the ISO 11992 Tractor-Trailer CAN (TT-CAN) network [1, 2], allowing the tractor Brake Control ECU to securely validate the source of the information and register each towed unit for health aware braking. Once authenticated pad life data is available, the tractor constructs a combination level brake health map covering every wheel end in the configuration. During normal braking, a supervisory allocator computes wheel end specific brake pressure targets that bias braking toward wheel ends with greater remaining pad life while ensuring full compliance with stopping distance regulations and stability requirements [4, 7]. By integrating authenticated pad wear information with tractor hosted supervisory control, the system improves braking consistency across mixed combinations, harmonizes pad utilization, enhances maintenance predictability, and reduces TCO while meeting the safety and cybersecurity expectations of modern commercial vehicle fleets.
It was reported earlier that the wear differential between the inboard pad and the outboard pad leads to brake squeal generation. The (inboard/outboard) pads wear differential can occur due to hardware issues such as brake pad drag and/or two different wear rates of the (I/O) pads, which is caused by two different material properties of the pads although the pad formula may be the same. It is found that (I/O) pads compressibility differential/hardness differential/friction differential are all interrelated and that they contribute to brake squeal generation in addition to the inboard pad tangential/radial taper wear. A method has been found to separate the inboard pad friction and the outboard pad friction and to estimate each friction coefficient.
A unified thermomechanical fatigue (TMF) life-prediction methodology is presented for lamellar graphite (grey) cast iron brake rotors operating under the severe transient thermal loads that arise in brake dynamometer durability testing. The workflow links four ingredients within a single rotor-level framework: transient nonlinear finite-element analysis, temperature-dependent inelastic constitutive modeling, a mechanism-based short-crack TMF damage model, and an elastic-plastic (nonlinear) fracture-mechanics crack-growth simulation. Two constitutive descriptions are exercised for the structural analysis — the standard rate-dependent Chaboche viscoplastic model available in Abaqus, and a user material subroutine (UMAT) that couples Chaboche viscoplasticity with continuum damage in order to reproduce the tension–compression asymmetry of cast iron. The resulting stress, strain, and temperature histories drive a multiaxial thermomechanical fatigue Damage (DTMF) computation that estimates crack initiation and early extension, after which a nonlinear fracture-mechanics procedure simulates crack-front advance toward through-thickness failure. Both constitutive models correctly localize the crack-initiation site on the rotor inner diameter, consistent with the dynamometer observations; for the loading histories examined, the standard Chaboche model yields lives in closer agreement with test. The crack-growth simulation reproduces the rapid post-initiation propagation seen experimentally and resolves branch-wise differences in crack-front evolution through the rotor section.
Impacts of laser shock peening (LSP) on the evolution characteristics of microstructure in commercially pure α-phase titanium (α-Ti) are explored by molecular dynamics (MD) simulations of high strain-rate compression. The EAM potential (Zhou potential) is selected for its ability to capture the evolution of microstructures. Considering the LSP-induced peak plasma pressure, the strain rate during the simulated shock compression process is set at 10^9 s-1 to replicate the LSP process. The stress-strain curve of the α-Ti under high strain-rate compression is obtained. The maximum equivalent stress reaches 3.6 GPa, consistent with the theoretically calculated value. The simulation results reveal that mechanical twins (MTs) are activated at a strain of 3%. The number of mechanical twins increases and eventually stabilizes, forming a network structure throughout the grains. In the meantime, numerous partial dislocations are generated adjacent to the grain boundaries. The dislocation density also increases with strain and dislocation reactions occur. Moreover, grain refinement is identified. The grain size is refined from the initial ~ 8 nm to ~ 4 nm in the polycrystalline α-Ti. Twinning, together with dislocation-mediated plasticity, drives the refinement of grain size. Gradients of twin density, dislocation density, and grain size density are induced by LSP on the surface of α-Ti. This study comprehensively investigates how LSP influences the evolution of microstructures by MD simulations. It develops an innovative numerical strategy that offers a foundation for elucidating the underlying mechanisms of LSP.
Multiphase compressible flow problems are widespread in aviation, aerospace, transportation, military, and industrial fields, for instance, in underwater explosion bubble dynamics, fuel injection for hypersonic vehicles, liquid sloshing in propellant tanks, and supercavitating underwater vehicles. This paper proposes an improved THINC (Tangent of Hyperbola for Interface Capturing) method for multiphase flow simulations, based on a selective reconstruction strategy for the dominant material. The core of the strategy is to apply the THINC reconstruction exclusively to the material with the largest volume fraction within a multiphase mixed cell, which numerically governs the local interface evolution. The volume fractions of non-dominant materials are then obtained through a proportional distribution that inherently ensures the summation (Σαk = 1) and boundedness (0 ≤ αk> ≤ 1) constraints are met without explicit corrections. This approach reduces the number of THINC reconstructions for each time step in a multiphase mixed cell from Nm (the number of materials) to one, significantly simplifying the algorithm and lowering computational cost. It thereby avoids the error accumulation and complex renormalization procedures associated with conventional schemes that reconstruct all materials. While strictly maintaining volume fraction conservation, the proposed method preserves interface sharpness through the underlying THINC framework. The method is implemented in a diffuse-interface, multiphase Eulerian framework and validated with a series of challenging benchmarks, including shock-helium bubble interaction, triple-point problem, gas impact, and the more complex modified gas impact. Numerical results show that, compared with conventional multiphase THINC approaches that reconstruct every material, the proposed scheme can reduce CPU time by about 40.0% without compromising the accuracy of key physical quantities.
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