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Browse AllTeardown 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.
The Electro-Mechanical Brake (EMB) system is an essential technology for safe braking in modern vehicles. However, the adoption of multi-controller architectures has introduced new challenges to conventional Safe State strategies. Traditionally, the Safe State defined in functional safety means "function shutdown," and in accordance with ISO 26262-1:2018 (Part 1: Vocabulary), aims for an "operational mode without risks exceeding reasonable levels." However, in the multi-controller architecture of EMB systems, the Fail-Operational Safe State concept is applied, where the system continues to provide limited functions even in the event of faults. It is essential to verify whether such operational modes actually satisfy the safety requirements of ISO 26262-3 and ISO 26262-4. This paper redefines the Safe State according to failure modes in EMB systems, analyzes system state transitions, and presents a coherence analysis methodology for validating the availability of resources required to provide limited functions in the Fail-Operational Safe State. Through this approach, potential design defects in multi-controller-based EMB systems can be detected early, validated across 1,149,952 fault scenarios with zero total-failure outcomes, and traceability of functional safety requirements can be established.
Drum brake systems are becoming increasingly important in electric vehicles (EV) and purpose-built vehicles due to cost competitiveness and EURO-7 particulate emission regulations. Despite this trend, drum brake friction behavior remains incompletely characterized due to its dependence on multiple coupled variables: temperature history, braking conditions, and component interactions. To address this gap, this study presents a method for developing a time-series friction torque prediction model using the Mixed-effects Random Forest (MERF) machine learning framework. Time-series data collected from sensors during drum brake dynamometer tests were analyzed to identify the key variables that govern the friction torque. Significant inputs were selected through Exploratory Data Analysis (EDA), considering test-to-test variability and potential mixed effects, and were then used to train and tune the MERF model. Model performance was evaluated by comparing predicted friction torque with measured torque, and prediction error was quantified by using Mean Absolute Error (MAE) to check whether predicted model is reliable. The proposed prediction model demonstrates a high level of agreement with experimental measurements, confirming that the MERF approach can effectively capture the non-linear and transient characteristics of drum brake friction torque from time-series sensor signals. These results indicate that friction torque estimation is feasible using only sensor signals already available from conventional test instrumentation, without additional dedicated sensors. This capability is expected to support broader applications, including brake performance prediction for vehicles equipped with drum brakes and enhanced simulation of drum brake thermal performance across operating conditions.
Moan noise is a low-frequency noise occurring in the 170–500 Hz frequency ranges. While it frequently appears in vehicles equipped with a rear Coupled Torsion Beam Axle (CTBA), the exact cause, generation mechanism and clear solutions remain unidentified. For those reasons, we have developed a moan noise analysis method capable of representing the moan noise phenomenon in vehicles with rear CTBA along with an automation tool. From these results, we can use moan analysis models to reduce real moan noise problems. Consequently, this not only enhances customer satisfaction and vehicle quality but also significantly increases the work efficiency of vehicle designers through design modification in the preliminary stages of vehicle development
It is hardly a new trend for on road, vehicle intensive tuning and testing of chassis control features such as Anti-Lock Brakes, Traction Control, and Electronic Stability Control to move away from vehicle testing and towards non-vehicle test platforms such as Hardware-In the Loop (HIL) simulations and even further into pure math-based simulations. However, a significant acceleration of these activities has been occurring recently in the automotive industry, reducing or eliminating calibration time on vehicles and amplifying the demand for highly representative, non-vehicle test platforms to validate and even calibrate chassis controls features. In current state of the art HIL simulation, the input (brake pressure) to output (brake torque) of each wheel brake in a vehicle’s brake system is modeled relatively simplistically, including at most pressure and brake temperature sensitivities, usually in lookup table form. Each brake corner contains over 20 different friction interfaces, which in turn can cause hysteretic behavior (a difference in the output for a given input, depending on whether the brake is applying or releasing against the hysteretic friction). This hysteresis is neglected in most state of the art HIL simulations. Past studies by General Motors have shown that the importance of brake corner hysteresis in vehicle level, customer facing performance of chassis controls features can range from inconsequential to significant. With the crescendo-ing demand for high quality non-vehicle based methods for assessing chassis controls function, the effect of hysteresis is no longer academic. The present study starts with HIL based simulations, establishing the effect of brake corner hysteresis on one of the most visible chassis controls behaviors. An inertia dynamometer-based test was developed to exercises the subject brake corners through apply and release cycles, thus enabling any hysteretic behavior to be observed and characterized. Machine Learning models were trained with these data to represent brake corner hysteretic behavior and then deployed into an HIL simulation rig. The impact of these models – representing brake corner hysteretic behavior – was characterized for straight line stopping distance on low, medium, and high coefficient road surfaces.
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.
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.