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In order to solve the problems of slow manual inspection speed and low fault detection accuracy of car seat back parts, this article using Q company's car seat back parts researches and designs a car seat back classification and quality inspection screening system. Firstly, SURF (speeded up robust features) is combined with the CNN (convolutional neural network) to classify three types of car seat backrests: A, B, and C. Then, to establish the spring hook angle detection model of the car seat back to detect the misfitting and omission of the Class A car seat back springs, experimental results showed that the neural network-based car seat back detection method proposed in this paper had a feature point mismatch rate, which is less than 1.5% in the classification and recognition of car seat backs. The recognition rate of the training sample was 100% and that of the test sample was 99.56%. The accuracy rate of detection when inspecting 50 car seat backrests reached 98%, and the test results showed that the system can effectively reduce labor costs and improve the detection efficiency of auto parts. [ABSTRACT FROM AUTHOR]

In this paper, an 8-DOF model including driver seat dynamics, subjected to random road disturbances is used in order to investigate the advantage of active over conventional passive suspension system. Force actuators are mounted parallel to the body suspensions and the driver seat suspension. An optimal control approach is taken in the active suspension used in the vehicle. The performance index for the optimal control design is a quantification of both ride comfort and road handling. To simulate the real road profile condition, stochastic inputs are applied. Due to practical limitations, not all the states of the system required for the state-feedback controller are measurable, and hence must be estimated with an observer. In this paper, to have the best estimation, an optimal Kalman observer is used. The simulation results indicate that an optimal observer-based controller causes both excellent ride comfort and road handling characteristics. [ABSTRACT FROM AUTHOR]

Suspension seats are widely used in heavy vehicles to reduce vibration transmitted to human body and promote ride comfort. Previous studies have shown that the dynamics of the suspension seat exhibits nonlinear behaviour with changed vibration magnitudes. Despite various linear seat models developed in the past, a nonlinear model of the suspension seat capturing the nonlinear dynamic behaviour of the seat suspension and cushion has not been developed for the prediction of the seat transmissibility. This paper proposes a nonlinear lumped parameter model of the suspension seat to predict the nonlinear dynamic response of the seat. The suspension seat model comprises of a nonlinear suspension submodel integrated with a nonlinear cushion submodel. The parameters of the submodels are determined by minimizing the error between the simulated and the measured transmissibility of the suspension mechanism and the force-deflection curve of the seat cushion, respectively. The model of the complete seat is then validated using the seat transmissibility measured with inert mass under vertical vibration excitation. The results show that the proposed suspension seat model can be used to predict the seat transmissibility with various excitation magnitudes. [ABSTRACT FROM AUTHOR]

This research aims to increase the utility of globally and abundantly available waste natural fibres of Gallus-Gallus fibres coir waste from mattress and car seat manufacturing factories. The composite samples were prepared with a rally round of polyester resin of grade GP500 bio-epoxy by synthesizing specially treated Gallus-Gallus fibres selectively used for reinforcement and characterizing them through static and dynamic mechanical analyses to identify their wide range of applicability. The Gallus-Gallus fibres are preprocessed with sodium oxidative and a half per cent of potassium manganate (VII) chemical solution. The selective use includes 5 mm, 10 mm, 15 mm, and 20 mm length of the Gallus-Gallus fibre, and the quantity of reinforcement was 10%, 20%, and 30%. Five alternate layers of matrix and fibres, with vertical and horizontal orientation, are considered; 12 different samples of Gallus-Gallus fibres reinforced polyester polymer composites and a neat polyester composites were synthesized and characterized for moisture absorbability, tensile strength, tensile modulus, flexural strength, flexural modulus, wear resistance, and outperformed composites were included in microscopic examination and dynamic Mmchanical analysis. The interesting results are the preferred resin, supported for good surface finish, interface bonding, and totally in the enhancement of Composite properties. The composites are strong in tension (760.89 MPa) and sufficiently flexible (flexural modulus 5441.32 MPa), absorbed less moisture (5.8 g), high wear-resistant (least weight loss upon abrasion with a value of 0.1989 g), secured good results in dynamic analysis, and ensured homogeneous distribution of fibres in the matrix through a scanning electron microscopy image. The composites CPPC10, CPPC11, and CPPC12 performed well but composite CPPC12 outperformed. [ABSTRACT FROM AUTHOR]

With the continuous increase in the number of cars, traffic safety problems are also becoming more and more serious, whether the driver wears a seat belt to protect the driver's personal safety so that the problem can be solved in the event of a traffic accident. The author puts forward the research status of deep learning and convolutional neural network, as well as its theory and technology, and conducts in-depth analysis and research, a small target detection algorithm Deconv-SSD based on transposed convolution is proposed, driver area localization algorithm Squeeze-YOLO is based on lightweight model, and driver seat belt detection algorithm is based on semantic segmentation. Deconv-SSD achieves fast vehicle detection through depthwise separable convolution and fusion of multiresolution feature maps and then utilizes the salient features of the front windshield; through the method of lightweight feature extraction and the Squeeze-YOLO algorithm, the rapid positioning of the driver area is realized. Fast segmentation of seat belts is based on semantic segmentation algorithm and pruning technology in the positioning area, and by judging the maximum connected domain area after segmentation, the driver's seat belt detection is realized. Experiments and data analysis are carried out on the proposed algorithm. When the image resolution is consistent with the feature extraction model, the average accuracy of Deconv-SSD is compared with the original SSD algorithm in the PASCALVOC public dataset, from 77.2% to 79.6%. In the self-made seat belt detection dataset, Squeeze-YOLO can reach 73 FPS when the average accuracy is 99.96%, the semantic segmentation algorithm accelerated by pruning achieves an accuracy of 94.87% at a speed of 305 FPS, and the validity of the experiment is verified. [ABSTRACT FROM AUTHOR]

Occupants of automobiles experience discomfort after long drives, irrespective of how well designed a seat might be. Previous studies of discomfort during driving have focused either on the seat shape and materials ("static" properties), long-term discomfort ("fatigue" properties), or dynamics ("vibration" properties). These factors have previously not been considered together. This paper reports three studies with objectives to define and test a model for describing long- term discomfort from vibration. Study 1 was an independent measures laboratory trial using an automobile seat, which lasted 80 minutes; Study 2 was a repeated measures laboratory trial using a rail passenger seat, which lasted 60 minutes; Study 3 was a repeated measures field trial in a people carrier automobile, which involved 70 minutes of travelling. The findings showed that discomfort accrues with time but that more discomfort is experienced when subjects are also exposed to whole-body vibration. Exposure to whole-body vibration accelerates development of discomfort. The relationship between the reported discomfort, the vibration magnitude, and the exposure time can be described using a multifactorial linear model. It is concluded that ignoring parts of the multi-factorial model (i.e., static, dynamic, or temporal factors) will compromise understanding of discomfort in context. [ABSTRACT FROM AUTHOR]

Active vibration isolation of automotive seats requires actuators that achieve millimeter-range displacements and forces on the order of 300 N. Recent developments in piezoceramic actuator technology provide a means for achieving these force and displacement levels in a compact device. This work demonstrates that prestressed, curved piezoceramic actuators achieve the force and displacement levels required for active isolation of automotive seats. An estimate of the force and displacement requirements are obtained from numerical simulations on a four-degree-of-freedom car and seat model that utilize representive road accelerations as inputs. An actuator that meets these specifications is designed using piezoceramic materials. Free displacement of 4.4 mm and blocked force greater than 300 N are measured. The actuator is integrated within a dead mass setup that simulates the isolation characteristics of an automotive seat. Control experiments demonstrate that active vibration is achievable with realistic road disturbances. Feedback control is able to eliminate any amplification due to mechanical resonance and reduce the isolation frequency from 9.5 Hz to 2 Hz. [ABSTRACT FROM AUTHOR]