Door Trim Research Articles

A study on classifying BSR noise in automotive door trim using a deep learning method

In this paper, we demonstrated a deep learning model that can automatically classifies BSR(Buzz, Squeak, Rattle) noise in automotive door trim using convolutional neural networks. In the preprocessing process, sound features of door trim noises are extracted by using STFT(Short-time Fourier Transform) algorithm to obtain spectrogram images for each noises we obtained from experiment. To classify various noises of door trim, those image data are labeled with its root cause, then it is trained with deep convolutional neural network. Furthermore, t-SNE (t-Stochastic Neighbor Embedding) algorithm was used to visualize the effect of deep convolutional neural network we demonstrated. The classifier of the demonstrated method achieved with the accuracy of 96%.

Perancangan dan Pembuatan Sistem Power Window pada Mobil Daihatsu Zebra Tahun 1995

Power window system is a series of electrical body system that functions to facilitate the driver when opening and closing the glass door electrically by using a power window switch mounted on the inside side of the door, where the manual power window system driver must rotate the glass opening lever to raise and lower the door glass. The power window motor rotates when the power window switch is pressed. The power window motor rotation will turn up and down through the door glass regulator to open or close the door glass.
 The power window application process includes preparation of component placement design and preparation of materials to be used such as motor power windows, relays, fuses and switches, after all is ready, then the installation of components such as motor power windows to the regulator which has been modified in the form of additional plates for the holder The motor, after the power window regulator is ready, then assembles the electrical system and the installation of connections or sockets to each component. After the regulator and the electrical system are ready then install the switch holder on the door trim that has been prepared.
 The results of the power window application obtained power window can work in accordance with its function using a switch, namely the glass door can move up and down according to the position of the switch button pressed on each switch and from the results of the measurement process that the average voltage requirement for the power window to the position goes up 11.5 V while the position goes down 12V. The average time needed to raise the glass on the door 3.63 seconds and to lower the glass door takes 2.78 seconds

Exergy Assessment of Plastic Car Parts

Light-duty vehicles are increasingly incorporating plastic materials to reduce production costs and achieve lightweight designs. On average, a conventional car utilizes over 200 kg of plastic, comprising more than 23 different types, which often present challenges for recycling due to their incompatibility. Consequently, the focus on plastic recycling in end-of-life vehicles has intensified. This study aims to analyze critical car parts based on the plastics used, employing a novel thermodynamic approach that examines the embodied exergy (EE) of different plastics. Six vehicles from various segments, years, and equipment levels were assessed to understand their plastic compositions. The findings reveal that, on average, a vehicle contains 222 kg of plastic, accounting for 17.7% of its total weight. Among these plastics, 47.5% (105 kg) are utilized in car parts weighing over 1 kg, with plastics comprising over 80% of the part’s weight. The identified critical car parts include the front door trim panel, front and rear covers, fuel tank, floor covering, front lighting, dashboard, rear door trim panel, plastic front end, backrest pad, door trim panel pocket, plastic foam rear seat, rear lighting, window guide, molded headliner, bulkhead sound insulation, foam seat part, and wheel trim. Regarding their contribution to EE, the plastics with the highest shares are polypropylene—PP (24.5%), polypropylene and ethylene blends—E/P (20.3%), and polyurethane- PU (15.3%). Understanding the criticality of these car parts and their associated plastics enables targeted efforts in design, material selection, and end-of-life management to enhance recycling and promote circularity within the automotive industry.

Application Research of Low-density Polypropylene Materials on Door Trim Panels

Lightweight design is an important indicator in the process of automobile development, including product structure design, using plastic instead of steel and low density materials and other technical solution, this paper studies low density material technology to achieve lightweight automotive parts. The material system of polypropylene (PP)/elastomer/talc blends(PP+EPDM-TD)was selected to study the combination of different types of PP (homopolymer PP and random PP) and talc powder (mesh number and content) in the material formula. With the goal of the performance of the door panel parts, PP materials with 5% (PP+EPDM-TD5), 10% (PP+EPDM-TD10) and 20% (PP+EPDM-TD20) talc content were developed to verify the material properties. 10% and 20% talc content can meet the requirements. Then, the two materials were comprehensively evaluated from the aspects of parts forming analysis, size verification and application cost, and the low-density material (PP+EPDM-TD10) was finally selected for application to meet the lightweight design requirements of parts.

Application of YOLO and ResNet in Heat Staking Process Inspection

In the automobile manufacturing industry, inspecting the quality of heat staking points in a door trim involves significant labor, leading to human errors and increased costs. Artificial intelligence has provided the industry some aid, and studies have explored using deep learning models for object detection and image classification. However, their application to the heat staking process has been limited. This study applied an object detection algorithm, the You Only Look Once (YOLO) framework, and a classification algorithm, residual network (ResNet), to a real heat staking process image dataset. The study leverages the advantages of YOLO models and ResNet to increase the overall efficiency and accuracy of detecting heat staking points from door trim images and classify whether the detected heat staking points are defected or not. The proposed model achieved high accuracy in both object detection (mAP of 95.1%) and classification (F1-score of 98%). These results show that the developed deep learning models can be applied to the real-time inspection of the heat staking process. The models can increase productivity and quality while decreasing human labor cost, ultimately improving a firm’s competitiveness.

Optimization for squeak and rattle reduction using vibration analysis of passenger car door trim

The quality of vehicle plays an important role during the vehicle purchase. Sound quality is one of the highly ranked and commonly identified issues in automobiles. Automotive OEMs want the right-first-time design approach thus designs should be strong enough to fulfill all the criteria in terms of safety, fatigue, quality, etc. Buzz, Squeak and Rattle (BSR) are commonly classified as vehicle quality issues and marked under NVH. It is very hard to resolve BSR due to complex mechanisms and assemblies in vehicles. In this paper, the methodology is developed to reduce Rattle issues with help of shape optimization. BSR is quite unpredictable so it is hard to completely avoid it during development of overall vehicle, hence during the test run noise and its locations are marked and the focus is shifted to solving them. The methodology presented is helpful to solve the problem during the design and development phase instead of find and cure method which is currently in use. The work is limited to reducing the rattle issue in door trim and audio speaker assembly.

Design of Self-Healing EPDM/Ionomer Thermoplastic Vulcanizates by Ionic Cross-Links for Automotive Application.

The development of smart elastomeric materials with inherent self-repairing abilities after mechanical damage has important technological and scientific implications, particularly in regard to the durability and life cycle of rubber products. The interest in self-healing materials for automotive applications is rapidly growing along with the increasing importance of vehicle scratch quality and quantity. The creation of a reversible network by noncovalent ionic cross-linking in elastomer/rubber blends is an effective approach to generate the self-healing phenomenon, with reprocessing and recycling properties. In this work, thermoplastic vulcanizates (TPVs) were prepared using ethylene–propylene–diene (EPDM) polymers and high-acid-containing thermoplastic ionomers. Along with the general EPDM, maleic anhydride grafted EPDM (EPDM-g-MAH) was also used for the preparation of the TPVs. The strategy was based on a simple ionic crosslinking reaction between the carboxyl groups present in the ionomer and zinc oxide (ZnO), where the formation of reversible Zn2+ salt bondings exhibits the self-healing behavior. The heterogeneous blending of EPDM and ionomers was also used to investigate the thermal and mechanical properties of the TPVs. The experimental findings were further supported by the surface morphology of the fracture surfaces viewed using microscopy. The self-healing behavior of the TPVs has been identified by scratch resistance testing, where the EPDM-g-MAH TPVs showed excellent healing efficiency of the scratch surface. Therefore, this work provides an efficient approach to fabricate new ionically cross-linked thermoplastic vulcanizates with excellent mechanical and self-repairing properties for the skins of automotive interior door trims and instrument panel applications.

Sustainable Fabrication Technology of Composite Board by Kenaf-Polypropylene for Automobile Door Interior Applications

Polypropylene resins have been enfolded with the automotive industry and suppliers to produce several spare parts. This is aimed at achieving zero emissions, kenaf plant which in Latin is Hibiscus Cannabinus is a natural fiber replacement resin. Natural fiber composites come in many different types, but kenaf has been exploited extensively over the last few years. The pre-board flow process of kenaf-polypropylene starts from mixing kenaf about 40% with 60% polypropylene, forming a pre-mat as the first output, entering the main treatment with the hot press into pre-board as the final output. Kenaf-polypropylene door trim is very absorbent of CO2, which is related to natural fiber base material. Door trim with kenaf-polypropylene as the base material reduces the weight by about 30% of the previous polypropylene resin and still provides high rigidity even at a reduced weight. The entire process is requiring 48382.4 kWh / month per cycle of total power consumption.

가변유연조립 스마트공장을 위한 가변형 지그

With the advent of the 4th industrial revolution, manufacturing companies are facing unpredictable market changes such as sharp fluctuations in product demand and frequent launches of new products. In order to respond to this changes, order-based manufacturing companies are striving to make the manufacturing system reconfigurable and flexible. Traditional manufacturing systems are required to replace main equipment and reorganize production lines when a new product is to be released, and it incurs cost and time to produce the dedicated tools for new products. Therefore, it is necessary to fabricate a flexible jig tool that can be quickly reconfigured to cope with various products in different shapes. In this paper, as a part of constructing a reconfigurable and flexible assembly of smart factory, the flexible jig which can be applied universally to the production of various vehicle door trims in different shapes is proposed. Also, on a trial basis, it is applied to the reconfigurable and flexible assembly lines of smart factory for verifying the responsiveness of the reconfigurable jig tools to product changes.

Assembly Part Positioning on Transformable Pin Array Fixture by Active Pin Maximization and Joining Point Alignment

Designing and installing jigs and fixtures for new products are very costly tasks. In order to rapidly respond to frequent product changes in a manufacturing system, we developed a transformable pin array fixture system including reconfiguration planning software that finds the optimal position for mounting an assembly part and automatically generates a pin height control code. We first classified main fixturing quality factors such as type of contact, distance between a pin and a joining point, and part shape. We then formulate the part positioning problem as an alignment problem between the joining point set and the pin point set in the xy plane in consideration of the fixturing quality factors. A car door trim assembly module was used to verify the proposed method. Finite-element analysis results showed that in a car door trim assembly process such as ultrasonic welding and screwing, relocating an assembly part at the optimal position reduces its deformation.

In-Process Noise Inspection System for Product Fault Detection in a Loud Shop-Floor Environment

Abnormal noise originating from within faulty products often irritates customers, which may lead to expensive warranty claims. Therefore, it is important to identify these faulty products proactively in the manufacturing process. However, noise detection in a loud shop-floor is not straightforward because inspection in an anechoic chamber is very costly, and some prerequisites for conventional noise reduction and source separation methods, such as stationary and independent signals and prior knowledge about the signal of interest, are sometimes not feasible in practice. Therefore, we developed an in-process noise inspection system that supports dual-channel acoustic data collection during the inspection process. By using two different groups of acoustic signals, abnormal sound separation and noise detection are made possible through three main steps: in-process background noise training, abnormal noise separation, and significance evaluation. The efficiency of the proposed procedure is demonstrated with two case studies: car door trim panels and dual-channel sound generator and collector.

Squeak Noise Prediction of a Door Trim Panel Using Harmonic Balance Method

<div class="section abstract"><div class="htmlview paragraph">Squeak and rattle noise in a vehicle’s interior is perceived as an annoying sound by customers. Since persistent noise (e.g. engine, wind or drive train noise) has been reduced continuously during the last decades, the elimination of sounds, which have their origin in the vehicle’s interior components, is getting more important. Therefore, noise prediction based on simulation models is useful, since design changes can be realized at lower costs in early virtual development phases. For this task, linear simulation methods are state of the art for the identification of noise risk, but in general without knowing if a sound is audible or not. First approaches have been developed based on the Harmonic Balance Method to predict squeak noise and assess their audibility. This paper presents vibroacoustic measurements at a door trim panel for squeaking and non-squeaking configurations. Vibrations are excited harmonically by a force controlled low noise shaker. The system response is measured in a semi-anechoic chamber by acceleration sensors and audibility is assessed. Additionally, a 3D finite element model is built and the Harmonic Balance Method using a dry friction law is applied to predict the acoustic behavior. Finally, the simulation results are compared to the measurements. A good agreement between simulation and experiment can be observed.</div></div>

Data-driven smart control of injection molding process

The most widely used cyclic manufacturing process to produce plastic components, injection molding produces large quantities of scrap materials and consumes resources to mitigate the increasing demands for quality products and sophisticated shapes. Such inefficiencies require a smart quality-control system that can bring consistency in the product quality. Continuous monitoring and control of the injection molding are of excessive importance in providing a better understanding of the occurrence of the failures and their correction measures. In the presented paper, a cyber engineering analysis model is developed to illustrate the real injection molding behavior, and multiple experiments are performed to derive the process window for the product with very good quality. To detect the process disturbances, a control boundary with threshold limits of cavity temperature and pressure is established. If-then rule based control algorithm is derived to detect the failures and make the compensatory adjustments to the process parameters. Finally, the proposed method is illustrated with the manufacturing of an automotive product i.e. car door trim module, and its validity is verified. Study results after the introduction of the developed quality control methodology indicate the dramatic reduction in the quality failure rates and the wastages of resources.

Pemilihan Pemasok Dengan Mempertimbangkan Produksi Bersih/Green Manufacturing Pada Industri Otomotif

This study aims to select the best suppliers based on Fuzzy-AHP and ELECTRE methods in multinational automotive companies in Indonesia. Fuzzy AHP method is used to determine the weight of criteria and sub-criteria in reducing uncertainty and unclear information. Meanwhile, the ELECTRE method is used to rank alternative suppliers. Both methods are used to reduce subjective decisions and improve the accuracy of selected suppliers. After applying Fuzzy AHP and ELECTRE methods, the supplier chosen for the steel fuel tank is S1, for side beam impact is S2, wheel disc is S2, Seat is S1, door trim is S2, lamp is S3, headlining is S3, insulator is S1 and hose is S3.Keywords: JIT, supplier performance, Fuzzy-AHP, ELECTRE

An in-Process Inspection System to Detect Noise Originating from within the Interior Trim Panels of Car Doors.

Car body parts are sometimes responsible for irritating noise caused by assembly defects. Typically, various types of noise are known to originate from within the interior trim panels of car doors. This noise is considered to be an important factor that degrades the emotional satisfaction of the driver of the car. This research suggests an in-process inspection system consisting of an inspection workstation and a noise detection method. The inspection workstation presses down the car door trim panel by using a pneumatic pusher while microphones record the acoustic signals directly above the door trim panel and on the four sides of the workstation. The collected signals are analyzed by the proposed noise detection method after applying noise reduction. The noise detection method determines the presence of irritating noise by using noise source localization in combination with the time difference of arrival method and the relative signal strengths. The performance of the in-process noise detection system was evaluated by conducting experiments on faulty and healthy car door trim panels.

Promising processing technologies of glass-filled polyurethane materials

The review of modern methods of glass-filled polyurethane materials processing is presented. A possibility to use of glass-filled PU in the manufacture of interior parts with different surface textures (instrument panels, door trim, armrests) has been revealed.

Development and Characterization of Sisal Fiber Reinforced Polypropylene Composite Materials

In most of the developing countries, plastic polypropylene is not fully recycled and converted in-to use after it is once used. Sisal fiber is also widely available in different developing countries like Ethiopia. Adding this two materials and developing automotive interior part was taken as a primary motive for it reduces cost and is environmentally friendly. Thus, the main purpose of this research is to develop composite material from natural fibre (sisal fiber) reinforced with recycled plastic waste (polypropylene) for interior automobile accessories specifically for internal door trim panel application. This research examines effect of fiber length, fiber loading and chemical treatment of fiber on the physical and chemical properties of the sisal fiber reinforced polypropylene (SFRPP) composite material. The waste polypropylene and the treated and untreated sisal fiber with variable length and weight ratio (fiber/matrix ratio) were mixed. Flammability of sisal fiber reinforced Polypropylene (SFRPP) composites material was examined by a horizontal burning test according to ASTM D635 and chemical resistance of the sisal fibre reinforced PP composites was studied using ASTM D543 testing method. The result on the flammability test shows that treated fiber has lower burning rate than untreated fiber and decreases with increase in fiber length and fiber loading. The resistance of the composites to water has increased as the fiber length increases and decreased as the fiber loading increase. Generally, SFRPP composite is found to have better resistance to water than NaOH and H2SO4 and treating the fiber has brought considerable improvement on chemical resistance of the composite. Fiber loading and fiber length has positive and negative effect on the flammability of the SFRPP composite respectively.

PEMANFAATAN LIMBAH SERAT KAPAS DARI INDUSTRI PEMINTALAN UNTUK FELT DAN PAPAN SERAT

Cotton is widely used for clothing material, because it is able to absorb sweat/water, so comfortable to wear. But the product is still an export comodity and waste that occurs after spinning into yarn is about (4 - 5)%. Much of the waste still contains short fibers, potentially as composite reinforcement; and the rest are dirt, dust, soil and others. Therefore, in the framework of handling and utilization of such waste, in order to produce an added value product, then a research was conducted to make biocomposites using that waste. The purpose is to get a biocomposite with a cotton fiber waste of felt (a nonwoven product) with a low melt polyester matrix and manufactured wood in the form of a fiber board with an epoxy resin matrix that can meet applicable standards. From the test results, it is known that the felt can absorb sound up to 78% at the reference frequency of 5000 Hz (meets minimum standards for sound absorbing coefficient, based on ISO 11654: 1997), so it can be used as a sound absorber material (for building/automotive) and can also be used as raw material for automotive textile (door trim, dashboard or head lining). In addition, the fiber board also has performance that meets the standard (SNI 01-4449-2006) and can reflect sound ≥ 90%, so it can be used as a sound reflector interior panel

Study on durability test method for improvement of door trim grip handle friction noise

Study on durability test method for improvement of door trim grip handle friction noise

Defect state and severity analysis using discretized state vectors

The time series of sensor data for condition monitoring of a system is often characterized as very-short, intermittent, transient, highly nonlinear and non-stationary random signals, which hinder the straightforward pattern analysis. In order to identify meaningful features in measured sensor data, we transform the continuous time series into a set of contiguous discretized state vectors using a multivariate discretization approach. We then search for important patterns that are only found in defective systems. We discuss how to measure the severity degree of each defect pattern and assess the criticality of a defective state. We consider a defective state to be more severe if various defect patterns are observed in the state. Similarly, if a particular defect pattern describes multiple defect states, the pattern is treated as significant. The proposed procedure is utilized to detect defective car door trims that generate small but irritating noises. We analyzed the datasets obtained from a typical acoustic sensor array and acoustic emission sensors. The defective door trims were efficiently identified including the severity degrees of the identified patterns.