Virtual technology in tyre development

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  • June 25, 2020
Virtual technology in tyre development

In that respect, tyre industry should continue to improve New Product Development (NPD) process to a different level by expanding R&D efforts. Consequently, innovative tyre technology and tyre knowledge will be extremely important to compete in the future, more than at any time in the past. As we all know, there are several key processes to design and to produce the right tyres to meet the customer requirements.

In the new tyre development process, duration is getting more crucial and all manufacturers are trying to shorten it by using modern simulation and modeling technics. TIC - Tyre Industry Consulting - and AccuPredict LLC believe in “Speed to market with right solutions and innovation.” In that respect, Virtual Technologies enable companies to launch world-class product faster and more cost effectively than ever. Therefore, we recommend using the Virtual technology which is essential to understand how all the various parts of the tyre interact and add up to whole. With modeling and simulation, you will foresee the full effects of cascading events as well as novel events that our mental models cannot even imagine.   

Tyre, as a component of a vehicle, can largely influence the performance of the vehicle. On the other hand, to design tyres with high quality and high-performance, not only the characteristics of tyres such as tread pattern, tyre structure, material, local stress and thermal properties are also important, but the running conditions such as the load of a vehicle, the road roughness, temperature changes are equally critical. Considering the rapid advancement in electric vehicle, tyre will be the major source of noise for the modern vehicles. European regulation on tyre pass-by (PB) noise has been restricted to be less than 72 dB (A) and set the future noise level at a limit of 68 dB(A) after 2025 (phase 3). Tyre models based on virtual technology provide a comprehensive evaluation of key aspects of tyres. To ensure that the tyres are designed with high quality and good durability, and also in compliance with regulation, simulating tyres and predicting the tyre performance under various conditions can largely improve the tyre develop efficiency. The following tyre simulation and modeling are a few examples to showcase the vast potential of the tyre virtual technology.

  • Components of tyre simulation - creation of rim, carcass, ply, and belts, reinforcement modeling, tread pattern meshing method, element types, etc.
  • Footprint and force/moment prediction – Quasi-static footprint prediction with normal force, lateral force, torsion, and camber; steady-state rolling with brake and acceleration, cornering, and camber, etc.
  • Integration of tyre structure and tyre Noise, Vibration and Harshness (NVH) – pitch sequencing, tyre profile and structure, tread blocks, void area, non-skid depth, angle of groove, shape of footprint, local stress distribution and tyre uniformity can be investigated in combination with tyre impact noise, air-pumping noise and cavity noise to obtain the tyre radiation noise model for tyre design optimisation.
  • Rolling resistance – Deformation analysis, radial stiffness (R deflection) prediction, tread pattern and side wall stiffness may be included to a finite element model for specific tyre designs and then simulate the energy loss under different tyre running conditions to obtain the optimal rolling resistance level.
  • Thermal stress and fatigue analysis based on fluid dynamics - fluid-solid coupling simulation, creation and evaluation of detailed tread design, and dynamic meshes for tyre structure and rotating machinery fields will collectively provide precise understanding of the thermal distribution of given tyres. In particular, the analysis can predict the thermal stress of tyre molds accurately.

We do recommend applying Virtual analysis in design and development phases regarding; sizing, carcass line design, pattern design, footprint, tyre structure, materials, running conditions, durability, rolling resistance, noise and others.

TIC-Tyre Industry Consulting and AccuPredict LLC Subject Matter Experts (SME) have vast hands on experiences for above topics and ready to support your activities. We provide specialised technical solutions for your challenges and we guarantee a high standard of professional-ethical principles that we have kept and developed for years.

In addition to those services, we also provide Simulation and Modeling Technical Courses that design to give your workforce the skills, mind set and competences. Our trainers guide participants through a learning journey featuring workshops, case studies and the latest educational technologies.

 

 

Haluk Kizilay has built over 30 years an impressive career that spans everything from tyre design & development to strategic planning, marketing and business development with global leaders including Bridgestone Turkey and Cooper Tire & Rubber. In 2019, he established TIC (Tire Industry Consulting). Haluk is one of the authorised judges of EU Horizon 2020 work programme and one of the registered researchers of TUBITAK, the Scientific & Technological Research Council of Turkey. In this article he focuses how to enhance New Product Development (NPD) process.

Jun Han Ph.D. has more than 15 years of experience in solving mechanical problems related to structural sound and vibration, tyre NVH, Computational Fluid Dynamics (CFD), thermal, and numerical modelling. His exceptional modeling work won him the Outstanding Contribution to Innovation Award in 2019. He was a Senior Engineer at Cooper Tire & Rubber Company (OH) and has served as the core technical support for Cooper Tire’s Global R&D centers in the US, China and Europe. His work in reducing total tyre pass-by noise won him the prestigious Chairman’s Award at Copper in 2016. In 2020, Jun Han established his own technology company AccuPredict LLC

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    Hankook Tire introduces Design Innovation 2020 project

    Hankook Tire introduces Design Innovation 2020 project

    Hankook Tire revealed the Design Innovation 2020 project, which defines a vision for the future driving and innovation in mobility.

    Launched in 2012, the Design Innovation is Hankook’s R&D project held every two years, in collaboration with one of the world’s leading design universities.

    Under the theme ‘Urban Reshaping’, professors and students from the Department of Industrial Design at the University of Cincinnati in the U.S. focused on the transformation of cities geared by reconfiguring mobility as part of living spaces rather than stand-alone purpose in the future with augmented automation infrastructure and cutting-edge technologies such as eco-friendly technology, autonomous driving and Artificial Intelligence (AI).

    Throughout the project, modular platform of mobility concept named ‘Hankook Platform System (HPS)-Cell’ was proposed with tyre representing the root of mobility. It is applied with ‘Hankook Electric Mobility Technology (H.E.M.)’ which represents Hankook’s passion for future technological breakthroughs. Then a scenario was created which distinguishes mobility as a moving platform and its function as a pod (space), clearly elaborating that tire indeed sits at the center of the mobility.

    The tyre of HPS-Cell embodied an airless tyres’ double-layered unit-cell structure to acquire complex rigidity. It is a concept tyre that uses sensor technology to not only identify tire treads and road conditions in real time, but also to respond to wear-out risks and change tread patterns according to the road condition utilizing variable wheels and optimized infrastructure.

    The scenario was brought into reality in a concept film and a mock-up. The productions suggest that in 2040 urban population will be able to use this mobility platform combined with pods of various forms to each meet a specific purpose. The modular platform can also be combined with commercial pods such as urban farming to maximize the scalability and efficiency of movement within smart cities of future generation.

    The unveiled productions will be exhibited at various global channels and will represent Hankook’s capabilities in design innovation globally.

    Jimmy Kwon, Vice President of Hankook Tire Brand Lab said, “Hankook Tire is incorporating new ideas with our cutting-edge technology to explore design concepts for the next generation, as Hankook believes creativity is the first step towards bringing the imagination into the reality. We are more than excited to present this year’s works as they speak for the essence of the future mobility that Hankook envisions.”

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      TATNEFT Develops New AVT Tyre Line

      TATNEFT Develops New AVT Tyre Line

      TATNEFT has announced the development of a new line of ATV tyres called the KAMA Quadro ATM. The first model has been made in 25x8-12 standard size at its Nizhnekamskshina factory in Russia.

      The ATV tyre, which is developed by Kama Scientific and Technical Center, has been specially designed for off-road driving, providing excellent cross-country ability in mud and snow. The tyre’s special rubber composition ensures high reliability and traction performance.

      The first batch of tyres will go for pilot testing to TATNEFT subdivisions that operate off-road special vehicles.

      The KAMA Quadro ATM range is currently being developed in nine tyre sizes covering 12 to 14 inches diameter, with nine more sizes coming up over the next year. The factory will begin production of 25x10 tyres for the rear axle in addition to the already manufactured  25x8 tyres intended for the front axle.

      The KAMA Quadro ATM will meet the needs of the TATNEFT Group’s all-terrain vehicles used in oil fields and will also be used to equip Russian ATV manufacturers and the secondary market. (TT)

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        Kumho Tyre Aces Summer Tyre Test Over 52 Opponents

        Kumho Tyre Aces Summer Tyre Test Over 52 Opponents

        Kumho tyres have outperformed 52 rival manufacturers to ace the Auto Bild magazine’s summer tyre test with its ECSTA HS51 high-performance pattern tyre.

        The annual test is among the most comprehensive of its type, the results of which are regarded as highly significant by both the European tyre trade and its consumers.

        Conducted on both wet and dry surfaces, it left Kumho in a fighting third place overall. However, while the further qualifications caused the two leaders to slide down the order, 33 of the 53 entries were eliminated by the initial braking test. Kumho’s highly competitive and consistent scores in almost every discipline ultimately left it as the sole test winner.

         Awarding the ECSTA HS51 their coveted ‘Exemplary’ badge, the Auto Bild testers commended it for its precise steering response, secure wet grip, well-balanced handling, short braking distance, low wear rate and affordable price.

        Unlike some tyre tests, where the products are supplied by the manufacturers, those for the Auto Bild ones are covertly purchased by the magazine from regular retail outlets. The chosen size was 205/55R16, the direct fitment for the bulk of Volkswagen Golfs and Audi A3s etc., and therefore arguably the one most common within the European car market.

        UK purchasers currently have the choice of 35 sizes of ECSTA HS51 for wheels of 15 to 18 inches in diameter. The qualification round of the test was carried out at ATP (Automotive Testing Papenburg) in Germany and the other tests were performed at the IDIADA facility in Spain. 

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          Tire Leap AI Analysis Technology: An Overview

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          • June 25, 2020
          Sumitomo Rubber Becomes OE Tyre Supplier for Toyota All-new Alphard and Vellfire

          Thus, our newly developed "Tire Leap AI Analysis" utilises advanced AI-based analysis technology to analyse (for example) electron microscope imagery of tyre rubber compounds in order to achieve high-precision analysis that far exceeds human capabilities, thereby making it possible to derive accurate estimates of rubber properties from structural data found in this imagery.

           

          Specifically, it is a technology that estimates rubber properties precise from combining data on the individual raw materials contained in a rubber compound with data on its internal structure. In the future, we will continue to develop this technology and develop technology to estimate the future rubber properties from electron microscope imagery of unused rubber.

          ■ Technology to Precisely Estimate Rubber Properties Based on Structures & Materials

           

          Tire Leap AI Analysis utilises an AI-based image analysis system to analyse the internal structures of rubber in images captured by an electron microscope in order to infer information about the properties of the rubber based on its structural data (i.e. the results of image analysis). By combining this structural data with data about the materials that make up rubber compounds, this technology is then able to derive information about the physical properties of rubber with a high degree of precision.

          ■ Technology to Detect Changes in the Internal Structures of Rubber After Use & Estimate Resulting Changes in Rubber Properties

          By comparing images of a tyre that has never been used (i.e. that is brand new) with images of a tyre that has been used (i.e. after wear over time), this AI-based image analysis system can determine where changes have occurred in the internal structures of the tyre’s rubber and then estimate the physical properties of the rubber in the areas that have undergone these changes. The practical application of this technology will facilitate the design of new rubber compounds that are less prone to performance degradation due to wear and tear, thus contributing to the development and advancement of Performance Sustaining Technology.

          Dr. Miki Haseyama, Hokkaido University: We have developed a new AI technology that is able to estimate the extent of changes in the structures based on analysis of images of the internal structures of rubber. As compiling data for this kind of machine learning would otherwise be extremely time-consuming, one of the main merits of this new technology is the fact that this AI does not require prior field data from structural changes in rubber for machine learning. Rather, this AI uses deep learning to learn about the properties of new rubber (i.e. prior to undergoing structural changes) and then estimates the extent of changes in the structure by analysing how data from old rubber (i.e. after undergoing structural changes) compares to the data that it has previously learned about new data. This approach to machine learning allows the AI to automatically detect various types of changes in the structures of rubber.

          Kiyoshige Muraoka, Senior Executive Officer, Sumitomo Rubber Industries: We have been working jointly with Hokkaido University to further advance the development of AI technology that can understand how the internal structures of tyre rubber change through use. We have already put this new technology to use in the development of our latest “ENASAVE NEXT III” fuel-efficient tyres, which not only achieve the highest possible “AAA-a” rating for fuel efficiency and wet grip performance (under Japan’s tyre labelling system), but also reduce changes in tyre performance that occur over time as a result of use by half. Moving forward, we will continue to advance our Tire Leap AI Analysis technology to find and analyse slight variations in the internal structures of rubber that human senses and knowhow have been unable to detect so that we can then use the resulting knowledge to develop new technologies that further enhance tyre performance and ensure that this high performance lasts longer. In this way, we will accelerate research and development toward producing high-performance tyres that provide greater safety and peace of mind with the aim of contributing to the realisation of a sustainable mobility society for future generations.

           

          References:

          Ren Togo, Naoki Saito, Takahiro Ogawa, Miki Haseyama, “Estimating regions of deterioration in electron microscope images of rubber materials via a transfer learning-based anomaly detection model,” IEEE Access, vol. 7, pp. 162395-162404, 2019.

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