Horacio Pinzón, Cinthia Audivet, J. Alexander, M. J. Torres, M. Consuegra, M. Sanjuan
Fault detection and diagnosis schemes based on data-driven statistical modelling are highly dependent on an accurate and exhaustive feature extraction procedure to deliver a superior performance as a monitoring strategy. Otherwise conducted, a deficient feature extraction procedure leads to a monitoring structure widely deviated from normal operating conditions. If an operating state is not identified as it, an increment in false alarm rate would be evidenced whenever the process shifts towards that condition and the monitoring scheme triggers the abnormal condition warning. On the other hand, if two similar operating conditions could not be individualized i.e. to be identified as a single operating state, a lack of sensitivity for minor — yet typical — deviations would render a monitoring strategy with prominent misdetection rates. Although Multimode Operational Mapping requires the proper identification of a finite set of normal process states, it is a challenging task especially for large-scale systems. Its complexity derives from a broad universe of monitoring variables, highly interactuating process units integrated over very dynamic network systems, among others. This is the case of natural gas transmission infrastructure, as it deals with variable upstream production rates, diverse consumption trends from customers, internal processes constrains, merged in a stringent operating scheme. This paper proposes a novel strategy to address the identification and feature extraction of normal conditions on multimode operation systems. The proposed framework uses a segmentation approach based on operator’s knowledge, the Takagi-Sugeno-Kang fuzzy engine and k-means algorithm to characterize the normal operation states of the system. The results show an improvement in the performance of Principal Component Analysis during abnormal conditions detection, in addition an increase on the sensitivity of Hotelling and Q statistics.
{"title":"A Novel Hybrid Strategy for Multimode Operation Mapping and Feature Extraction on Data-Driven Statistical Fault Detection Methods","authors":"Horacio Pinzón, Cinthia Audivet, J. Alexander, M. J. Torres, M. Consuegra, M. Sanjuan","doi":"10.1115/IMECE2018-87417","DOIUrl":"https://doi.org/10.1115/IMECE2018-87417","url":null,"abstract":"Fault detection and diagnosis schemes based on data-driven statistical modelling are highly dependent on an accurate and exhaustive feature extraction procedure to deliver a superior performance as a monitoring strategy. Otherwise conducted, a deficient feature extraction procedure leads to a monitoring structure widely deviated from normal operating conditions. If an operating state is not identified as it, an increment in false alarm rate would be evidenced whenever the process shifts towards that condition and the monitoring scheme triggers the abnormal condition warning. On the other hand, if two similar operating conditions could not be individualized i.e. to be identified as a single operating state, a lack of sensitivity for minor — yet typical — deviations would render a monitoring strategy with prominent misdetection rates.\u0000 Although Multimode Operational Mapping requires the proper identification of a finite set of normal process states, it is a challenging task especially for large-scale systems. Its complexity derives from a broad universe of monitoring variables, highly interactuating process units integrated over very dynamic network systems, among others. This is the case of natural gas transmission infrastructure, as it deals with variable upstream production rates, diverse consumption trends from customers, internal processes constrains, merged in a stringent operating scheme.\u0000 This paper proposes a novel strategy to address the identification and feature extraction of normal conditions on multimode operation systems. The proposed framework uses a segmentation approach based on operator’s knowledge, the Takagi-Sugeno-Kang fuzzy engine and k-means algorithm to characterize the normal operation states of the system. The results show an improvement in the performance of Principal Component Analysis during abnormal conditions detection, in addition an increase on the sensitivity of Hotelling and Q statistics.","PeriodicalId":201128,"journal":{"name":"Volume 13: Design, Reliability, Safety, and Risk","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123955550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Currently, pressure vessels that operate in hydrogen service and subjected to fatigue must be designed using a defect tolerant design procedure. This means that first the fracture mechanics properties of the material being considered must be measured in hydrogen at the maximum service pressure. The properties are fatigue crack propagation properties and threshold stress intensity factor for hydrogen embrittlement (KIHE). With these properties, a fatigue crack propagation life can be estimated assuming an initial crack size and geometry and growing this defect to failure. The property measurements are costly and can only be performed at a few laboratories. Furthermore, the resulting lives are usually very short because of the assumed initial crack size. These things limit the application of this design method to lower cycle or static loading applications. This work introduces a cost-effective method of design and construction of pressure vessels for high cycle use in hydrogen service at pressures below 40,000 psi that eliminates the need for determining fracture mechanics properties in hydrogen environment. The method uses shrink fit construction of a liner inside a jacket. The method requires that when the pressure is applied, the magnitude of the resultant stress at the pressure boundary of the liner is more compressive than the magnitude of the applied pressure and the maximum allowed size of defect in the jacket at the interface between the jacket and the liner is such that when the cyclic stress is applied the resultant fatigue loading of that defect at that location to be less than the threshold value for growth of that defect.
{"title":"Method of Construction for High Cycle Fatigue Resistant Pressure Vessels in Hydrogen Service","authors":"Pooya Mahmoudian","doi":"10.1115/IMECE2018-86292","DOIUrl":"https://doi.org/10.1115/IMECE2018-86292","url":null,"abstract":"Currently, pressure vessels that operate in hydrogen service and subjected to fatigue must be designed using a defect tolerant design procedure. This means that first the fracture mechanics properties of the material being considered must be measured in hydrogen at the maximum service pressure. The properties are fatigue crack propagation properties and threshold stress intensity factor for hydrogen embrittlement (KIHE). With these properties, a fatigue crack propagation life can be estimated assuming an initial crack size and geometry and growing this defect to failure. The property measurements are costly and can only be performed at a few laboratories. Furthermore, the resulting lives are usually very short because of the assumed initial crack size. These things limit the application of this design method to lower cycle or static loading applications. This work introduces a cost-effective method of design and construction of pressure vessels for high cycle use in hydrogen service at pressures below 40,000 psi that eliminates the need for determining fracture mechanics properties in hydrogen environment. The method uses shrink fit construction of a liner inside a jacket. The method requires that when the pressure is applied, the magnitude of the resultant stress at the pressure boundary of the liner is more compressive than the magnitude of the applied pressure and the maximum allowed size of defect in the jacket at the interface between the jacket and the liner is such that when the cyclic stress is applied the resultant fatigue loading of that defect at that location to be less than the threshold value for growth of that defect.","PeriodicalId":201128,"journal":{"name":"Volume 13: Design, Reliability, Safety, and Risk","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116743105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Indeevar Shyam Lanka, Akhil Challa, Nithya Sridhar, S. Subramanian, S. Sankaralingam, Gunasekaran Vivekanandan
This work proposes a method to simulate wheel lock of a Heavy Commercial Road Vehicle (HCRV) using pneumatic brake circuit on a brake dynamometer. The proposed methodology lumps the effects of wheel slip and load transfer during straight-line braking into ‘equivalent inertia’ on the wheels. This inertia profile could then be imported on a dynamometer interface and realized using suitable inertia discs and an electric motor. Equivalent inertia was computed from test datasets obtained from a Hardware-in-Loop (HiL) experimental system consisting of an air brake system and IPG TruckMaker®, a vehicle dynamic simulation software. These datasets were obtained for various road, vehicle load and braking conditions. This framework would facilitate the evaluation of wheel slip regulation algorithms using a brake dynamometer by capturing necessary dynamics of HCRVs during braking. It is expected that such testing can be placed between HiL and on-road tests, and would provide greater confidence in Active Safety Systems (ASSs) before their deployment on vehicles.
{"title":"A Model Based Framework for Wheel Lock Simulation in a Brake Dynamometer Towards Heavy Road Vehicle Safety","authors":"Indeevar Shyam Lanka, Akhil Challa, Nithya Sridhar, S. Subramanian, S. Sankaralingam, Gunasekaran Vivekanandan","doi":"10.1115/IMECE2018-87230","DOIUrl":"https://doi.org/10.1115/IMECE2018-87230","url":null,"abstract":"This work proposes a method to simulate wheel lock of a Heavy Commercial Road Vehicle (HCRV) using pneumatic brake circuit on a brake dynamometer. The proposed methodology lumps the effects of wheel slip and load transfer during straight-line braking into ‘equivalent inertia’ on the wheels. This inertia profile could then be imported on a dynamometer interface and realized using suitable inertia discs and an electric motor. Equivalent inertia was computed from test datasets obtained from a Hardware-in-Loop (HiL) experimental system consisting of an air brake system and IPG TruckMaker®, a vehicle dynamic simulation software. These datasets were obtained for various road, vehicle load and braking conditions. This framework would facilitate the evaluation of wheel slip regulation algorithms using a brake dynamometer by capturing necessary dynamics of HCRVs during braking. It is expected that such testing can be placed between HiL and on-road tests, and would provide greater confidence in Active Safety Systems (ASSs) before their deployment on vehicles.","PeriodicalId":201128,"journal":{"name":"Volume 13: Design, Reliability, Safety, and Risk","volume":"17 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126088918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In oil and gas industry, machineries and mechanical components are designed with high reliability to meet the demand of the oil field. Rotating machinery is a widely used equipment and any failure of critical components within the machinery could lead to delays and large expenses. Failure of rotary seal is one of the foremost causes of breakdown in rotary machinery and such a failure can affect the other process operations in oil and gas plants. Assessing seal degradation and severity estimation are very important for maintenance decision-making. Extracting meaningful and sensitive features that can show seal degradation from raw signals is a challenging task of degradation assessment. However, no extensive works are dedicated in this area of seals. In this paper, we perform accelerated aging and testing to capture the behavior of seals through their cycle of operation and demonstrated a statistical time domain feature based approach for extracting the sensitive features that can show seal degradation. Out of eleven statistical features extracted, seven extracted features such as mean, RMS, maximum, squared mean rooted absolute amplitude, impulse factor, crest factor, margin factor are found to be significant factors which have a potential to differentiate severity levels in seals. The findings from our work show that our approach has a potential to assess the severity in seals. As a possible extension, extracted features can be used to build a classification model to classify severity in seals which could be of great interest to the users and manufacturers of rotary seals.
{"title":"Statistical Time Domain Feature Based Approach to Assess the Performance Degradation of Rotary Seals","authors":"M. Ramachandran, Z. Siddique","doi":"10.1115/IMECE2018-87857","DOIUrl":"https://doi.org/10.1115/IMECE2018-87857","url":null,"abstract":"In oil and gas industry, machineries and mechanical components are designed with high reliability to meet the demand of the oil field. Rotating machinery is a widely used equipment and any failure of critical components within the machinery could lead to delays and large expenses. Failure of rotary seal is one of the foremost causes of breakdown in rotary machinery and such a failure can affect the other process operations in oil and gas plants. Assessing seal degradation and severity estimation are very important for maintenance decision-making. Extracting meaningful and sensitive features that can show seal degradation from raw signals is a challenging task of degradation assessment. However, no extensive works are dedicated in this area of seals. In this paper, we perform accelerated aging and testing to capture the behavior of seals through their cycle of operation and demonstrated a statistical time domain feature based approach for extracting the sensitive features that can show seal degradation. Out of eleven statistical features extracted, seven extracted features such as mean, RMS, maximum, squared mean rooted absolute amplitude, impulse factor, crest factor, margin factor are found to be significant factors which have a potential to differentiate severity levels in seals. The findings from our work show that our approach has a potential to assess the severity in seals. As a possible extension, extracted features can be used to build a classification model to classify severity in seals which could be of great interest to the users and manufacturers of rotary seals.","PeriodicalId":201128,"journal":{"name":"Volume 13: Design, Reliability, Safety, and Risk","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127905881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Current trends in product development process highlight the increasing adoption of digital data and virtual processes. Nowadays, a huge amount of product data are collected without a clear management strategy and, oftentimes, they dont even cover the whole product development process. A global and integrated planning about information needed to sustain product design process is not a trivial task and, usually, companies underrates this issue. From the perspective of virtualization of processes, and then their automation, the lack of structured knowledge is certainly awful. This paper aims at making a critical analysis how product data evolve throughout the product design or configuration process and how they impact the product development activities. Efficient digital product twin allows companies to virtualize processes and leverage their automation, but it is important to understand how the knowledge management should be carried out. Three case studies, directly experienced by the authors, have been investigated analyzing digital data and virtual tools that allow companies to automate the design process, each one bringing a peculiar perspective of the problem.
{"title":"From Customer Requirements to Detailed Design: How Do Product Data Change?","authors":"M. Rossoni, G. Colombo, Luca Bergonzi","doi":"10.1115/IMECE2018-87900","DOIUrl":"https://doi.org/10.1115/IMECE2018-87900","url":null,"abstract":"Current trends in product development process highlight the increasing adoption of digital data and virtual processes. Nowadays, a huge amount of product data are collected without a clear management strategy and, oftentimes, they dont even cover the whole product development process. A global and integrated planning about information needed to sustain product design process is not a trivial task and, usually, companies underrates this issue. From the perspective of virtualization of processes, and then their automation, the lack of structured knowledge is certainly awful. This paper aims at making a critical analysis how product data evolve throughout the product design or configuration process and how they impact the product development activities. Efficient digital product twin allows companies to virtualize processes and leverage their automation, but it is important to understand how the knowledge management should be carried out. Three case studies, directly experienced by the authors, have been investigated analyzing digital data and virtual tools that allow companies to automate the design process, each one bringing a peculiar perspective of the problem.","PeriodicalId":201128,"journal":{"name":"Volume 13: Design, Reliability, Safety, and Risk","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129944539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wafa Batayneh, Khaled S. Hatamleh, Amjad Nusayr, Rama Alquraan, Aseel Al-Khaleel, Ahmad S. Batainah
This paper presents the design and implementation of a low-cost and reliable wireless motion control system for conventional electric wheelchairs. The presented work aims to enhance the mobility of handicapped and elderly wheelchair users by utilizing a mobile application to control the motion of their unattained wheelchairs. The designed system takes into consideration cost, weight, a range of operation, ease of use, and implementation. The conventional electric wheelchair is equipped with a motorized front wheel steering mechanism. In addition, it is equipped with a Wi-Fi module to support remote motion control via a specially designed Android mobile application called “Android Application For NavigAtioN”; AAFNAN for short. Experimental testing of the prototype showed successful remote motion control and ease of use.
本文介绍了一种低成本、可靠的传统电动轮椅无线运动控制系统的设计与实现。这项工作旨在通过使用移动应用程序来控制残疾人和老年轮椅使用者无法获得的轮椅的运动，从而提高他们的行动能力。所设计的系统考虑了成本、重量、操作范围、易用性和实现。传统的电动轮椅配有电动前轮转向机构。此外，它还配备了Wi-Fi模块，支持通过专门设计的Android移动应用程序“Android application For NavigAtioN”进行远程运动控制;简称AAFNAN。样机的实验测试表明，远程运动控制成功，使用方便。
{"title":"Low-Cost Wi-Fi Navigation of Smart Wheelchairs","authors":"Wafa Batayneh, Khaled S. Hatamleh, Amjad Nusayr, Rama Alquraan, Aseel Al-Khaleel, Ahmad S. Batainah","doi":"10.1115/IMECE2018-86277","DOIUrl":"https://doi.org/10.1115/IMECE2018-86277","url":null,"abstract":"This paper presents the design and implementation of a low-cost and reliable wireless motion control system for conventional electric wheelchairs. The presented work aims to enhance the mobility of handicapped and elderly wheelchair users by utilizing a mobile application to control the motion of their unattained wheelchairs. The designed system takes into consideration cost, weight, a range of operation, ease of use, and implementation. The conventional electric wheelchair is equipped with a motorized front wheel steering mechanism. In addition, it is equipped with a Wi-Fi module to support remote motion control via a specially designed Android mobile application called “Android Application For NavigAtioN”; AAFNAN for short. Experimental testing of the prototype showed successful remote motion control and ease of use.","PeriodicalId":201128,"journal":{"name":"Volume 13: Design, Reliability, Safety, and Risk","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131120740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Daijun Hu, Yingchun Shan, Xiandong Liu, Weihao Chai, Wang Xiaoyin
The use of automobile lightweight is an effective measure to reduce energy consumption and vehicle emissions. The utilization of high-performance composite materials is an important way to achieve lightweight vehicles technically. The advantages of using thermoplastic composite wheels are: easy to form, high manufacturing efficiency, low cost and easy to recycle. This leads to broader application prospects. Taking composite anisotropy into consideration, the mechanical performance of a wheel made of long glass fiber reinforced thermoplastic (LGFT), is analysed using the finite element method (FEM). This is done by placing the wheel under a bending fatigue load simulation. According to the simulation results, the sample database is established by orthogonal experimental method on the Isight platform, and the approximate model is established by the Response Surface Methodology (RSM). Based on this model, uncertainty optimization analysis is then conducted on the wheel’s design using Sigma Principle whereby the optimization target is the mass minimization. The maximum deformation of the wheel and the stress on both sides of the spoke will serve as constraint conditions and the key dimension parameters of the wheel model will be taken as the design variables. The uncertainty optimization is based on the Sigma criterion, taking into consideration the wheel’s geometry and property-fluctuation materials. The feasibility of design schemes is then verified after comparison analysis between the optimization results and the simulation results obtained. The result shows that compared with deterministic optimization, though the weight of the wheel has slightly increased, the uncertainty optimization based on the Sigma criterion is much more robust and the reliabilities of the three constraints are all above 6 Sigma. The resulting optimized LGFT wheel weighs 5.28kg, which has a 5.5% more loss in weight than the initial target and is also 25.6% lighter than the counterpart wheel which is made of aluminum alloy. The desired design results is now achieved with this lightweight effect.
{"title":"Uncertainty Optimization Design of Vehicle Wheel Made of Long Glass Fiber Reinforced Thermoplastic","authors":"Daijun Hu, Yingchun Shan, Xiandong Liu, Weihao Chai, Wang Xiaoyin","doi":"10.1115/IMECE2018-86769","DOIUrl":"https://doi.org/10.1115/IMECE2018-86769","url":null,"abstract":"The use of automobile lightweight is an effective measure to reduce energy consumption and vehicle emissions. The utilization of high-performance composite materials is an important way to achieve lightweight vehicles technically. The advantages of using thermoplastic composite wheels are: easy to form, high manufacturing efficiency, low cost and easy to recycle. This leads to broader application prospects. Taking composite anisotropy into consideration, the mechanical performance of a wheel made of long glass fiber reinforced thermoplastic (LGFT), is analysed using the finite element method (FEM). This is done by placing the wheel under a bending fatigue load simulation. According to the simulation results, the sample database is established by orthogonal experimental method on the Isight platform, and the approximate model is established by the Response Surface Methodology (RSM). Based on this model, uncertainty optimization analysis is then conducted on the wheel’s design using Sigma Principle whereby the optimization target is the mass minimization. The maximum deformation of the wheel and the stress on both sides of the spoke will serve as constraint conditions and the key dimension parameters of the wheel model will be taken as the design variables. The uncertainty optimization is based on the Sigma criterion, taking into consideration the wheel’s geometry and property-fluctuation materials. The feasibility of design schemes is then verified after comparison analysis between the optimization results and the simulation results obtained. The result shows that compared with deterministic optimization, though the weight of the wheel has slightly increased, the uncertainty optimization based on the Sigma criterion is much more robust and the reliabilities of the three constraints are all above 6 Sigma. The resulting optimized LGFT wheel weighs 5.28kg, which has a 5.5% more loss in weight than the initial target and is also 25.6% lighter than the counterpart wheel which is made of aluminum alloy. The desired design results is now achieved with this lightweight effect.","PeriodicalId":201128,"journal":{"name":"Volume 13: Design, Reliability, Safety, and Risk","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114867783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yiben Zhang, L. Sun, Lijun Li, Taikun Wang, Yantao Wang
Metal-polymer hybrid (MPH) materials can integrate the excellent mechanical properties of metal and complex geometry formability of polymer into a single component, which has become an effective way of reducing the weight of automotive semi-structural components. For example, the hybrid steel/thermoplastic polymer has been applied in automotive front-end modules, bumper cross-beams and B-pillars due to its light weight, excellent strength and stiffness, good corrosion resistance and recycling, high integration and reasonable cost. These components are usually subjected to impact or crash loads and the strain rate effect should be taken into account. This paper aims to experimentally and numerically study the dynamic behavior of MPH materials at different strain rates and provide an accurate and efficient numerical model for crash simulation of vehicles with MPH components. Firstly, MPH specimens with high strength steel (HSS) and glass fiber-reinforced thermoplastic polymer (GFRTP) were fabricated by direct injection molding adhesion (DIMA) process. Then, the dynamic mechanical properties of MPH specimens under strain rates from 800 s−1 to 2000 s−1 were investigated by Split Hopkinson Pressure Bar (SHPB) experiments. Finally, a strain rate-dependent numerical model was established in ABAQUS software to simulate the dynamic behavior of MPH specimens and validated by experimental results. Three numerical approaches for modeling the interface between the two discrete material phases were considered and compared to examine the level of interaction between two constitute materials. Cohesive zone modeling technique at the interface which saved modeling and characterization time and showed adequate predictive capability proved to be generally applicable to the evaluation of structural concepts in an early vehicle development stage. This study provides a foundation for the future engineering application of HSS/GFRP hybrid materials and numerical models for automotive crash simulation.
{"title":"Experimental and Numerical Studies on Dynamic Mechanical Properties of Metal-Polymer Hybrid Materials","authors":"Yiben Zhang, L. Sun, Lijun Li, Taikun Wang, Yantao Wang","doi":"10.1115/IMECE2018-86521","DOIUrl":"https://doi.org/10.1115/IMECE2018-86521","url":null,"abstract":"Metal-polymer hybrid (MPH) materials can integrate the excellent mechanical properties of metal and complex geometry formability of polymer into a single component, which has become an effective way of reducing the weight of automotive semi-structural components. For example, the hybrid steel/thermoplastic polymer has been applied in automotive front-end modules, bumper cross-beams and B-pillars due to its light weight, excellent strength and stiffness, good corrosion resistance and recycling, high integration and reasonable cost. These components are usually subjected to impact or crash loads and the strain rate effect should be taken into account.\u0000 This paper aims to experimentally and numerically study the dynamic behavior of MPH materials at different strain rates and provide an accurate and efficient numerical model for crash simulation of vehicles with MPH components.\u0000 Firstly, MPH specimens with high strength steel (HSS) and glass fiber-reinforced thermoplastic polymer (GFRTP) were fabricated by direct injection molding adhesion (DIMA) process. Then, the dynamic mechanical properties of MPH specimens under strain rates from 800 s−1 to 2000 s−1 were investigated by Split Hopkinson Pressure Bar (SHPB) experiments. Finally, a strain rate-dependent numerical model was established in ABAQUS software to simulate the dynamic behavior of MPH specimens and validated by experimental results. Three numerical approaches for modeling the interface between the two discrete material phases were considered and compared to examine the level of interaction between two constitute materials. Cohesive zone modeling technique at the interface which saved modeling and characterization time and showed adequate predictive capability proved to be generally applicable to the evaluation of structural concepts in an early vehicle development stage.\u0000 This study provides a foundation for the future engineering application of HSS/GFRP hybrid materials and numerical models for automotive crash simulation.","PeriodicalId":201128,"journal":{"name":"Volume 13: Design, Reliability, Safety, and Risk","volume":"50 9-10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120894227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This research proposes an analytical vehicle KANO model, called V-KANO Model, which will be able to improve vehicle technical characteristics and performance target setting more precisely at the early stage during vehicle design and development process. A vehicle needs to be designed to satisfy the wants of the customers, the key is to truthfully and accurately interpret customers’ wants into engineering specifications; in other words, to translate customers’ voices into vehicle’s technical characteristics and design targets correctly. In this research, the KANO model originally introduced by Japanese quality expert Dr. Noriaki Kano is improved and modified for vehicle’s technical characteristics and performance design targets setting. The conventional KANO model was only a qualitative-based general model lack of precision and detailed information, the new innovatively developed V-KANO Model modifies and improves it into a quantitative-based mathematical model which correlates the customers’ satisfaction degree of vehicle performance with the professional trained engineers’ evaluation index closely to successfully derive an analytical vehicle KANO model, which can be adopted to guide a new vehicle system technical characteristics and performance design targets setting and development at the early stages of vehicle design process.
{"title":"Analytical Vehicle KANO Model Development","authors":"Jiaquan Chen, Yin-ping Chang","doi":"10.1115/IMECE2018-87825","DOIUrl":"https://doi.org/10.1115/IMECE2018-87825","url":null,"abstract":"This research proposes an analytical vehicle KANO model, called V-KANO Model, which will be able to improve vehicle technical characteristics and performance target setting more precisely at the early stage during vehicle design and development process. A vehicle needs to be designed to satisfy the wants of the customers, the key is to truthfully and accurately interpret customers’ wants into engineering specifications; in other words, to translate customers’ voices into vehicle’s technical characteristics and design targets correctly. In this research, the KANO model originally introduced by Japanese quality expert Dr. Noriaki Kano is improved and modified for vehicle’s technical characteristics and performance design targets setting. The conventional KANO model was only a qualitative-based general model lack of precision and detailed information, the new innovatively developed V-KANO Model modifies and improves it into a quantitative-based mathematical model which correlates the customers’ satisfaction degree of vehicle performance with the professional trained engineers’ evaluation index closely to successfully derive an analytical vehicle KANO model, which can be adopted to guide a new vehicle system technical characteristics and performance design targets setting and development at the early stages of vehicle design process.","PeriodicalId":201128,"journal":{"name":"Volume 13: Design, Reliability, Safety, and Risk","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122983961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sangram Patil, Aum Patil, V. Handikherkar, Sumit Desai, V. Phalle, Faruk Kazi
Rolling element bearings are very important and highly utilized in many industries. Their catastrophic failure due to fluctuating working conditions leads to unscheduled breakdown and increases accidental economical losses. Thus these issues have triggered a need for reliable and automatic prognostics methodology which will prevent a potentially expensive maintenance program. Accordingly, Remaining Useful Life (RUL) prediction based on artificial intelligence is an attractive methodology for several researchers. In this study, data-driven condition monitoring approach is implemented for predicting RUL of bearing under a certain load and speed. The approach demonstrates the use of ensemble regression techniques like Random Forest and Gradient Boosting for prediction of RUL with time-domain features which are extracted from given vibration signals. The extracted features are ranked using Decision Tree (DT) based ranking technique and training and testing feature vectors are produced and fed as an input to ensemble technique. Hyper-parameters are tuned for these models by using exhaustive parameter search and performance of these models is further verified by plotting respective learning curves. For the present work FEMTO bearing data-set provided by IEEE PHM Data Challenge 2012 is used. Weibull Hazard Rate Function for each bearing from learning data set is used to find target values i.e. projected RUL of the bearings. Results of proposed models are compared with well-established data-driven approaches from literature and are found to be better than all the models applied on this data-set, thereby demonstrating the reliability of the proposed model.
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