C. Yuce, Ozhan Gecgel, Oğuz Doğan, S. Dabetwar, Yasar Yanik, O. Kalay, E. Karpat, F. Karpat, S. Ekwaro-Osire
The improvements in wind energy infrastructure have been a constant process throughout many decades. There are new advancements in technology that can further contribute towards the Prognostics and Health Management (PHM) in this industry. These advancements are driven by the need to fully explore the impact of uncertainty, quality and quantity of data, physics-based machine learning (PBML), and digital twin (DT). All these aspects need to be taken into consideration to perform an effective PHM of wind energy infrastructure. To address these aspects, four research questions were formulated. What is the role of uncertainty in machine learning (ML) in diagnostics and prognostics? What is the role of data augmentation and quality of data for ML? What is the role of PBML? What is the role of the DT in diagnostics and prognostics? The methodology used was Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA). A total of 143 records, from the last five years, were analyzed. Each of the four questions was answered by discussion of literature, definitions, critical aspects, benefits and challenges, the role of aspect in PHM of wind energy infrastructure systems, and conclusion.
{"title":"Prognostics and Health Management of Wind Energy Infrastructure Systems","authors":"C. Yuce, Ozhan Gecgel, Oğuz Doğan, S. Dabetwar, Yasar Yanik, O. Kalay, E. Karpat, F. Karpat, S. Ekwaro-Osire","doi":"10.1115/1.4053422","DOIUrl":"https://doi.org/10.1115/1.4053422","url":null,"abstract":"\u0000 The improvements in wind energy infrastructure have been a constant process throughout many decades. There are new advancements in technology that can further contribute towards the Prognostics and Health Management (PHM) in this industry. These advancements are driven by the need to fully explore the impact of uncertainty, quality and quantity of data, physics-based machine learning (PBML), and digital twin (DT). All these aspects need to be taken into consideration to perform an effective PHM of wind energy infrastructure. To address these aspects, four research questions were formulated. What is the role of uncertainty in machine learning (ML) in diagnostics and prognostics? What is the role of data augmentation and quality of data for ML? What is the role of PBML? What is the role of the DT in diagnostics and prognostics? The methodology used was Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA). A total of 143 records, from the last five years, were analyzed. Each of the four questions was answered by discussion of literature, definitions, critical aspects, benefits and challenges, the role of aspect in PHM of wind energy infrastructure systems, and conclusion.","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":"26 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89806973","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}
Surveys and the resulting data provide powerful insights to an item or concept being assessed. Not only do they garner feedback that can be used to enhance the item, but, in the instance of this article, they can enhance the validity of positive change via novel concepts prior to spending the time and money in incorporating these concepts and running the risk of negligible meaningful return. In an effort to ensure the validity of proposed novel methods in a design engineering context, a survey was developed and administered to engineering subject matter experts. This was done to not only ensure that the proposed methods would be viable if implemented but gave confidence in the results of the research that drove the method development. This assessment activity served as a risk reduction activity to ensure smooth implementation of the methods from both an efficiency standpoint and, most importantly, as part of maximizing system safety. This paper discusses the composition and considerations of the survey administered in the research study, in addition to the survey results with the intention of providing a format for others in a similar context to glean from and, if practical, replicate the method.
{"title":"Survey Use to Validate Engineering Methodology and Enhance System Safety","authors":"Jonathan K. Corrado","doi":"10.1115/1.4053305","DOIUrl":"https://doi.org/10.1115/1.4053305","url":null,"abstract":"\u0000 Surveys and the resulting data provide powerful insights to an item or concept being assessed. Not only do they garner feedback that can be used to enhance the item, but, in the instance of this article, they can enhance the validity of positive change via novel concepts prior to spending the time and money in incorporating these concepts and running the risk of negligible meaningful return. In an effort to ensure the validity of proposed novel methods in a design engineering context, a survey was developed and administered to engineering subject matter experts. This was done to not only ensure that the proposed methods would be viable if implemented but gave confidence in the results of the research that drove the method development. This assessment activity served as a risk reduction activity to ensure smooth implementation of the methods from both an efficiency standpoint and, most importantly, as part of maximizing system safety. This paper discusses the composition and considerations of the survey administered in the research study, in addition to the survey results with the intention of providing a format for others in a similar context to glean from and, if practical, replicate the method.","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":"313 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77991065","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}
{"title":"Special Issue on Uncertainty Quantification and Management in Additive Manufacturing","authors":"Zhen Hu, S. Nannapaneni, S. Mahadevan","doi":"10.1115/1.4053183","DOIUrl":"https://doi.org/10.1115/1.4053183","url":null,"abstract":"\u0000 <jats:p>N/A</jats:p>","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":"1 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2021-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88312785","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 paper reviews the state of the art in applying uncertainty quantification (UQ) methods to additive manufacturing (AM). Physics-based as well as data-driven models are increasingly being developed and refined in order to support process optimization and control objectives in AM, in particular to maximize the quality and minimize the variability of the AM product. However, before using these models for decision-making, a fundamental question that needs to be answered is to what degree the models can be trusted, and consider the various uncertainty sources that affect their prediction. Uncertainty quantification (UQ) in AM is not trivial because of the complex multi-physics, multi-scale phenomena in the AM process. This article reviews the literature on UQ methodologies focusing on model uncertainty, discusses the corresponding activities of calibration, verification and validation, and examines their applications reported in the AM literature. The extension of current UQ methodologies to additive manufacturing needs to address multi-physics, multi-scale interactions, increasing presence of data-driven models, high cost of manufacturing, and complexity of measurements. The activities that need to be undertaken in order to implement verification, calibration, and validation for AM are discussed. Literature on using the results of UQ activities towards AM process optimization and control (thus supporting maximization of quality and minimization of variability) is also reviewed. Future research needs both in terms of UQ and decision-making in AM are outlined.
{"title":"Uncertainty Quantification for Additive Manufacturing Process Improvement: Recent Advances","authors":"S. Mahadevan, Paromita Nath, Zhen Hu","doi":"10.1115/1.4053184","DOIUrl":"https://doi.org/10.1115/1.4053184","url":null,"abstract":"\u0000 This paper reviews the state of the art in applying uncertainty quantification (UQ) methods to additive manufacturing (AM). Physics-based as well as data-driven models are increasingly being developed and refined in order to support process optimization and control objectives in AM, in particular to maximize the quality and minimize the variability of the AM product. However, before using these models for decision-making, a fundamental question that needs to be answered is to what degree the models can be trusted, and consider the various uncertainty sources that affect their prediction. Uncertainty quantification (UQ) in AM is not trivial because of the complex multi-physics, multi-scale phenomena in the AM process. This article reviews the literature on UQ methodologies focusing on model uncertainty, discusses the corresponding activities of calibration, verification and validation, and examines their applications reported in the AM literature. The extension of current UQ methodologies to additive manufacturing needs to address multi-physics, multi-scale interactions, increasing presence of data-driven models, high cost of manufacturing, and complexity of measurements. The activities that need to be undertaken in order to implement verification, calibration, and validation for AM are discussed. Literature on using the results of UQ activities towards AM process optimization and control (thus supporting maximization of quality and minimization of variability) is also reviewed. Future research needs both in terms of UQ and decision-making in AM are outlined.","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":"334 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75935259","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}
B. Kapusuzoglu, Paromita Nath, Matthew Sato, S. Mahadevan, P. Witherell
This work presents a data-driven methodology for multi-objective optimization under uncertainty of process parameters in the fused filament fabrication (FFF) process. The proposed approach optimizes the process parameters with the objectives of minimizing the geometric inaccuracy and maximizing the filament bond quality of the manufactured part. First, experiments are conducted to collect data pertaining to the part quality. Then, Bayesian neural network (BNN) models are constructed to predict the geometric inaccuracy and bond quality as functions of the process parameters. The BNN model captures the model uncertainty caused by the lack of knowledge about model parameters (neuron weights) and the input variability due to the intrinsic randomness in the input parameters. Using the stochastic predictions from these models, different robustness-based design optimization formulations are investigated, wherein process parameters such as nozzle temperature, nozzle speed, and layer thickness are optimized under uncertainty for different multi-objective scenarios. Epistemic uncertainty in the prediction model and the aleatory uncertainty in the input are considered in the optimization. Finally, Pareto surfaces are constructed to estimate the trade-offs between the objectives. Both the BNN models and the effectiveness of the proposed optimization methodology are validated using actual manufacturing of the parts.
{"title":"Multi-Objective Optimization Under Uncertainty of Part Quality in Fused Filament Fabrication","authors":"B. Kapusuzoglu, Paromita Nath, Matthew Sato, S. Mahadevan, P. Witherell","doi":"10.1115/1.4053181","DOIUrl":"https://doi.org/10.1115/1.4053181","url":null,"abstract":"\u0000 This work presents a data-driven methodology for multi-objective optimization under uncertainty of process parameters in the fused filament fabrication (FFF) process. The proposed approach optimizes the process parameters with the objectives of minimizing the geometric inaccuracy and maximizing the filament bond quality of the manufactured part. First, experiments are conducted to collect data pertaining to the part quality. Then, Bayesian neural network (BNN) models are constructed to predict the geometric inaccuracy and bond quality as functions of the process parameters. The BNN model captures the model uncertainty caused by the lack of knowledge about model parameters (neuron weights) and the input variability due to the intrinsic randomness in the input parameters. Using the stochastic predictions from these models, different robustness-based design optimization formulations are investigated, wherein process parameters such as nozzle temperature, nozzle speed, and layer thickness are optimized under uncertainty for different multi-objective scenarios. Epistemic uncertainty in the prediction model and the aleatory uncertainty in the input are considered in the optimization. Finally, Pareto surfaces are constructed to estimate the trade-offs between the objectives. Both the BNN models and the effectiveness of the proposed optimization methodology are validated using actual manufacturing of the parts.","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":"65 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75970429","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}
A. C. Uggenti, R. Gerboni, A. Carpignano, G. Ballocco, Andrea Tortora, Amedeo Aliberti
In the framework of energy transition, a focus is given to the study of the conversion of offshore Oil&Gas platforms at the end of their life due to depletion of the reservoirs on which they operate. Their modular and versatile structure allows the implementation of new processes and innovative sustainable technologies for reducing the environmental impact of a complete decommissioning, especially on the subsea ecosystem that has grown around the jacket, and for guaranteeing costsaving solutions. Among different conversion options, this paper focuses on the installation on the platform of a system for the production of photovoltaic (PV) energy to be used for seawater desalination and its delivery to other platforms operating in the same area. The project focuses on the definition of technical characteristics of the basic design, on the investigation of the technical feasibility of the conversion process, on qualitative safety and environmental impact studies. Moreover, the old platform equipment to be decommissioned (ie. the equipment necessary for hydrocarbons treatment) are identified and the installation of new equipment is optimized, eg. the number of PV panels and, therefore, the installed power are maximized. At the same time, decommissioning costs and impacts can be minimized. The basic design is completed with a preliminary structural verification to guarantee that critical situations do not rise, with an indication on the main maintenance activities for the preservation of plant good efficiency and with safety and environmental preliminary analyses for the identification of potential criticalities to be managed at different design levels.
{"title":"Definition of a Basic Design for Conversion of an Offshore Fixed Platform on a Depleted Reservoir Into a Sustainable Plant","authors":"A. C. Uggenti, R. Gerboni, A. Carpignano, G. Ballocco, Andrea Tortora, Amedeo Aliberti","doi":"10.1115/1.4053061","DOIUrl":"https://doi.org/10.1115/1.4053061","url":null,"abstract":"\u0000 In the framework of energy transition, a focus is given to the study of the conversion of offshore Oil&Gas platforms at the end of their life due to depletion of the reservoirs on which they operate. Their modular and versatile structure allows the implementation of new processes and innovative sustainable technologies for reducing the environmental impact of a complete decommissioning, especially on the subsea ecosystem that has grown around the jacket, and for guaranteeing costsaving solutions. Among different conversion options, this paper focuses on the installation on the platform of a system for the production of photovoltaic (PV) energy to be used for seawater desalination and its delivery to other platforms operating in the same area. The project focuses on the definition of technical characteristics of the basic design, on the investigation of the technical feasibility of the conversion process, on qualitative safety and environmental impact studies. Moreover, the old platform equipment to be decommissioned (ie. the equipment necessary for hydrocarbons treatment) are identified and the installation of new equipment is optimized, eg. the number of PV panels and, therefore, the installed power are maximized. At the same time, decommissioning costs and impacts can be minimized. The basic design is completed with a preliminary structural verification to guarantee that critical situations do not rise, with an indication on the main maintenance activities for the preservation of plant good efficiency and with safety and environmental preliminary analyses for the identification of potential criticalities to be managed at different design levels.","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":"18 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2021-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77318864","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}
The purpose of this work is to enable the use of the Dempster-Shafer evidence theory for uncertainty propagation on computationally expensive automotive crash simulations. This is necessary as the results of these simulations are influenced by multiple possibly uncertain aspects. To avoid negative effects, it is important to detect these factors and their consequences. The challenge when pursuing this effort is the prohibitively high computational cost of the evidence theory. To this end, we present a framework of existing methods that is specifically designed to reduce the necessary number of full model evaluations and parameters. An initial screening removes clearly irrelevant parameters to mitigate the curse of dimensionality. Next, we approximate the full-scale simulation using metamodels to accelerate output generation and thus enable the calculation of global sensitivity indices. These indicate effects of the parameters on the considered output and more profoundly sort out irrelevant parameters. After these steps, the evidence theory can be performed rapidly and feasibly due to fast-responding metamodel and reduced input dimension. It yields bounds for the cumulative distribution function of the considered quantity of interest. We apply the proposed framework to a simplified crash test dummy model. The elementary effects method is used for screening, a kriging metamodel emulates the finite element simulation, and Sobol' sensitivity indices are determined before the evidence theory is applied. The outcome of the framework provide engineers with information about the uncertainties they may face in hardware testing and that should be addressed in future vehicle design.
{"title":"Proposing an Uncertainty Management Framework to Implement the Evidence Theory for Vehicle Crash Applications","authors":"J. Jehle, Volker A. Lange, M. Gerdts","doi":"10.1115/1.4053062","DOIUrl":"https://doi.org/10.1115/1.4053062","url":null,"abstract":"\u0000 The purpose of this work is to enable the use of the Dempster-Shafer evidence theory for uncertainty propagation on computationally expensive automotive crash simulations. This is necessary as the results of these simulations are influenced by multiple possibly uncertain aspects. To avoid negative effects, it is important to detect these factors and their consequences. The challenge when pursuing this effort is the prohibitively high computational cost of the evidence theory. To this end, we present a framework of existing methods that is specifically designed to reduce the necessary number of full model evaluations and parameters. An initial screening removes clearly irrelevant parameters to mitigate the curse of dimensionality. Next, we approximate the full-scale simulation using metamodels to accelerate output generation and thus enable the calculation of global sensitivity indices. These indicate effects of the parameters on the considered output and more profoundly sort out irrelevant parameters. After these steps, the evidence theory can be performed rapidly and feasibly due to fast-responding metamodel and reduced input dimension. It yields bounds for the cumulative distribution function of the considered quantity of interest. We apply the proposed framework to a simplified crash test dummy model. The elementary effects method is used for screening, a kriging metamodel emulates the finite element simulation, and Sobol' sensitivity indices are determined before the evidence theory is applied. The outcome of the framework provide engineers with information about the uncertainties they may face in hardware testing and that should be addressed in future vehicle design.","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":"107 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2021-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79344708","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}
A methodology to account for the effect of epistemic uncertainty (regarding model parameters) on the strength prediction of carbon fiber reinforced polymer (CFRP) composite laminates is presented. A three-dimensional concurrent multiscale physics modeling framework is considered. A continuum damage mechanics-based constitutive relation is used for multiscale analysis. The parameters for the constitutive model are unknown and need to be calibrated. A least squares-based approach is employed for the calibration of model parameters and a model discrepancy term. The calibrated constitutive model is validated quantitatively using experimental data for both unnotched and open-hole specimens with different composite layups. The quantitative validation results are used to indicate further steps for model improvement.
{"title":"Calibration and Validation of Multiscale Model for Ultimate Strength Prediction of Composite Laminates Under Uncertainty","authors":"R. Bhattacharyya, S. Mahadevan","doi":"10.1115/1.4053060","DOIUrl":"https://doi.org/10.1115/1.4053060","url":null,"abstract":"\u0000 A methodology to account for the effect of epistemic uncertainty (regarding model parameters) on the strength prediction of carbon fiber reinforced polymer (CFRP) composite laminates is presented. A three-dimensional concurrent multiscale physics modeling framework is considered. A continuum damage mechanics-based constitutive relation is used for multiscale analysis. The parameters for the constitutive model are unknown and need to be calibrated. A least squares-based approach is employed for the calibration of model parameters and a model discrepancy term. The calibrated constitutive model is validated quantitatively using experimental data for both unnotched and open-hole specimens with different composite layups. The quantitative validation results are used to indicate further steps for model improvement.","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":"60 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2021-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75700048","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}
L. Landi, E. Uhlmann, Robert Hoerl, S. Thom, Giuseppe Gigliotti, A. Stecconi
Machine guards provide protection against ejection of parts during operation, such as chips or workpiece fragments. They are considered safe if the impact resistance is at least as high as the resulting projectile energy in the worst case of damage. To protect the machine operator, the impact resistance of machine guards is determined according to ISO standards. The bisection method can be used to determine the impact resistance through impact tests. However, this method is inaccurate for a small number of impact tests and does not provide an indication of uncertainties in the determination. Moreover, the result of testing is validated in different ways depending from the standard utilized for testing.Relevant uncertainties affecting impact testing and a new probabilistic approach for assessing the impact resistance using the Recht & Ipson equation are presented. With multiple impact tests at different initial velocities a Recht & Ipson best-fit curve and a confidence interval for a ballistic limit can be obtained, which is used to determine the impact resistance by defining a velocity reduction coefficient. This method can be applied to any machine guard made of ductile material. This paper validates the Recht & Ipson method by performing impact tests with a standardized 2.5 kg projectile on polycarbonate sheets of different thicknesses. Determination of the ballistic limit showed good agreement with experimental results. With the ballistic limits, the velocity reduction coefficients have been found to determine the impact resistances. Therefore, an alternative method for standardized tests to determine the impact resistance was found.
{"title":"Evaluation of Testing Uncertainties for the Impact Resistance of Machine Guards","authors":"L. Landi, E. Uhlmann, Robert Hoerl, S. Thom, Giuseppe Gigliotti, A. Stecconi","doi":"10.1115/1.4052995","DOIUrl":"https://doi.org/10.1115/1.4052995","url":null,"abstract":"\u0000 Machine guards provide protection against ejection of parts during operation, such as chips or workpiece fragments. They are considered safe if the impact resistance is at least as high as the resulting projectile energy in the worst case of damage. To protect the machine operator, the impact resistance of machine guards is determined according to ISO standards. The bisection method can be used to determine the impact resistance through impact tests. However, this method is inaccurate for a small number of impact tests and does not provide an indication of uncertainties in the determination. Moreover, the result of testing is validated in different ways depending from the standard utilized for testing.Relevant uncertainties affecting impact testing and a new probabilistic approach for assessing the impact resistance using the Recht & Ipson equation are presented. With multiple impact tests at different initial velocities a Recht & Ipson best-fit curve and a confidence interval for a ballistic limit can be obtained, which is used to determine the impact resistance by defining a velocity reduction coefficient. This method can be applied to any machine guard made of ductile material. This paper validates the Recht & Ipson method by performing impact tests with a standardized 2.5 kg projectile on polycarbonate sheets of different thicknesses. Determination of the ballistic limit showed good agreement with experimental results. With the ballistic limits, the velocity reduction coefficients have been found to determine the impact resistances. Therefore, an alternative method for standardized tests to determine the impact resistance was found.","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":"20 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2021-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87176377","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}
Modern vehicles are connected to the network and between each other through smart sensors and smart objects commonly present on board. This situation has allowed manufacturers to send over-the-air updates, receive diagnostic information, and offer various multimedia services. More generally, at present, all this is indicated by the term 'Vehicle to Everything' (V2X), which indicates a system of communication between a vehicle to any entity that may influence the vehicle and vice versa. However, it introduces problems regarding the vehicle's IT security. It is possible, for example, by tampering with one of the Electronic Control Units (ECUs) to take partial or total control of the vehicle. In this paper, we introduce a preliminary study case to guarantee cybersecurity inside connected vehicles. In particular, an Intrusion Detection System over the CAN-Bus to allow the possible malicious massages. In particular, through the use of a two-step detection algorithm that exploits both the variation of the status parameters of the various ECUs over time and the Bayesian networks can identify a possible attack. The first experimental results seem encouraging.
{"title":"Two-Step Algorithm to Detect Cyber-Attack Over the Can-Bus: A Preliminary Case Study in Connected Vehicles","authors":"Marco Lombardi, F. Pascale, D. Santaniello","doi":"10.1115/1.4052823","DOIUrl":"https://doi.org/10.1115/1.4052823","url":null,"abstract":"\u0000 Modern vehicles are connected to the network and between each other through smart sensors and smart objects commonly present on board. This situation has allowed manufacturers to send over-the-air updates, receive diagnostic information, and offer various multimedia services. More generally, at present, all this is indicated by the term 'Vehicle to Everything' (V2X), which indicates a system of communication between a vehicle to any entity that may influence the vehicle and vice versa. However, it introduces problems regarding the vehicle's IT security. It is possible, for example, by tampering with one of the Electronic Control Units (ECUs) to take partial or total control of the vehicle. In this paper, we introduce a preliminary study case to guarantee cybersecurity inside connected vehicles. In particular, an Intrusion Detection System over the CAN-Bus to allow the possible malicious massages. In particular, through the use of a two-step detection algorithm that exploits both the variation of the status parameters of the various ECUs over time and the Bayesian networks can identify a possible attack. The first experimental results seem encouraging.","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":"4 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87552749","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：