This paper presents a novel real-time kinematic simulation algorithm for planar N-bar linkage mechanisms, both single- and multi-degree-of-freedom, comprising revolute and/or prismatic joints and actuators. A key feature of this algorithm is a reinterpretation technique that transforms prismatic elements into a combination of revolute joint and links. This gives rise to a unified system of geometric constraints and a general purpose solver which adapts to the complexity of the mechanism. The solver requires only two types of methods -- fast dyadic decomposition and relatively slower optimization-based -- to simulate all types of planar mechanisms. From an implementation point of view, this algorithm simplifies programming without requiring handling of different types of mechanisms. This versatile algorithm can handle serial, parallel, and hybrid planar mechanisms with varying degrees of freedom and joint types. Additionally, this paper presents an estimation of simulation time and structural complexity, shedding light on computational demands. Demonstrative examples showcase the practicality of this method.
本文介绍了一种新颖的实时运动学模拟算法，适用于平面 N 杆连杆机构，包括单自由度和多自由度，由外旋式和/或棱柱式关节和执行器组成。该算法的一个主要特点是采用了一种重新解释技术，将棱柱元素转换为旋卷关节和连杆的组合。这就产生了一个统一的几何约束系统和一个通用求解器，可以适应机构的复杂性。该求解器只需要两类方法--快速的二元分解法和相对较慢的优化法--就能模拟所有类型的平面机构。从实现的角度来看，这种算法简化了编程，无需处理不同类型的机构。这种通用算法可以处理具有不同自由度和关节类型的串行、并行和混合平面机构。此外，本文还对模拟时间和结构复杂性进行了估算，阐明了计算需求。示例展示了该方法的实用性。
{"title":"A Unified Real-time Motion Generation Algorithm for Approximate Position Analysis of Planar N-Bar Mechanisms","authors":"Z. Lyu, A. Purwar, Wei Liao","doi":"10.1115/1.4064132","DOIUrl":"https://doi.org/10.1115/1.4064132","url":null,"abstract":"This paper presents a novel real-time kinematic simulation algorithm for planar N-bar linkage mechanisms, both single- and multi-degree-of-freedom, comprising revolute and/or prismatic joints and actuators. A key feature of this algorithm is a reinterpretation technique that transforms prismatic elements into a combination of revolute joint and links. This gives rise to a unified system of geometric constraints and a general purpose solver which adapts to the complexity of the mechanism. The solver requires only two types of methods -- fast dyadic decomposition and relatively slower optimization-based -- to simulate all types of planar mechanisms. From an implementation point of view, this algorithm simplifies programming without requiring handling of different types of mechanisms. This versatile algorithm can handle serial, parallel, and hybrid planar mechanisms with varying degrees of freedom and joint types. Additionally, this paper presents an estimation of simulation time and structural complexity, shedding light on computational demands. Demonstrative examples showcase the practicality of this method.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":"2 8","pages":""},"PeriodicalIF":3.3,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139243882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A vicious cycle exists when higher global temperatures increase the demand for indoor air-conditioning, which consumes significant energy while heating the outdoors. These higher outdoor temperatures then prompt more air-conditioning use. This unsustainable cycle motivated us to develop an intervention to encourage energy-efficient temperature adjustments during warm ambient conditions. We explored whether an experimental thermostat interface, which incorporated mechanical fans, affected individual thermostat-setting behavior. Experiment parameters were 1) feel versus do-not-feel fan, and 2) high- versus low-visibility fan. Participants were 23 university students, including 20 enrolled in an introductory psychology course. When prompted to make temperature adjustments, we found that participants who felt the fan selected higher (more energy-efficient) temperatures in warm weather. This effect held regardless of whether participants could clearly see the fan or not. These results inform how products can be designed to increase energy-conscious behaviors.
{"title":"Does Fan Feel and Visibility during Thermostat Interaction Affect Temperature Selection in Warm Ambient Conditions?","authors":"Alexa Rea, Laura Corbit, L.H. Shu","doi":"10.1115/1.4064100","DOIUrl":"https://doi.org/10.1115/1.4064100","url":null,"abstract":"A vicious cycle exists when higher global temperatures increase the demand for indoor air-conditioning, which consumes significant energy while heating the outdoors. These higher outdoor temperatures then prompt more air-conditioning use. This unsustainable cycle motivated us to develop an intervention to encourage energy-efficient temperature adjustments during warm ambient conditions. We explored whether an experimental thermostat interface, which incorporated mechanical fans, affected individual thermostat-setting behavior. Experiment parameters were 1) feel versus do-not-feel fan, and 2) high- versus low-visibility fan. Participants were 23 university students, including 20 enrolled in an introductory psychology course. When prompted to make temperature adjustments, we found that participants who felt the fan selected higher (more energy-efficient) temperatures in warm weather. This effect held regardless of whether participants could clearly see the fan or not. These results inform how products can be designed to increase energy-conscious behaviors.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":"38 6","pages":""},"PeriodicalIF":3.3,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139255904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mingxiang Ling, Linfeng Zhao, Shilei Wu, Liguo Chen, Lining Sun
Owing to the advantages of monolithic structure and little need for assembling, compliant guiding mechanisms appear to be an effective solution for decoupling multi-freedom precision motions but are still prone to geometric nonlinearities of parasitic error and stiffening effect for large strokes. This paper proposes a coiled L-shape compliant guiding mechanism featuring millimeter-scale strokes with a compact structure, constant stiffness and minimized parasitic error. The coiled compliant guiding mechanism is formed by convolving L-shape flexure beams in a zigzag configuration with decoupled XY motions achieved. Its geometrically nonlinear parasitic error, variation in stiffness and primary vibration are captured by using a dynamic beam constraint model (DBCM). It is theoretically, numerically and experimentally found, by comparing with double parallel guiding mechanisms, that the kinetostatic and dynamic behaviors of the coiled L-shape compliant mechanism are nearly independent on the applied force within the intermediate-deformation ranges. Such a weak geometric nonlinearity with the minimized influence of axially-loaded stiffening and kinematics-arching effects is much different from the double parallel guiding mechanisms. The obtained results indicate that large strokes with constant stiffness and invariable resonance frequency can be realized, which also allows small parasitic errors.
由于具有单片结构和无需组装的优点，顺应式导向机构似乎是解耦多自由度精密运动的有效解决方案，但仍容易受到寄生误差的几何非线性影响，以及大行程的刚化效应。本文提出了一种具有毫米级冲程、结构紧凑、刚度恒定且寄生误差最小的盘绕式 L 型顺导机构。该盘绕式顺应导引机构由 L 型挠性梁以之字形配置卷绕而成，并实现了 XY 运动的解耦。其几何非线性寄生误差、刚度变化和主振动通过使用动态梁约束模型（DBCM）来捕捉。通过与双平行导向机构进行理论、数值和实验比较，发现在中间变形范围内，盘绕式 L 型顺应机构的运动静态和动态行为几乎与施加的力无关。这种几何非线性较弱，轴向加载刚度和运动学拱形效应的影响最小，与双平行导向机构有很大不同。所获得的结果表明，可以实现具有恒定刚度和不变共振频率的大冲程，同时允许较小的寄生误差。
{"title":"Nonlinear evaluation of a large-stroke coiled L-shape compliant guiding mechanism with constant stiffness","authors":"Mingxiang Ling, Linfeng Zhao, Shilei Wu, Liguo Chen, Lining Sun","doi":"10.1115/1.4064074","DOIUrl":"https://doi.org/10.1115/1.4064074","url":null,"abstract":"Owing to the advantages of monolithic structure and little need for assembling, compliant guiding mechanisms appear to be an effective solution for decoupling multi-freedom precision motions but are still prone to geometric nonlinearities of parasitic error and stiffening effect for large strokes. This paper proposes a coiled L-shape compliant guiding mechanism featuring millimeter-scale strokes with a compact structure, constant stiffness and minimized parasitic error. The coiled compliant guiding mechanism is formed by convolving L-shape flexure beams in a zigzag configuration with decoupled XY motions achieved. Its geometrically nonlinear parasitic error, variation in stiffness and primary vibration are captured by using a dynamic beam constraint model (DBCM). It is theoretically, numerically and experimentally found, by comparing with double parallel guiding mechanisms, that the kinetostatic and dynamic behaviors of the coiled L-shape compliant mechanism are nearly independent on the applied force within the intermediate-deformation ranges. Such a weak geometric nonlinearity with the minimized influence of axially-loaded stiffening and kinematics-arching effects is much different from the double parallel guiding mechanisms. The obtained results indicate that large strokes with constant stiffness and invariable resonance frequency can be realized, which also allows small parasitic errors.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":"33 3","pages":""},"PeriodicalIF":3.3,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139266158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Current high-performance prosthetic feet work well for many users, but the low resolution of size and stiffness categories may limit walking performance for certain users. A line of prosthetic feet with a high resolution of sizes and stiffnesses, designed through amputee-specific personalization, could provide clinical and economic value. The lower leg trajectory error (LLTE) design framework facilitates the design of high-performance, amputee-specific prosthetic feet; however, previous foot prototypes were not designed to satisfy the economic, mechanical, and aesthetic requirements for commercial adoption. The aims of this work were to understand how a personalized, affordable prosthetic foot can align with the clinical-commercial ecosystem, innovate a viable future product, and inform other prosthesis designers of considerations required to connect innovation to real-world implementation. We evaluated needs by identifying how products, capital, and services flow between stakeholders, and we elucidated design requirements for a personalized prosthetic foot that can be manufactured, dis- tributed, and clinically provided. Based on material properties and manufacturing process capabilities, CNC machining of Nylon 6/6 satisfies these requirements. We present a novel parametric foot architecture that can be CNC machined, fits within a commercial foot shell, and can be designed for individual users' body characteristics and activity levels. Prototypes made using the new foot design behaved as anticipated (1-12% error in modeled displacement), satisfied industry-standard strength (ISO 10328) and mechanical performance (AOPA dynamic heel/keel) requirements, and elicited positive feedback from both amputees and prosthetists.
{"title":"Design and mechanical validation of commercially viable, personalized passive prosthetic feet","authors":"Charlotte Folinus, V. Amos G. Winter","doi":"10.1115/1.4064073","DOIUrl":"https://doi.org/10.1115/1.4064073","url":null,"abstract":"Current high-performance prosthetic feet work well for many users, but the low resolution of size and stiffness categories may limit walking performance for certain users. A line of prosthetic feet with a high resolution of sizes and stiffnesses, designed through amputee-specific personalization, could provide clinical and economic value. The lower leg trajectory error (LLTE) design framework facilitates the design of high-performance, amputee-specific prosthetic feet; however, previous foot prototypes were not designed to satisfy the economic, mechanical, and aesthetic requirements for commercial adoption. The aims of this work were to understand how a personalized, affordable prosthetic foot can align with the clinical-commercial ecosystem, innovate a viable future product, and inform other prosthesis designers of considerations required to connect innovation to real-world implementation. We evaluated needs by identifying how products, capital, and services flow between stakeholders, and we elucidated design requirements for a personalized prosthetic foot that can be manufactured, dis- tributed, and clinically provided. Based on material properties and manufacturing process capabilities, CNC machining of Nylon 6/6 satisfies these requirements. We present a novel parametric foot architecture that can be CNC machined, fits within a commercial foot shell, and can be designed for individual users' body characteristics and activity levels. Prototypes made using the new foot design behaved as anticipated (1-12% error in modeled displacement), satisfied industry-standard strength (ISO 10328) and mechanical performance (AOPA dynamic heel/keel) requirements, and elicited positive feedback from both amputees and prosthetists.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":"1 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139265839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Designing mechatronic products requires interdisciplinary skills and as products become more complex, the design of mechatronic systems plays a critical role. Repairing plays a key part in achieving a circular economy. Through repairability, the product lifespan can be extended, and combined with maintenance the rate of product replacement can be reduced. Within this context, the goal of this paper is to propose a design methodology (based on the EN 45554:2020 standard) for generating and implementing eco-design rules for disassembly and repair. The methodology has four phases, the first one is the identification of target components (those that are more likely to fail during the lifespan). The second phase encompasses the experimental disassembly analysis which can be manual or virtual. The third phase is the assessment of the Disassemblability Index which includes the analysis of parameters that affect the disassembly phase. The last phase is the implementation of the eco-design methodology for all the components that do not meet the minimum repairability requirements. A case study of electro-mechanical ovens is presented, targeting replaceable components. The results show that the use of this framework and the eco-design actions derived from it are successful in improving the repairability of the product and increasing the Disassemblability Index (30% on average) through a virtual analysis. A sensitivity analysis has been conducted to study the impact of parameter weight modification. This research contributes to advancing repairability and supporting the circular economy paradigm in mechatronic product design.
机电一体化产品的设计需要跨学科的技能，随着产品变得越来越复杂，机电一体化系统的设计起着至关重要的作用。维修在实现循环经济方面发挥着关键作用。通过可维修性，可以延长产品的使用寿命，并与维护相结合，降低产品的更换率。在此背景下，本文旨在提出一种设计方法（基于 EN 45554:2020 标准），用于生成和实施拆卸和维修的生态设计规则。该方法分为四个阶段，第一阶段是确定目标部件（那些在使用寿命期间更有可能失效的部件）。第二阶段包括实验性拆卸分析，可以是手动的，也可以是虚拟的。第三阶段是评估可拆解性指数，包括分析影响拆解阶段的参数。最后一个阶段是对所有不符合最低可修复性要求的部件实施生态设计方法。本报告介绍了一项针对可更换组件的机电烤箱案例研究。结果表明，通过虚拟分析，该框架的使用和由此衍生的生态设计行动成功地改善了产品的可修复性，并提高了拆卸指数（平均 30%）。还进行了敏感性分析，以研究参数权重修改的影响。这项研究有助于在机电一体化产品设计中提高可修复性和支持循环经济模式。
{"title":"Disassembly and repairability of mechatronic products: insight for engineering design","authors":"Núria Boix Rodríguez, Claudio Favi","doi":"10.1115/1.4064075","DOIUrl":"https://doi.org/10.1115/1.4064075","url":null,"abstract":"Designing mechatronic products requires interdisciplinary skills and as products become more complex, the design of mechatronic systems plays a critical role. Repairing plays a key part in achieving a circular economy. Through repairability, the product lifespan can be extended, and combined with maintenance the rate of product replacement can be reduced. Within this context, the goal of this paper is to propose a design methodology (based on the EN 45554:2020 standard) for generating and implementing eco-design rules for disassembly and repair. The methodology has four phases, the first one is the identification of target components (those that are more likely to fail during the lifespan). The second phase encompasses the experimental disassembly analysis which can be manual or virtual. The third phase is the assessment of the Disassemblability Index which includes the analysis of parameters that affect the disassembly phase. The last phase is the implementation of the eco-design methodology for all the components that do not meet the minimum repairability requirements. A case study of electro-mechanical ovens is presented, targeting replaceable components. The results show that the use of this framework and the eco-design actions derived from it are successful in improving the repairability of the product and increasing the Disassemblability Index (30% on average) through a virtual analysis. A sensitivity analysis has been conducted to study the impact of parameter weight modification. This research contributes to advancing repairability and supporting the circular economy paradigm in mechatronic product design.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":"32 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139265161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jice Zeng, Ying Zhao, Guosong Li, Zhenyan Gao, Yang Li, Saeed Barbat, Zhen Hu
This study aims at improving the prediction accuracy of the Computer-Aided Engineering (CAE) model for crashworthiness performance evaluation at speeds beyond those defined by current regulations and public domain testing protocols. In this study, two scenarios are investigated: (1) improving CAE model prediction accuracy using test data of a vehicle type that is the same as that of the CAE model; (2) improving CAE model prediction accuracy using test data from two different types of vehicles (e.g., two different sizes of SUVs). In the first scenario, a novel approach is proposed in the displacement domain (deceleration vs. displacement) to enable data fusion to help recover the unmodeled physics in the CAE model. A nonlinear spring-mass model is used to simulate rigid-barrier vehicle frontal impact. A Gaussian process regression (GPR) model is then applied in conjunction with a Gaussian mixture model to capture the model bias of the nonlinear spring constant. In the second scenario, we propose a time domain method (deceleration vs. time) based on temporal convolutional network (TCN) and transfer learning. An initial TCN model is first trained by fusing CAE data with physical test data of the first vehicle type based on data augmentation. This data-augmented TCN model is then fine-tuned through transfer learning using CAE and test data of the second vehicle type. Cased studies are used to validate the proposed approaches, and to demonstrate their efficacy in improving the prediction accuracy of the CAE models.
{"title":"Vehicle Crashworthiness Performance Prediction through Fusion of Multiple Data Sources","authors":"Jice Zeng, Ying Zhao, Guosong Li, Zhenyan Gao, Yang Li, Saeed Barbat, Zhen Hu","doi":"10.1115/1.4064063","DOIUrl":"https://doi.org/10.1115/1.4064063","url":null,"abstract":"This study aims at improving the prediction accuracy of the Computer-Aided Engineering (CAE) model for crashworthiness performance evaluation at speeds beyond those defined by current regulations and public domain testing protocols. In this study, two scenarios are investigated: (1) improving CAE model prediction accuracy using test data of a vehicle type that is the same as that of the CAE model; (2) improving CAE model prediction accuracy using test data from two different types of vehicles (e.g., two different sizes of SUVs). In the first scenario, a novel approach is proposed in the displacement domain (deceleration vs. displacement) to enable data fusion to help recover the unmodeled physics in the CAE model. A nonlinear spring-mass model is used to simulate rigid-barrier vehicle frontal impact. A Gaussian process regression (GPR) model is then applied in conjunction with a Gaussian mixture model to capture the model bias of the nonlinear spring constant. In the second scenario, we propose a time domain method (deceleration vs. time) based on temporal convolutional network (TCN) and transfer learning. An initial TCN model is first trained by fusing CAE data with physical test data of the first vehicle type based on data augmentation. This data-augmented TCN model is then fine-tuned through transfer learning using CAE and test data of the second vehicle type. Cased studies are used to validate the proposed approaches, and to demonstrate their efficacy in improving the prediction accuracy of the CAE models.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":"24 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139270766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jesse Mullis, Cheng Chen, Scott Ferguson, Beshoy Morkos
Abstract Given the foundational role of system requirements in design projects, designers can benefit from classifying, comparing, and observing connections between requirements. Manually undertaking these processes, however, can be laborious and time-consuming. Previous studies have employed Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art natural language processing (NLP) deep neural network model, to automatically analyze written requirements. Yet, it remains unclear whether BERT can sufficiently capture the nuances that differentiate requirements between and within design documents. This work evaluates BERT’s performance on two requirement classification tasks (one inter- document and one intra-document) executed on a corpus of 1,303 requirements sourced from five system design projects. First, in the “parent document classification” task, a BERT model is fine-tuned to classify requirements according to their originating project. A separate BERT model is then fine-tuned on a “functional classification” task where each requirement is classified as either functional or nonfunctional. Our results also include a comparison with a baseline model, Word2Vec, and demonstrate that our model achieves higher classification accuracy. When evaluated on test sets, the former model receives a Matthews correlation coefficient (MCC) of 0.95, while the latter receives an MCC of 0.82, indicating BERT’s ability to reliably distinguish requirements. This work then explores the application of BERT’s representations, known as embeddings, to identify similar requirements and predict requirement change.
{"title":"Efficacy of Deep Neural Networks in Natural Language Processing for Classifying Requirements by Origin and Functionality: An Application of BERT in System Requirement","authors":"Jesse Mullis, Cheng Chen, Scott Ferguson, Beshoy Morkos","doi":"10.1115/1.4063764","DOIUrl":"https://doi.org/10.1115/1.4063764","url":null,"abstract":"Abstract Given the foundational role of system requirements in design projects, designers can benefit from classifying, comparing, and observing connections between requirements. Manually undertaking these processes, however, can be laborious and time-consuming. Previous studies have employed Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art natural language processing (NLP) deep neural network model, to automatically analyze written requirements. Yet, it remains unclear whether BERT can sufficiently capture the nuances that differentiate requirements between and within design documents. This work evaluates BERT’s performance on two requirement classification tasks (one inter- document and one intra-document) executed on a corpus of 1,303 requirements sourced from five system design projects. First, in the “parent document classification” task, a BERT model is fine-tuned to classify requirements according to their originating project. A separate BERT model is then fine-tuned on a “functional classification” task where each requirement is classified as either functional or nonfunctional. Our results also include a comparison with a baseline model, Word2Vec, and demonstrate that our model achieves higher classification accuracy. When evaluated on test sets, the former model receives a Matthews correlation coefficient (MCC) of 0.95, while the latter receives an MCC of 0.82, indicating BERT’s ability to reliably distinguish requirements. This work then explores the application of BERT’s representations, known as embeddings, to identify similar requirements and predict requirement change.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":"6 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134993750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vispi Karkaria, Jie Chen, Chase Siuta, Damien Lim, Robert Radelescu, Wei Chen
Abstract In the commercial freight industry, tire retreading decisions are often conservative due to limited knowledge of a tire’s remaining service life. This practice leads to increased costs and material waste. This paper proposes a machine learning–based approach for estimating tire casing life and retreadability, focusing on usage data rather than wear information. This approach could extend the tire’s lifespan and reduce landfill waste. Data integration from diverse tire casing measurement sources presents challenges, including imbalanced removal data. Our methodology addresses these challenges by using historical inspection, telematics, and finite element modeling (FEM) datasets. We introduce “Tire Casing Energy” as a comprehensive usage input and apply a Variance-Reduction Synthetic Minority Oversampling Technique (VR-SMOTE) for data imbalance rectification. A random forest model is used to estimate the state of the tire casing and the casing removal probability, with Bayesian optimization applied for hyperparameter tuning, enhancing model accuracy. The proposed prediction framework is able to differentiate different truck fleets and tire locations based on their usage parameters. With the aid of this machine learning model, the importance and sensitivity of different tire usage parameters can be obtained, which is beneficial to maximize tire life.
{"title":"A MACHINE LEARNING BASED TIRE LIFE PREDICTION FRAMEWORK FOR INCREASING LIFE OF COMMERCIAL VEHICLE TIRES","authors":"Vispi Karkaria, Jie Chen, Chase Siuta, Damien Lim, Robert Radelescu, Wei Chen","doi":"10.1115/1.4063761","DOIUrl":"https://doi.org/10.1115/1.4063761","url":null,"abstract":"Abstract In the commercial freight industry, tire retreading decisions are often conservative due to limited knowledge of a tire’s remaining service life. This practice leads to increased costs and material waste. This paper proposes a machine learning–based approach for estimating tire casing life and retreadability, focusing on usage data rather than wear information. This approach could extend the tire’s lifespan and reduce landfill waste. Data integration from diverse tire casing measurement sources presents challenges, including imbalanced removal data. Our methodology addresses these challenges by using historical inspection, telematics, and finite element modeling (FEM) datasets. We introduce “Tire Casing Energy” as a comprehensive usage input and apply a Variance-Reduction Synthetic Minority Oversampling Technique (VR-SMOTE) for data imbalance rectification. A random forest model is used to estimate the state of the tire casing and the casing removal probability, with Bayesian optimization applied for hyperparameter tuning, enhancing model accuracy. The proposed prediction framework is able to differentiate different truck fleets and tire locations based on their usage parameters. With the aid of this machine learning model, the importance and sensitivity of different tire usage parameters can be obtained, which is beneficial to maximize tire life.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":"6 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134992709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cole Jetton, Matthew I. Campbell, Christopher Hoyle
Abstract This paper develops a method to integrate user knowledge into the optimization process by simultaneously modelling feasible design space and optimizing an objective function. In engineering, feasible design space is a constraint similar to those in optimization problems. However, not all constraints can be explicitly written as mathematical functions. This includes manufacturing concerns, ergonomic issues, complex geometric considerations, or exploring material options for a particular application. There needs to be a way to integrate designer knowledge into the design process and, preferably, use that to guide an optimization problem. In this research, these constraints are modeled using classification surrogate models and incorporated with Bayesian optimization. By suggesting design options to a user and allowing them to box off areas of feasible and infeasible designs, the method models both the feasible design space and an objective function probability of new design targets that are more optimal and have a high probability of being feasible. This proposed method is first proven with test optimization problems to show viability then is extended to include user feedback. This paper shows that by allowing users to box off areas of feasible and infeasible designs, it can effectively guide the optimization process to a feasible solution.
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Abstract A novel intervention to increase water-conserving behavior was developed and tested. Behavior-change interventions range from information-based, where individuals have full control over whether they act on the provided information, to forcing/automation, where individuals have no control over the desired behavior. This study's intervention was devised to be more forceful than providing information alone, but unlike forcing/automation, still allows individuals to control whether they perform the desired behavior. While resource-conservation strategies tend to target resource intake, the studied intervention examines whether limiting resource outflow can in turn limit resource intake. Specific to water, this study explored whether reducing waste-water outflow, causing accumulation, can in turn reduce water inflow. Data was collected online using a simulation of handwashing at a sink, which had different sink-outflow rates. Amazon Mechanical Turk workers completed three randomly ordered handwashing simulations. Study participants (n=74) significantly reduced simulated consumption of water when it accumulated quickly in the sink (p<0.001). Participants reduced simulated water consumption, on average by 14% at lower outflow rates, as they decreased inflow rates to prevent sink overflow. In contrast to informational interventions that rely on user motivation, reducing outflow significantly decreased simulated water usage, independent of participant-reported performance of other pro-environmental behaviors. Thus, reducing outflow may be effective regardless of individuals' motivation to act sustainably. Finally discussed is the value of online simulations to test pro-environmental behavior interventions. Potential directions for in-person testing are outlined as future work.
{"title":"Reducing Waste Outflow to Motivate Water Conservation","authors":"Sarah Halabieh, L.H. Shu","doi":"10.1115/1.4064042","DOIUrl":"https://doi.org/10.1115/1.4064042","url":null,"abstract":"Abstract A novel intervention to increase water-conserving behavior was developed and tested. Behavior-change interventions range from information-based, where individuals have full control over whether they act on the provided information, to forcing/automation, where individuals have no control over the desired behavior. This study's intervention was devised to be more forceful than providing information alone, but unlike forcing/automation, still allows individuals to control whether they perform the desired behavior. While resource-conservation strategies tend to target resource intake, the studied intervention examines whether limiting resource outflow can in turn limit resource intake. Specific to water, this study explored whether reducing waste-water outflow, causing accumulation, can in turn reduce water inflow. Data was collected online using a simulation of handwashing at a sink, which had different sink-outflow rates. Amazon Mechanical Turk workers completed three randomly ordered handwashing simulations. Study participants (n=74) significantly reduced simulated consumption of water when it accumulated quickly in the sink (p&lt;0.001). Participants reduced simulated water consumption, on average by 14% at lower outflow rates, as they decreased inflow rates to prevent sink overflow. In contrast to informational interventions that rely on user motivation, reducing outflow significantly decreased simulated water usage, independent of participant-reported performance of other pro-environmental behaviors. Thus, reducing outflow may be effective regardless of individuals' motivation to act sustainably. Finally discussed is the value of online simulations to test pro-environmental behavior interventions. Potential directions for in-person testing are outlined as future work.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":"100 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135391172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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