2021 IEEE 6th International Forum on Research and Technology for Society and Industry (RTSI)最新文献_第6页

Penetration impact behaviour of innovative 3d printing onyx/glass composite samples 创新3d打印玛瑙/玻璃复合材料样品的穿透冲击行为

2021 IEEE 6th International Forum on Research and Technology for Society and Industry (RTSI) Pub Date : 2021-09-06 DOI: 10.1109/rtsi50628.2021.9597317

I. Papa, Emanuele Manco, V. Lopresto, Claudio Cigliano, Alessandro Manzo, A. Silvestri, A. Squillace

{"title":"Penetration impact behaviour of innovative 3d printing onyx/glass composite samples","authors":"I. Papa, Emanuele Manco, V. Lopresto, Claudio Cigliano, Alessandro Manzo, A. Silvestri, A. Squillace","doi":"10.1109/rtsi50628.2021.9597317","DOIUrl":"https://doi.org/10.1109/rtsi50628.2021.9597317","url":null,"abstract":"Among the several additive manufacturing techniques, Fused Filament Fabrication (FFF) is a 3d printing technique fast, handly and low-cost to produce complex-shaped parts easily and quickly. FFF adds material layer by layer, saving energy, costs, raw material costs and waste. Nevertheless, Among the recent papers, analysis is absent on the impact performance of 3D printed composites. The response to low-velocity impact events is a crucial issue for composite materials, for the operational reliability of the element that has experienced a great reduction of mechanical strength due to their anisotropic nature. However, there is a lack of knowledge, mainly about the impact behaviour of 3d printing composite materials, also considering that the main demand comes from the transportation field (automotive, aircraft, etc.), where the impact event is always possible. Thus, the aim of this research is to begin to fill the knowledge gap investigating the impact behaviour of specimens obtained by continuous fiber fabrication technique. Onyx and Nylon white filaments were used as matrices and glass fibres for reinforcement. The present paper deals with the manufacturing of composite material items obtained by the Markforged FFF technique. An innovative matrix of nylon and micro carbon fibre (ONYX) was used with and without glass filament as reinforcement. Low velocity impact tests at penetration were carried out to study the impact performance of the novel material and the effect of the reinforcement used.","PeriodicalId":294628,"journal":{"name":"2021 IEEE 6th International Forum on Research and Technology for Society and Industry (RTSI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129196972","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}

引用次数: 1

Modeling, Simulation and Analysis of Bipolar Driven BLDC Motor 双极驱动无刷直流电机的建模、仿真与分析

2021 IEEE 6th International Forum on Research and Technology for Society and Industry (RTSI) Pub Date : 2021-09-06 DOI: 10.1109/rtsi50628.2021.9597275

U. Çeltekligil, Metin Çavdar

引用次数: 0

The University of Genoa Savona Campus Sustainability Projects 热那亚萨沃纳大学校园可持续发展项目

2021 IEEE 6th International Forum on Research and Technology for Society and Industry (RTSI) Pub Date : 2021-09-06 DOI: 10.1109/rtsi50628.2021.9597244

P. Laiolo, R. Procopio, F. Delfino, A. Andreotti, L. Angrisani

引用次数: 2

A Practical Account of Designing a Support Tool for an Educational Experience 为教育经验设计辅助工具的实践说明

2021 IEEE 6th International Forum on Research and Technology for Society and Industry (RTSI) Pub Date : 2021-09-06 DOI: 10.1109/rtsi50628.2021.9597284

Benedetta Bolis, Mauro Fama, Mirko Salaris, M. Santambrogio

引用次数: 0

On the effect of the layer thickness in Laser-Powder Bed Fusion of pre-mixed Incone1718-Cu powders 层厚对预混合Incone1718-Cu粉末激光床熔合的影响

2021 IEEE 6th International Forum on Research and Technology for Society and Industry (RTSI) Pub Date : 2021-09-06 DOI: 10.1109/rtsi50628.2021.9597295

A. E. Hassanin, F. Scherillo, Irene del Sol Illana, A. Caraviello, D. Borrelli, A. Astarita, A. Squillace

{"title":"On the effect of the layer thickness in Laser-Powder Bed Fusion of pre-mixed Incone1718-Cu powders","authors":"A. E. Hassanin, F. Scherillo, Irene del Sol Illana, A. Caraviello, D. Borrelli, A. Astarita, A. Squillace","doi":"10.1109/rtsi50628.2021.9597295","DOIUrl":"https://doi.org/10.1109/rtsi50628.2021.9597295","url":null,"abstract":"The customization of material characteristics in combination with extreme design flexibility represents the most intriguing scope of Additive Manufacturing. In this context, this work investigated the feasibility of the Laser-based Powder Bed Fusion process (L-PBF) of pre-mixed Inconel 718-Cu powder blends, exploring the effects of the laser-related process parameters and Cu addition, in order to develop alloys able to combine excellent mechanical properties under severe operating conditions with good thermal and electrical conductivity. More specifically, this work investigated the effects of using a layer thickness of $boldsymbol{30 mu mathrm{m}}$ on the process quality in terms of density, microhardness, microstructure and surface roughness. Results showed that cubic samples with 10 mm side were successfully produced, with a relative density always greater than 98 percent. The resulting microhardness was from 290 to 340 HV, decreasing as a function of the Cu addition and keyhole defects development. The latter were the main defects observed in the microstructure, characterized also by heterogeneous crystalline grains for all the investigated conditions. Finally, the surface roughness (Sa) was from 7 to $boldsymbol{25 mu mathrm{m}}$, in line with the typical values of L-PBF products.","PeriodicalId":294628,"journal":{"name":"2021 IEEE 6th International Forum on Research and Technology for Society and Industry (RTSI)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123187265","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}

引用次数: 0

Multi-Polarization Methods to Detect and Classify Burned Areas using Sentinel-1 Sar Data 基于Sentinel-1 Sar数据的烧伤区域多极化检测与分类方法

2021 IEEE 6th International Forum on Research and Technology for Society and Industry (RTSI) Pub Date : 2021-09-06 DOI: 10.1109/rtsi50628.2021.9597245

E. Ferrentino, F. Nunziata, A. Buono, M. Sarti, M. Migliaccio

引用次数: 0

Optimization of an artificial neural network to study accelerations of foetal heart rhythm 优化人工神经网络研究胎儿心律加速

2021 IEEE 6th International Forum on Research and Technology for Society and Industry (RTSI) Pub Date : 2021-09-06 DOI: 10.1109/rtsi50628.2021.9597213

A. M. Ponsiglione, G. Cesarelli, Francesco Amato, M. Romano

{"title":"Optimization of an artificial neural network to study accelerations of foetal heart rhythm","authors":"A. M. Ponsiglione, G. Cesarelli, Francesco Amato, M. Romano","doi":"10.1109/rtsi50628.2021.9597213","DOIUrl":"https://doi.org/10.1109/rtsi50628.2021.9597213","url":null,"abstract":"Given the importance of accelerations of the foetal health rate (FHR) in the monitoring of the foetal wellbeing along the course of the pregnancy, and taking into consideration the contribution of computerized analysis of biosignals as well as the emerging role of artificial intelligence in medicine, this study describes the optimization and use of artificial neural networks (ANNs) as a tool for predicting and investigating FHR accelerations. To this aim, nineteen features have been extracted from 187 FHR signals recorded from healthy women by cardiotocography. Three training methods, including Levenberg-Marquardt (LM), Scaled Conjugate Gradient (SCG), and Bayesian Regularization (BR), have been tested by training ANNs with increasing number of neurons in the hidden layer. The optimal network configuration has been selected by checking at the coefficient of determination (R2) and the Root Mean Square Error (RMSE). Results suggest that a proper ANN configuration not only enables maximizing the predictive capability of the selected model but, mostly, could be helpful in investigating the influence of linear and nonlinear indices of the FHR variability (FHRV) on the total number of accelerations in the foetal heart rhythm.","PeriodicalId":294628,"journal":{"name":"2021 IEEE 6th International Forum on Research and Technology for Society and Industry (RTSI)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129876269","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}

引用次数: 18

Assessment of navigation capabilities of Mini Cheetah robot for monitoring of landslide terrains 小型猎豹滑坡地形监测机器人导航能力评估

2021 IEEE 6th International Forum on Research and Technology for Society and Industry (RTSI) Pub Date : 2021-09-06 DOI: 10.1109/rtsi50628.2021.9597335

P. Arena, F. D. Pietro, Alessia Li Noce, S. Taffara, L. Patané

引用次数: 6

Comparison of Intelligent Approaches for Cycle Time Prediction in Injection Moulding of a Medical Device Product 医疗器械产品注射成型周期预测智能方法的比较

2021 IEEE 6th International Forum on Research and Technology for Society and Industry (RTSI) Pub Date : 2021-09-06 DOI: 10.1109/rtsi50628.2021.9597254

Mandana Kariminejad, D. Tormey, Saif Huq, Jim Morrison, M. McAfee

{"title":"Comparison of Intelligent Approaches for Cycle Time Prediction in Injection Moulding of a Medical Device Product","authors":"Mandana Kariminejad, D. Tormey, Saif Huq, Jim Morrison, M. McAfee","doi":"10.1109/rtsi50628.2021.9597254","DOIUrl":"https://doi.org/10.1109/rtsi50628.2021.9597254","url":null,"abstract":"Injection moulding is an increasingly automated industrial process, particularly when used for the production of high-value precision components such as polymeric medical devices. In such applications, achieving stringent product quality demands whilst also ensuring a highly efficient process can be challenging. Cycle time is one of the most critical factors which directly affects the throughput rate of the process and hence is a key indicator of process efficiency. In this work, we examine a production data set from a real industrial injection moulding process for manufacture of a high precision medical device. The relationship between the process input variables and the resulting cycle time is mapped with an artificial neural network (ANN) and an adaptive neuro-fuzzy system (ANFIS). The predictive performance of different training methods and neuron numbers in ANN and the impact of model type and the numbers of membership functions in ANFIS has been investigated. The strengths and limitations of the approaches are presented and the further research and development needed to ensure practical on-line use of these methods for dynamic process optimisation in the industrial process are discussed.","PeriodicalId":294628,"journal":{"name":"2021 IEEE 6th International Forum on Research and Technology for Society and Industry (RTSI)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126097182","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}

引用次数: 0

Prediction of attitudes towards human-centred cognitive vehicles aware of their users' routines and preferences 预测对以人为中心的认知车辆的态度，了解其用户的惯例和偏好

2021 IEEE 6th International Forum on Research and Technology for Society and Industry (RTSI) Pub Date : 2021-09-06 DOI: 10.1109/rtsi50628.2021.9597265

Ankit R. Patel, Flora Ferreira, Sérgio Monteiro, Ana Carolina Silva, W. Erlhagen, E. Bicho

{"title":"Prediction of attitudes towards human-centred cognitive vehicles aware of their users' routines and preferences","authors":"Ankit R. Patel, Flora Ferreira, Sérgio Monteiro, Ana Carolina Silva, W. Erlhagen, E. Bicho","doi":"10.1109/rtsi50628.2021.9597265","DOIUrl":"https://doi.org/10.1109/rtsi50628.2021.9597265","url":null,"abstract":"Advances in the automotive industry are changing the relationship between cars and drivers. Advanced driver assistant systems, such as navigation systems, advanced cruise control, collision avoidance systems, and other safety systems, are now common and assist the driver in controlling the car. Smart key fobs have made getting in and starting the car almost effortless: the fob can be left in the pocket and the doors will unlock when a driver/user approaches the car and simply touches the door handle. This is a level of personalization and convenience that is almost standard today. The research presented here brings a new perspective on personalization and driver assistance systems. An online survey was conducted, which aimed to gather public opinion on the usefulness of endowing future (semi-)autonomous cars with social and cognitive behavior, such as the ability to learn drivers' routines and preferences in order to make decisions and perform actions in preparation for the next trip and to manage comfort within the cockpit without being commanded to do so. After filtering, the study included 657 respondents from 93 nations. The results demonstrate a favorable opinion towards such human-centered cognitive vehicles and could be helpful for designers in the automotive industry and other related stakeholders in the development of future cognitive vehicles.","PeriodicalId":294628,"journal":{"name":"2021 IEEE 6th International Forum on Research and Technology for Society and Industry (RTSI)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127484057","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}

引用次数: 0