International Journal of Innovations in Engineering and Science最新文献_第10页

Effectiveness of Machine Learning & Deep Learning Models for Diabetes Prediction 机器学习和深度学习模型在糖尿病预测中的有效性

International Journal of Innovations in Engineering and Science Pub Date : 2023-04-19 DOI: 10.46335/ijies.2023.8.3.5

Priyabrata Sahu, J. K. Mantri

引用次数: 0

Age and Gender Prediction Using Deep Learning 使用深度学习预测年龄和性别

International Journal of Innovations in Engineering and Science Pub Date : 2023-04-19 DOI: 10.46335/ijies.2023.8.3.11

Shobhit Yadav, Ashutosh Somavanshi, Teertharaj Haldar, Prof. Rupali satpute

引用次数: 0

Review on Voice Based E-Mail Assistant For Visually Blind People 视障人士语音电子邮件助手研究进展

International Journal of Innovations in Engineering and Science Pub Date : 2023-04-01 DOI: 10.46335/ijies.2023.8.2.5

D. S. Golait, Sahil Chalkhure, Sagar Balamwar, Rohit Shinde, Rohit Turkar, S. Gaikwad

引用次数: 0

College Management Notes 书院管理须知

International Journal of Innovations in Engineering and Science Pub Date : 2023-04-01 DOI: 10.46335/ijies.2023.8.2.4

Sneha Golhar, Sonali Chirange, Prachi Deshpande, Prof.Abhishek Kumar

引用次数: 0

A Review on Design and Analysis of Industrial Ball Valve Using Computational Fluid Dynamics 工业球阀的计算流体动力学设计与分析综述

International Journal of Innovations in Engineering and Science Pub Date : 2023-04-01 DOI: 10.46335/ijies.2023.8.2.6

Mr. Harshal Rajesh Dorsatwar, D. P. Kadu

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引用次数: 0

A Review on Process Parameters of Additive Manufacturing 增材制造工艺参数研究进展

International Journal of Innovations in Engineering and Science Pub Date : 2023-04-01 DOI: 10.46335/ijies.2023.8.2.2

Mahesh Gonjari, R. Banpurkar

{"title":"A Review on Process Parameters of Additive Manufacturing","authors":"Mahesh Gonjari, R. Banpurkar","doi":"10.46335/ijies.2023.8.2.2","DOIUrl":"https://doi.org/10.46335/ijies.2023.8.2.2","url":null,"abstract":"– The importance of dimensional accuracy in produced models has been highlighted by the use of FDM technology for prototyping in industries such as aerospace and medical. Several process factors, such as layer thickness, raster width, infill pattern, etc., can impact the dimensional accuracy of FDM-printed objects. The goal of this research is to conduct a systematic literature review of studies that examined the impact of process parameters on the dimensional accuracy of FDM printed parts. This will allow us to better understand the effect of each parameter individually and to find the optimal levels of each parameter based on the material types. The effects of layer thickness, extrusion temperature, and component orientation on common materials like ABS and PLA were outlined, along with a review of 29 related papers. Tables summarized the key findings from each study, revealing the optimum value for each process parameter and describing the articles' respective methods. Layer thickness levels between 0.1 and 0.2 millimetres are recommended for ABS and PLA parts, whereas higher layer thickness values are typically associated with greater precision for ASA and Nylon parts. The extrusion temperature is determined to be optimally low, and this parameter is also less sensitive to variations in the material being used. With regards to part orientation, it has been determined that 0 degrees is best for ABS printed parts while 90Ois best for PLA printed parts. Furthermore, additional factors like the geometry of the part, the type of resin, and the varying dimensions of the part are likely to affect the ideal level of each process parameter. It is important to account for the impact of confounding variables when trying to understand the effect of each process parameter on the dimensional accuracy of FDM printed items.","PeriodicalId":286065,"journal":{"name":"International Journal of Innovations in Engineering and Science","volume":"250 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122152266","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

IOT Based Fruit Cold Storage Monitoring and Controlling System 基于物联网的水果冷库监控系统

International Journal of Innovations in Engineering and Science Pub Date : 2023-04-01 DOI: 10.46335/ijies.2023.8.2.1

Minakshi Dobale, Prof. Rahul Bhandekar, Prof. Rupali Dasarwa, P. S. Shelke

引用次数: 0

Analysis of Car Selling Prediction Based On AIML 基于AIML的汽车销售预测分析

International Journal of Innovations in Engineering and Science Pub Date : 2023-04-01 DOI: 10.46335/ijies.2023.8.2.3

Kale Dnyaneshwar, Jabhade Tushar, Kangude Shivam, Thamke Sagar

引用次数: 0

Review on Credit Card Fraud Detection System using Machine Learning 基于机器学习的信用卡欺诈检测系统综述

International Journal of Innovations in Engineering and Science Pub Date : 2023-03-14 DOI: 10.46335/ijies.2023.8.1.5

D. S. Golait, Ruthwick S. Masidkar, Kunal S. Khobragade, Prerna S. Bhanarkar, Purva Ganeshkar, Prishita Ganeshkar

引用次数: 0

Automatic Number Plate Recognition (ANPR) 自动车牌识别(ANPR)

International Journal of Innovations in Engineering and Science Pub Date : 2023-03-14 DOI: 10.46335/ijies.2023.8.1.4

引用次数: 18