Academic Journal of Manufacturing Engineering最新文献_第3页

A Comparative Study on Prediction of Cutting Force using Artificial Neural Network and Genetic Algorithm during Machining of Ti-6Al-4V 人工神经网络与遗传算法在Ti-6Al-4V切削力预测中的对比研究

Academic Journal of Manufacturing Engineering Pub Date : 2022-09-01 DOI: 10.37255/jme.v17i3pp091-097

Rolvin Barreto, Malagi R R, Chougula S R

{"title":"A Comparative Study on Prediction of Cutting Force using Artificial Neural Network and Genetic Algorithm during Machining of Ti-6Al-4V","authors":"Rolvin Barreto, Malagi R R, Chougula S R","doi":"10.37255/jme.v17i3pp091-097","DOIUrl":"https://doi.org/10.37255/jme.v17i3pp091-097","url":null,"abstract":"The purpose of this comparative study is to improve the predictive accuracy of the cutting force during the turning of Ti-6Al-4V on a lathe machine. By optimizing the machining process parameters such as cutting speed, feed rate, and depth of cut, the cutting force in the machining process can be improved significantly. Cutting force is one of the crucial characteristics that must be monitored during the cutting process in order to enhance tool life and the surface finish of the workpiece. This paper is based on the experimental dataset of cutting forces collected during the turning of titanium alloy under the Minimum Quantity Lubrication (MQL) condition. To predict the cutting forces, two machine learning techniques are explored. Firstly, a black-box model called an Artificial Neural Network (ANN) is proposed to predict cutting force. Using the Levenberg-Marquardt algorithm, a two-layered feedforward neural network is built in MATLAB to predict cutting force. The second model to be implemented was the Genetic Algorithm (GA), a white-box model. GA is an optimization technique which is based on Darwinian theories. It is a probabilistic method of searching, unlike most other search algorithms, which require definite inputs. Using symbolic regression in HeuristicLab, a GA model is developed to estimate cutting force. The anticipated values of cutting forces for both models were compared. Since the ANN model had fewer errors, it was ascertained that the particular model is preferable for machining process optimization.","PeriodicalId":38895,"journal":{"name":"Academic Journal of Manufacturing Engineering","volume":"68 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90456956","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

Influence of Plasma Gas Flow Rate on the Mechanical and Microstructural Aspects of Plasma Arc Welded Titanium Alloy Joints 等离子体气体流量对等离子弧焊钛合金接头力学和显微组织的影响

Academic Journal of Manufacturing Engineering Pub Date : 2022-09-01 DOI: 10.37255/jme.v17i3pp080-086

P. T, R. S, B. V

引用次数: 1

Structural and Thermal Properties of a Selected Host Crystal Lattice: Exploration of Inherent Possibilities 一个选定的主晶格的结构和热性质:内在可能性的探索

Academic Journal of Manufacturing Engineering Pub Date : 2022-09-01 DOI: 10.37255/jme.v17i3pp104-110

Sanjay Kumar Dubey, Shashank sharma

引用次数: 0

Design and Analysis for Heat Transfer Through Twisted Tape with Nanoparticles 纳米颗粒缠绕带传热设计与分析

Academic Journal of Manufacturing Engineering Pub Date : 2022-09-01 DOI: 10.37255/jme.v17i3pp087-090

Subravel V, Chandrasekar V

引用次数: 0

Prediction of Weld Strength in Power Ultrasonic Spot Welding Process Using Artificial Neural Network (ANN) and Back Propagation method 基于人工神经网络和反向传播方法的功率超声点焊焊缝强度预测

Academic Journal of Manufacturing Engineering Pub Date : 2022-07-04 DOI: 10.37255/jme.v17i4pp119-126

Ziad Al Sarraf

{"title":"Prediction of Weld Strength in Power Ultrasonic Spot Welding Process Using Artificial Neural Network (ANN) and Back Propagation method","authors":"Ziad Al Sarraf","doi":"10.37255/jme.v17i4pp119-126","DOIUrl":"https://doi.org/10.37255/jme.v17i4pp119-126","url":null,"abstract":"In this presented work, an Artificial Neural Network (ANN) connected with the backpropagation method was employed to predict the strength of joining materials that were carried out by using an ultrasonic spot welding process. The models created in this study were investigated, and their process parameters were analyzed. These parameters were classified and set as input variables like applying pressure, time of duration weld and trigger of vibrating amplitude. In contrast, the weld strength of joining dissimilar materials (Al-Cu) is set as output parameters. The identification from the process parameters is obtained using several experiments and finite element analyses based on prediction. The results of actual and numerical are accurate and reliable; however, their complexity has a significant effect due to being sensitivity to the condition variation of welding processes. Therefore, an efficient technique like an artificial neural network coupled with the backpropagation method is required to use the experiments as input data in the simulation of the ultrasonic welding process, finding the adequacy of the modeling process in the prediction of weld strength and to confirm the performance of using mathematical methods. The results of the selecting non-linear models show a noticeable potency when using ANN with a backpropagation method in providing high accuracy compared with other results obtained by conventional models.","PeriodicalId":38895,"journal":{"name":"Academic Journal of Manufacturing Engineering","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90304679","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

Recent Progress and Evolution in the Development of Non-Asbestos Based Automotive Brake Pad- A Review 无石棉汽车刹车片的研究进展及发展趋势

Academic Journal of Manufacturing Engineering Pub Date : 2022-06-01 DOI: 10.37255/jme.v17i2pp051-063

E. Ekpruke, C. Ossia, A. Big-Alabo

引用次数: 0

Analysis and Prediction of Working Range of Process Parameters for Surface Roughness of 3D Printed Parts with Fused Deposition Modelling 基于熔融沉积建模的3D打印零件表面粗糙度工艺参数工作范围分析与预测

Academic Journal of Manufacturing Engineering Pub Date : 2022-06-01 DOI: 10.37255/jme.v17i2pp044-050

Chinmay V. Sutar, Adish A. Mandavkar, Sairaj B Patil, Tejas U. Mohite, Tushar A. Patole, Sunil J. Raykar

引用次数: 0

Fabrication of Surface Metal Matrix Composite of AA7075 using Friction Stir Processing 搅拌摩擦法制备AA7075表面金属基复合材料

Academic Journal of Manufacturing Engineering Pub Date : 2022-06-01 DOI: 10.37255/jme.v17i2pp064-067

A. Md

{"title":"Fabrication of Surface Metal Matrix Composite of AA7075 using Friction Stir Processing","authors":"A. Md","doi":"10.37255/jme.v17i2pp064-067","DOIUrl":"https://doi.org/10.37255/jme.v17i2pp064-067","url":null,"abstract":"Friction stir processing (FSP), derived from the friction stir welding (FSW) process, is an emerging novel, green and energy-efficient processing technique to fabricate surface composite. The FSP technique has been used in the present investigation to fabricate surface composites, using Aluminium Alloy 7075 as parent metal and Titanium Dioxide and Silicon Carbide powder particles as reinforcement. Aluminium Alloy 7075 has been selected as the matrix phase, as being widely used by the automotive and aerospace application and has the highest strength among all commercial Al alloys. The present work details the fabrication of surface composites using various reinforcement combinations like AA7075- TiO2, AA7075- and AA7075- SiC, TiO2+SiC at constant tool rotation, tool travel speed and the number of passes have been discussed. The same being intended to improve hardness and thereby wear resistance. The fabricated surface composites are examined for microstructure using an image analyzer and found friction stir processed zone with fine microstructure than the base material. It is also observed that the average hardness of friction stir processed surface composite was higher than that of parent metal. Wear Resistance is found to be improved compared to the parent metal. It is found that Tensile strength is also enhanced than the base material.","PeriodicalId":38895,"journal":{"name":"Academic Journal of Manufacturing Engineering","volume":"21 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72412242","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

Physical Characteristics of Ternary Blends of Biodiesel 生物柴油三元共混物的物理特性

Academic Journal of Manufacturing Engineering Pub Date : 2022-06-01 DOI: 10.37255/jme.v17i2pp068-072

A. V., S. K

{"title":"Physical Characteristics of Ternary Blends of Biodiesel","authors":"A. V., S. K","doi":"10.37255/jme.v17i2pp068-072","DOIUrl":"https://doi.org/10.37255/jme.v17i2pp068-072","url":null,"abstract":"Fossil fuels are being gradually exhausted and need to go to new energy options. The vegetable oils are significant resources for biodiesel production and the best alternative for diesel from crude oil. This research aims to study the physical characteristics of diesel in combination with linseed oil, waste cooking oil and rubber seed oil as ternary blend biodiesels. Ternary blends mean a combination of diesel, biodiesel-1 and biodiesel-2. Four ternary blends have been prepared in various proportions from linseed and rubber seed oil, and another four ternary blends have been prepared from linseed and waste cooking oil. These three oils have relatively similar physical characteristics, non-edible. Physical characteristics tests were carried out using ternary biodiesel mixtures. The experimental study has shown the physical characteristics of the ternary blend by comparing the blends' kinematic viscosity, density, flash point and fire point. The blend of 95% diesel, 2.5% linseed and 2.5% rubber seed biodiesel gives better physical characteristics. By analysing the graph, the particular blends give similar physical characteristics to diesel. So the blend of linseed and rubber seed oil gives the best physical characteristics compared to other blends. It has lower viscosity values, nearly the same as diesel. So it does not affect the performance of an engine.","PeriodicalId":38895,"journal":{"name":"Academic Journal of Manufacturing Engineering","volume":"79 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72704319","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

Effect of Holding Time on Bonding Strength and Joint Interface Microstructure of Vacuum Diffusion Bonded Dissimilar Austenitic Stainless Steel - Titanium Alloy Joints 保温时间对真空扩散焊异种奥氏体不锈钢-钛合金接头结合强度和界面组织的影响

Academic Journal of Manufacturing Engineering Pub Date : 2022-06-01 DOI: 10.37255/jme.v17i2pp073-078

A. Negemiya, Tushar Sonar, Rajakumar Selvarajan

引用次数: 0