Proceedings of international conference on intelligent manufacturing and automation ICIMA 2018 ( TQL)

Lecture Notes in Mechanical Engineering Hari Vasudevan Vijaya Kumar N Kottur Amool A Raina Editors Proceedings of International Conference on Intelligent Manufacturing and Automation ICIMA 2018 Lecture Notes in Mechanical Engineering Lecture Notes in Mechanical Engineering (LNME) publishes the latest developments in Mechanical Engineering—quickly, informally and with high quality Original research reported in proceedings and post-proceedings represents the core of LNME Also considered for publication are monographs, contributed volumes and lecture notes of exceptionally high quality and interest Volumes published in LNME embrace all aspects, subfields and new challenges of mechanical engineering Topics in the series include: • • • • • • • • • • • • • • • • • Engineering Design Machinery and Machine Elements Mechanical Structures and Stress Analysis Automotive Engineering Engine Technology Aerospace Technology and Astronautics Nanotechnology and Microengineering Control, Robotics, Mechatronics MEMS Theoretical and Applied Mechanics Dynamical Systems, Control Fluid Mechanics Engineering Thermodynamics, Heat and Mass Transfer Manufacturing Precision Engineering, Instrumentation, Measurement Materials Engineering Tribology and Surface Technology More information about this series at http://www.springer.com/series/11236 Hari Vasudevan Vijaya Kumar N Kottur Amool A Raina • Editors Proceedings of International Conference on Intelligent Manufacturing and Automation ICIMA 2018 123 Editors Hari Vasudevan Department of Production Engineering Dwarkadas J Sanghvi College of Engineering Mumbai, Maharashtra, India Amool A Raina Aerospace Group, Institute of Textile Technology RWTH Aachen University Aachen, Germany Vijaya Kumar N Kottur Department of Mechanical Engineering Dwarkadas J Sanghvi College of Engineering Mumbai, Maharashtra, India ISSN 2195-4356 ISSN 2195-4364 (electronic) Lecture Notes in Mechanical Engineering ISBN 978-981-13-2489-5 ISBN 978-981-13-2490-1 (eBook) https://doi.org/10.1007/978-981-13-2490-1 Library of Congress Control Number: 2018954026 © Springer Nature Singapore Pte Ltd 2019 This work is subject to copyright All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed The use of general descriptive names, registered names, trademarks, service marks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore Preface This is an exciting time for business and wealth creation in the fields of manufacturing and automation It is the time when industries are looking up to manufacturing and automation engineers for their assistance in increasing the overall productivity in their organisation It is also the time when the governments across the globe have started to focus more on the manufacturing sector The International Conference on Intelligent Manufacturing and Automation 2018 (ICIMA 2018) was therefore designed to encourage discussions and research on advancements and applications in the areas of manufacturing and automation The primary focus of this conference was to bring together academicians, researchers and scientists for knowledge sharing in various areas of manufacturing, automation and other allied domains The conference covered topics encompassing automation, mechatronics, robotics, manufacturing processes, management and other related areas such as product design and development, green manufacturing and smart materials with the objective of brainstorming and emphasising upon the applications in the field of intelligent manufacturing and automation The response to call for papers was overwhelming with 109 full papers being submitted, covering a wide spectrum of topics related to the theme of the conference We express our sincere appreciation to the authors for their contribution to this book We would also like to express our sincere gratitude to all the experts and referees for their valuable comments and support extended during the review process Mumbai, India Mumbai, India Aachen, Germany Hari Vasudevan Vijaya Kumar N Kottur Amool A Raina v ICIMA 2018 Patrons Shri Amrish R Patel, Chief Patron, President, SVKM Shri Bhupesh R Patel, Joint President, SVKM Shri Bharat M Sanghvi, Vice President and Trustee, SVKM, and Chairman, Governing Council, DJSCE Shri Sunandan R Divatia, Hon Secretary, SVKM Shri Utpal H Bhayani, Hon Treasurer, SVKM Shri Shalin S Divatia, Hon Joint Secretary, SVKM Shri Jayant P Gandhi, Hon Joint Secretary, SVKM Shri Harshad H Shah, Hon Joint Treasurer, SVKM Shri Harit H Chitalia, Hon Joint Treasurer, SVKM International Advisory Committee Dr Dr Dr Dr Dr Dr Amit S Jariwala, Georgia Institute of Technology, USA Huynh T Luong, Asian Institute of Technology, Thailand Raghu Echempati, Kettering University, USA Amool Raina, RWTH Aachen University, Germany Rohan A Shirwaiker, North Carolina State University, USA Iris V Rivero, Iowa State University, USA National Advisory Committee Dr S K Ukarande, Dean, Faculty of Science and Technology, University of Mumbai Dr S S Mantha, Former Chairman, AICTE, New Delhi Dr S K Mahajan, Joint Director, DTE, Maharashtra Dr S M Khot, FCRIT, Navi Mumbai Dr L Ganapathy, NITIE, Mumbai Dr S G Deshmukh, ABV-IIITM, Gwalior Dr K P Karunakaran, IIT Bombay Dr K Maddulety, NITIE, Mumbai vii viii ICIMA 2018 Dr Suhas S Joshi, IIT Bombay Dr Tushar Desai, NIT Surat Dr V R Kalamkar, VNIT Nagpur Dr P Sakthivel, VIT Vellore Mr C M Venkateswaran, Aker Solutions Organising Committee Dr Hari Vasudevan, General Chair, Convener and Principal, DJSCE Dr A C Daptardar, General Co-Chair, Vice Principal (Admin.), DJSCE Dr M J Godse, General Co-Chair, Vice Principal (Acad.), DJSCE Dr Vijaya Kumar N Kottur, Joint Convener, Professor and Head, Department of Mechanical Engineering, DJSCE Mr Rajendra S Khavekar, Co-Convener, Training and Placement Officer, DJSCE Members Dr Atul Dhale Dr Sanjeev Thool Mr E Narayanan Mr Sandeep R Vaity Mr Prasad S Shirodkar Mr Vyankatesh U Bagal Mr P Frank Crasta Mr Prashant P Patankar Mr Rajnarayan M Yadav Mr Bronin Cyriac Mr Greegory Mathew Mr Dharam V Ranka Mrs Meeta N Gandhi Mr Amit Chaudhari Mr Ramesh Rajguru Mrs Trupti Markose Mr Rohit K Chaurasia Mr Mehul S Prajapati Mr Vinit R Katira Mr Pavan R Rayar Mr Kartik M Ajugia Mr Dhaval J Birajdar Mr Sandip H Mane Mr Sanket D Parab Mr Shashikant M Auti Mr Dhananjay Shukla Mr Ravikant Hattale About This Book This volume comprises the best-selected papers presented at the International Conference on Intelligent Manufacturing and Automation, which was organised by the Departments of Mechanical Engineering and Production Engineering of Dwarkadas J Sanghvi College of Engineering The volume focuses on narrowed topics of automation, mechatronics, robotics, CAD/CAM/CAE/CIM/FMS in manufacturing, product design and development, DFM/DFA/FMEA, MEMS and nanotechnology, rapid prototyping, computational techniques, industrial engineering, manufacturing process management, modelling and optimisation techniques, CRM, MRP and ERP, logistics and supply chain management, quality assurance and environment protection, advanced materials processing and characterisation and composites and smart materials The papers are divided into four main domains like design, advanced materials, manufacturing and automation We expect the articles, being published in the book, would contribute to and reinvigorate the overall efforts in enhancing manufacturing productivity across various sectors The content of the book is also expected to be helpful for postgraduate and doctoral students in their efforts to enhance the research outcome of their studies ix Contents Part I Design Mathematical Modeling and Optimization of Process Parameters for Tensile Strength and Nugget Diameter in Resistance Spot Welding of HR E-34 Steel Sheet Joint B S Gawai, R L Karwande, Md Irfan and Prafull S Thakare Numerical Simulation Over Conical Aerospike at Mach Rahul S Pawar, N R Gilke and Vivek P Warade 15 Multi-characteristics Optimization in the Turning of GFRP Composites Based on Grey-Taguchi Method Hari Vasudevan, Ramesh Rajguru and Kalpesh Tank 27 Vibrational Analysis of Single-Point Cutting Tool for Different Tool Material and Nose Radius Using Design of Experiment C M Choudhari, I A Bhisti, M G Choudhary and A H Mistry 35 Design of Automated Two-Wheeled Forklift with Retracting Third Wheel and Dynamic Counterbalance Mechanism Abhinav Kshirsagar, Neha Kesarkar and N S Chandrashekhar 47 Design and Analysis of Piercing and Extrusion Tool Gopal B Mudholkar, Girish M Lonare and Sadhana R Hivre 55 Design and Analysis of Coaxial Rotor Wind Turbine Sachin Manohar Shinde, Mohit Chaudhari, Tejas Jeurkar, Sanket Kadam and Kiran B Salunkhe 69 Parametric Optimization of MIG Welding on IS 1079 HR by Taguchi Method Mayur D Jagtap and Niyati Raut 81 xi Condition Monitoring of Rolling… Fig Frequency spectrum at 750 RPM Fig Frequency spectrum at 1300 RPM Fig 10 Frequency spectrum at 750 RPM 707 708 A Nadar and R Sangam Fig 11 Frequency spectrum at 750 RPM Table Comparison between experimental and theoretical frequency RPM Hz Fault frequencies calculated Fault frequencies calculated theoretically experimentally 750 1300 1450 3.2.3 12.525 21.71 24.07 BPFI BPFO BPF BPFI BPFO BPF 68.01075 117.8853 130.7001 44.589 77.2876 85.6892 37.8255 5.5642 72.6914 68 118 129 44 76 85 37 64 72 Frequency Calculated for Ball Race Fault Using LabVIEW See Fig 11 3.2.4 Comparison Between Experimental and Theoretical Frequency Table shows the different value of frequency for different type of fault at three different speeds The experimental values are corresponding to the theoretical fault frequencies From the above table, we can say that bearing has inner-race, outerrace, and ball faults (Table 6) Conclusion The FFT analysis can be done using LabVIEW software The kurtosis value for bearing is more than which shows the bearing is faulty The kurtosis value for different speed does not vary much which indicates that it does not change with increase in speed Experimental test results clearly show the bearing is faulty as the peak frequency in frequency spectrum is closer to the characteristics fault frequencies Condition Monitoring of Rolling… 709 Experimental result shows that frequency response analysis is useful for finding out the location of defect MAX4466 sensor is useful for condition monitoring and fault identification of a deep groove ball bearing References M Entezami, E Stewart, J Tutcher, W Driscoll, R Ellis, G Yeo, Z Zhang, C Roberts, T Kono and S Bayram “Acoustic Analysis Techniques for Condition Monitoring of Roller Bearings” ©2014 P K Kankar, Satish C Sharma, S P Harsha “Fault Diagnosis of High Speed Rolling Element Bearings Due to Localized Defects Using Response Surface Method” AkhandRai, S.H Upadhyay “A review of signal processing techniques utilized in the fault diagnosis of rolling element bearings.” Tribology International 96 (2016) 289–306 K Raghavendra, Karabasanagouda.B.N “Frequency Response Analysis of Deep Groove Ball Bearing” (ISSN (Online): 2319–7064)(1) Surojit Poddar, MadanLal Chandravanshi “Ball Bearing Fault Detection Using Vibration Parameters” (IJERT) Vol Issue 12, December – 2013 IJERT ISSN: 2278-0181 VikramTalekar, Prof L S Dhamande “Condition Monitoring of Deep Groove Ball bearing using FFT Analyzer” (IJERT) ISSN: 2278-0181 IJERTV4IS040367 Vol Issue 04, April2015) Brandon Van Hecke, Jae Yoon, David He ”Low speed bearing fault diagnosis using acoustic emission sensors” © 2016 Published by Elsevier Ltd Mr Mahesh S Kale, Mr Vishal, P Hegana “Vibration Study of Deep Groove Ball Bearing by Considering Single and Multiple Defects In Races IJIRSE December 2016 SuatSardemirAdemầiỗek Vibration Analysis of Rolling Element Bearings Defects” Journal of Applied Research and Technology · June 2014 10 AttelManjunath and D V Girish “Defect Detection In Deep Groove Polymer Ball Bearing Using Vibration Analysis” (IJMECH) Vol.2, No.3, August 2013 11 N Dhanush, G Dinesh, V Perumal, Mohammed Salman R NafeezAhmed L “Analysis of DeepGroove Ball Bearing using Vibrational Parameters” Volume 4, Special Issue 3, March 2015 Computational Modeling and Analysis of Artificial Flood Using Automata Nabamita Deb and Ashiya Noorie Abstract The study is the effort to analyze the parameters that cause artificial flood in urban areas Artificial flood is considered a major problem that a place affected by it faces, as it hampers day-to-day life and creates a lot health hazards among aged people and children The paper tries to contribute in the field of computation by considering the factors which result in an artificial flood A timed transition automaton is used to depict the behavior of different states over time when there is a heavy rainfall keeping in mind, the rate of cleansing velocity along with it Keywords Bharalu basin · Sediment · Timed transition automata Introduction Assam is occupying about 2.40% of India in landmass and covering an area of 78,438 km2 Guwahati is situated in between the foothills of the Meghalaya plateau and the southern bank of the river Brahmaputra 1.1 Climate of Guwahati The city of Guwahati enjoys a subtropical humid climate; summer from march to june, which is followed by monsoon season from july to august, which gives relief from the humidity of summer Autumn which succeeds monsoon is favorable for N Deb (B) · A Noorie Department of Information Technology, Gauhati University, Jalukbari, Guwahati, Assam, India e-mail: deb.nabamita@gmail.com A Noorie e-mail: ashiya.noorie10@gmail.com © Springer Nature Singapore Pte Ltd 2019 H Vasudevan et al (eds.), Proceedings of International Conference on Intelligent Manufacturing and Automation, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-13-2490-1_66 711 712 N Deb and A Noorie Fig Source ASDMA, By CGSD, Earth Institute, Columbia University, New York tourists having warm and moderate climate Winters also see light rainfall, and the mornings and afternoons remain moist and foggy Other than the various natural calamities that our country is facing like earthquakes, storms, floods and landslides, there is another most frequent problem which the urban population is confronting; it is the artificial flood ‘Artificial’—the name itself depicts that it is not the natural flood that occurs due to the sea-level rise Artificial flood hampers the lives of the urban areas tremendously causing severe damage to health and property Artificial flood may occur when there is a heavy rainfall for a short period, or even when there is a continuous rainfall for a longer period Due to which the water rises in the drains, canals and sewages; if the outlets of the drains and sewers are not open or clean, then the water gets blocked and overflows over the area hence causing an artificial flood [1, 2] Figure shows the flood effect in the area of Zoo road in the city of Guwahati In this research, a major issue of urban flood is discussed; first of all, it is thoroughly studied and tried to compute the relation between the amount of rainfall and artificial flood by a mathematical calculation; which helps to depict, how much amount of rainfall and the associated duration of rainfall that may result in an artificial flood Modeling Techniques While talking about the natural hazards due to climate change, there are numerous disastrous events the globe has been facing from time to time Various modeling techniques and models are designed to cope up with the different types of natural disasters One of them is the global earthquake model (GEM)’s open-source software OpenQuake [7] It is a Web-based risk assessment platform which models, views, explores and manages earthquake risk; it has got five main calculators, each one has got its own contribution in the area of seismic risk assessment and mitigation One of the calculators of the OpenQuake engine is the probabilistic eventbased risk (PEB) which computes the probability of losses and loss statistics for the assets depending upon the probabilistic hazard The output is thus used to assess the aggregate losses of the collective assets The OpenQuake project was initiated by GEM [7] is an open standard for calculating and communicating earthquake risk worldwide Flood-inundation modeling using MIKE FLOOD [5] has been used to simulate the flood inundation for the year 2001, and the maximum simulated extent was then compared with the image obtained from the IRS-1D WiFS of the inundated area Computational Modeling and Analysis of Artificial … 713 Using the MIKE 11 software, the river flow for the delta region of Mahanadi river basin has been simulated and performed quite satisfactorily [5] Flood in the urban areas are due to many factors; improper design of the sewer system is one major factor The drainage system of Guwahati city during monsoon season cannot drain out floodwater The outlet of the drain is connected to the Bharalu river basin which is connected to the main river Brahmaputra A study [3] was conducted on artificial flood in Bharalu Basin, Assam focuses on— (i) The size of the particles in the various parts of the drain of Bharalu is investigated in detailed, and further, the self-cleansing velocity is analyzed (ii) An optimal management for proper controlling the flow of sediment and water from hilly watershed is considered after calculation The channel always carries suspended particles which either floats or suspends due to the flow of the drain water The design of the channel should be in such a way that no solid particles get deposited at the channel bed The minimum velocity required to remove the sedimentation and sewage water from the channel is called the self-cleansing velocity The study [3] highlights the self-cleansing velocity as, Vs 8K (Ss − 1)gds f where V s velocity of flow or self-cleansing velocity, K = characteristics of solid usually which is taken as 0.04–0.06, f Dancy’s coefficient of friction which is usually taken as 0.03, S s specific gravity of solid which is usually taken as 2.65 and d s effective grain size They [3] analyzed the cause of artificial flood which is examined by considering a part of the drainage system and silt samples that were collected from the channel bed, and the data was analyzed to determine the uniformity coefficient, coefficient of curvature and effective particle size of each sample Methodology The hourly rainfall data of Guwahati Airport area has been manually collected from the Regional Meteorological Center, Guwahati [6] for the period of 01-01-2016 to 30-06-2017 The data was then saved in a csv file with the given details: (i) (ii) (iii) (iv) Start time of the rainfall End time of the rainfall Total duration of rainfall, i.e., End time–Start time Amount of rainfall in millimeter 714 N Deb and A Noorie Since the data collected here is not considered for a long period of time though, it is kept in a csv file considering the fact that in the future, there might be a possibility of a longer period when there will be a large amount of rainfall data to analyze and study; it can be done at ease Thus, the rainfall data has been clustered into three different clusters with the help of Weka [9] as discussed below 2.1 Clustering of the Data It involves the following steps: (i) Amount of rainfall in mm, start_time of the rainfall, end_time of the rainfall, total duration of the rainfall is first of all saved in a comma-separated values (csv) file as shown in Fig (ii) Since the amount of data collected is not complex, here instead of grouping the data set manually into three different clusters it is decided to use simple K-means algorithm It is clustered using Euclidean distance (or similarity) function Here, all the data is clustered considering the mean of their value Thus from the rainfall data set, three clusters were formed—cluster0 for low rainfall, cluster1 for medium rainfall and cluster2 for heavy rainfall—using Waikato Environment for Knowledge Analysis (Weka), a software tool for data mining [9] Clusters are formed using the values of the attributes: Starting Time, Ending Time, Duration and Amt of rainfall; these clusters which are divided into three: low rainfall, medium rainfall and high rainfall; help us to examine the data which will be relevant for the study intended, since the region often faces rainfall, therefore sparse and low rainfall is not considered Fig The CSV file where the amount of rainfall data set is kept Computational Modeling and Analysis of Artificial … 715 Fig Three different clusters are shown in the colored dots Cluster results which can be visualized in the plot as cluster0 shown in blue dots for low rainfall, cluster1 shown in red dots for medium rainfall and cluster2 shown in green dots for excessive rainfall Figure shows the plot Final data after clustering is saved in.attribute-relation file format (arff) From this file, the relevant high rainfall, i.e., cluster2 can be retrieved for further calculation After the clustering is done, the next step is to design a model to find whether artificial flood (AF here onwards) is present or not Computational Model We use the rainfall data to check whether artificial flood occurs due to the heavy amount of rainfall or any other factor Here, we have considered AF to be dependent on the following factors: (i) Rise of water in the road (in meter) which is displaced due to the sediment in the Bharalu basin (ii) Amount of rainfall data (in meter) per unit time (in second) and (iii) Self-cleansing velocity V s (in m/s) in the basin AF thus can be calculated considering the following equation: AF where Vs 8K f Rise of water + (Ss − 1)gds [3] Amount of Rainfall (m) Time duration (sec) Vs 716 N Deb and A Noorie It is assumed that if the value of the AF is more than 0.5 m, then there is a possibility that the particular area is effected by artificial flood, or else not Results and Discussion After implementing the above equation in Java, we can insert the different values of the sediment which displaces the amount of water entering into the road and the low-lying areas In our study, the cause of artificial flood is examined by considering a part of the drainage system The drain is considered to be partially full, so when the sediment of a particular volume and height is deposited at the channel bed, then how much amount of water gets displaced from the river basin is calculated The self-cleansing velocity for the calculation varies between 0.7 and 1.2 m/s Considering a mathematical formula to calculate AF we see the following: Case I: AF Rise of water + Here, rise of water in the road Amount of Rainfall (m) Time duration (sec) Vs (m/s) displaced water from the basin due to the sediment Volume of the Sediment Thus, volume of the sediment (suppose) can be evaluated as Length 5.1 m Breadth Then, volume of the sediment 0.65 m 26.52 m3 Water in the road in meter Area of the Road m Height volume of the sediment area of the road Length × Breadth 5.1 × 10 51 m2 Therefore, water in the road in meter 0.52 m, amount of rainfall in meter 0.0052 m, time duration in sec 13500 s, self-cleansing velocity 0.7 m/s So, AF 0.52 m Therefore, the value of AF is larger than 0.5, so there is a chance of artificial flood in that area We have calculated the value of AF using different ranges of rainfall starting from low to high, but the result is not affected much as the artificial flood depends on the sediment, therefore we tried another case Computational Modeling and Analysis of Artificial … 717 Case II: AF Rainfall in m + Rate of Deposition of sediment in m/s Vs in m/s 8K where Vs − 1)gds [3] f (Ss Therefore, two examples are taken Example I: Considering rate of deposition of sediment 0.65 m/s Amount Of Rainfall in m 0.0073 m Self-Cleansing Velocity 0.7 m/s AF 0.935 m (big possibility of artificial flood) Example II: Now, when self-cleansing velocity is increased manually or mechanically by some suitable method Considering rate of deposition of sediment 0.65 m/s and amount of rainfall in m 0.0073 m Self-Cleansing velocity 1.2 m/s AF 0.548 m It is obvious that, when the self-cleansing velocity is increased the level of sediment in the drain basin should go down, here if we now consider rate of deposition of sediment 0.15 m/s, amount of rainfall in m 0.0073 m and self-cleansing velocity 1.2 m/s, therefore, AF 0.132 m 4.1 Program Code Program commands of the above mathematical calculation for AF is given as: program artificial flood (Output) Read rainfallDuration[] and rainfallAmount[] from the csv file Consider S_Cvelocity; Calculating volume of sediment, Vsediment; Considering the length l_sed, breadth b_sed and height of sediment h_sed; Vsediment:= l_sed*b_sed*h_sed; Calculating the dimension of the road,area_road; Considering length of the road l_road, breadth of the road b_road; Calculating the dimension of the road,area_road; Area_road:=l_road*b_road; Water in the road, water_level:=Vsediment/area_road; for i=0 to length(rainfallDuration[]); Af:=water_level+(((rainfallAmount[i]/1000)/(rainfallDuration[i]) )/S_Cvelocity); end of for loop Thus we see that the value of the artificial flood can be reduced when the selfcleansing velocity in the basin is increased A timed automaton over tuple A =( Q, T , I, F, X), where is a finite alphabet of actions, where Q is a finite set of states, and X a finite set of clocks, T ⊆ Q × [C(X ) × _ × 2X ] × Q is a finite set of transitions, I ⊆ Q is the subset of initial states and F ⊆ Q is the subset of final states [4] 718 N Deb and A Noorie Fig Processes are shown using Uppaal This process can be shown by using a timed transition automaton in Uppaal [8] Uppaal is a tool to model and simulate timed transition models Here the three different processes are considered each for sediment, artificial flood and self-cleansing velocity which are related to one another, and the transition from one state to another is time oriented In simple words, when there is an increase in rainfall, the bed of the drain basin is deposited with the sediment collectively with the rainfall water and the debris from the high level areas Resulting in the increase in the drain water which spread over the area creating artificial flood If the self-cleansing velocity is increased with the help of manual labor, the blockage in the drain can be cleared, and the natural flow of water in the basin can be generated The three process are ProcessSediment, ProcessArti_flood and ProcessCleansing are shown in Fig Each of the process are dependent on one another It is clear from the previous mathematical calculation for AF as the deposition of sediment at the bed of the basin plays the vital part in creating an artificial flood So during excessive rainfall when the sediment gets deposited along with the rain water, garbages, debris etc., it is required to increase the cleansing velocity of the drainage basin Conclusion Flood can be considered as the overflowing of water from a river at the adjacent land near to it spreading water all over the low-lying areas, whereas artificial flood or flash flood occurs suddenly due to two components for flood occurrences—one is rainfall intensity and the other is drainage area parameters It is not always the case that heavy rainfall will cause artificial flood, through this study it has been highlighted that artificial flooding and its causes also depend on certain features of the drainage Computational Modeling and Analysis of Artificial … 719 basin like the breadth and width of the basin, amount of accumulated sediments or debris flowing from the high level areas play a significant role in obstructing the normal flow of the water through the drainage basin In this research, we have tried to evaluate the factors resulting to an artificial flood due to the overflow of water from the drainage basin The paper depicts the different factors which play a vital role in creating artificial flood in the urban low-lying areas: i When there is a continuous rainfall measuring above 0.5 mm for a long duration of time ii The drainage basin through which the sewage water or the outlet flows, at the time of rainfall, should be free of sediment settled at the base iii And lastly during heavy rainfall, the self-cleansing velocity of the drainage basin should be increased to bring back the raised water level to a stable condition, or in other words, removing the blockage caused by the deposited sediment at the bed of the basin can be done by manually increasing the cleansing velocity resulting in the natural flow of rainfall water in the drainage basin In the future, if it is possible to detect the amount of sediment in the basin during the rainfall and create mechanical cleanser in the basin, it is possible to speculate the condition and might be possible to find out a clear solution Study is further going on to predict the artificial flood due to the deposition of sediments in the drainage basin by comparing the scenarios, considering with the help of a hybrid automata References AIDMI (All India Disaster Mitigation Institute) conducted by ASDMA In “Review of studies on Urban Floods in Guwahati from Flood Knowledge to Urban Action”, July, 2014 ASDMA, Govt of Assam, “Disaster Risk Reduction Including Climate Change Adaptation of Guwahati in Context of Dynamic Growth”, Columbia University, September, 2015 Barman P., Sarma B., Sarma A K., “A study on Flood Hazard Mitigation of Guwahati city”, The Asian Review of Civil Engineering, 2012-https://www.iwra.org/ Bouyer, P (2005) An introduction to timed automata Actes École d’été ETR’05, 79–94 Patro S., Chatterjee C., Mohanty S., Singh R & Raghuwanshi, N S.(2009).“Flood Inundation Modelling using MIKE FLOOD and Remote Sensing Data”, Journal of the Indian Society of Remote Sensing 37(1), 107–118 Regional Meteorological Center, Accessed Annual Rainfall Data, LGBI, Airport, Guwahati Silva, V., Crowley, H., Pagani, M., Monelli, D., & Pinho, R (2014) Development of the OpenQuake engine, the Global Earthquake Model’s open-source software for seismic risk assessment Natural Hazards, 72(3), 1409–1427 Uppaal Source: www.uppaal.org Weka, Waikato Environment for Knowledge Analysis Source: https://machinelearningmastery com/what-is-the-weka-machine-learning-workbench Author Index A Abhinav Kshirsagar, 47 Abhishek Gupta, 133 Aditya Sawant, 253 Ajinkya Netake, 253 Akash Mishra, 253 Akshay Kusneniwar, 239 Amit Choudhari, 133 Amit Kumar Patel, 89 Anand Balaji, 485 Anant Jhaveri, 161 Anish Nadar, 697 Anoop, K P., 623 Apurav Joshi, 389 Apurv Deshmukh, 389 Arun Alva, 401 Aryadutt, C S., 623 Ashish J Deshmukh, 593 Ashiya Noorie, 711 Atul D Dhale, 181 B Bhisti, I A., 35 Bhola Nagelia, 161 Bhushan T Patil, 463 Bysani Malakondaiah, 495 C Ch Joseph S Raju, 355 Chandramohana Reddy, B., 287, 379 Chandrashekhar, N S., 47, 171 Channamallikarjun S Mathpati, 161 Choudhari, C M., 35, 267, 689 Choudhary, M G., 35 Choukar, O R., 651 D Deepak Bondre, 389 Deokate, C R., 651 Devdatt Bhurke, 401 Dharmendra Choudhary, 689 Diwate, A D., 333 E Elroy Rodrigues, 615 F Farhan Sayed, 323 Francis J Emmatty, 229 Frank Crasta, 315 G Ganesh S Kadam, 473 Gawai, B S., Gayatri Malekar, 115 Geet Dave, 401 Gilke, N R., 15 Girish M Lonare, 55 Gopal B Mudholkar, 55 Gosar Vimal, 445 H Handa, C C., 125 Hari Vasudevan, 27, 301, 401, 413, 425, 445, 593, 601 Hrishikesh Pangarkar, 161 J Jadhav, S D., 195 Jaineel Desai, 413, 689 Jayakrishna, K., 583 © Springer Nature Singapore Pte Ltd 2019 H Vasudevan et al (eds.), Proceedings of International Conference on Intelligent Manufacturing and Automation, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-13-2490-1 721 722 Jayraj Ranade, 151 Jimit Shelat, 503 Joshi, S U., 195 K Kailas S Jagtap, 89 Kaival Rajesh Nayak, 565 Kale, V S., 195 Kalpesh Tank, 27 Kalra, V M., 551 Karwande, R L., Kavita Dhanawade, 389 Ketaki N Joshi, 463 Khavekar Rajendra, 445 Kiran B Salunkhe, 69 M Mahajan, S K., 267 Malleshappa T Bhagawati, 583 Malvika Sharma, 345 Manavalan, E., 583 Manohar Reddy Kunuthur, 287, 379 Mayur D Jagtap, 81 Md Irfan, Meet Shah, 661 Meeta Gandhi, 601 Megh Doshi, 661 Milan Kaklotar, 413 Mistry, A H., 35 Mitesh Parmar, 323 Mohit Chaudhari, 69 N Nabamita Deb, 711 Nandu Durge, 205 Narkhede, B E., 267 Neel Sanghvi, 315 Neha Kesarkar, 47 Nikhil S Divate, 89 Nimeshchandra S Patel, 99 Niti Doshi, 669 Nitin Panaskar, 253 Niyati Raut, 81, 115 O Onkar V Potadar, 473 P Pabla, B S., 551 Paramjit Thakur, 279 Parshva Mehta, 151 Parth Thakar, 151 Parth Thakkar, 485 Patel, H A., 195 Author Index Patil, S J., 651 Pavan Rayar, 133 Prabha Rastogi, 221 Prafull S Thakare, 125 Prasad V Thete, 531 Prashant T Borlepwar, 435, 511 Prathamesh Mohite, 615 Prathamesh Potdar, 239, 253 Praveen Kumar Loharkar, 345 Pravin S Misal, 171 Pullareddy Mekala, 287 R Raghuvanshi, F C., 369 Rahul Paliwal, 161 Rahul S Pawar, 15 Rajanarasimha Sangam, 697 Rajkumar P Narkhede, 221 Rajnarayan Yadav, 301 Ramesh Babu, P., 355 Ramesh R Lekurwale, 531, 575 Ramesh Rajguru, 27 Ramesh Rajguru, 301, 401, 413, 425 Ramnarayanan, R., 355 Ramzan Muhammad, 369 Ritwik Dhar, 669 Ronak D Gandhi, 99 Routh Rajesh, 631 Rushabh Mutha, 253 Rushank Sangani, 541 S Sachin Manohar Shinde, 69 Sachin Patel, 661 Sadhana R Hivre, 55 Samadhan Deshmukh, 679 Sandip M Salodkar, 125 Sandip Mane, 455 Sangeeta Bansode, 89 Sanidhya Mathur, 413 Sanjay Kumar, 455 Sanket Kadam, 69 Santosh Rane, 239 Shaishav M Jadav, 575 Shankar Mantha, 205 Shashikant Auti, 323, 565 Shivani Vartak, 615 Shivkumar Biradar, 435 Shlok Bhavsar, 689 Shreyans Jain, 413 Siddhesh Lad, 279 Srinivasa Rao, S., 631 Sugam Shivhare, 345 Suhrid Subramaniam, 661 Author Index Sunil Pagare, 181 Supriya Vyas, 345 Suraj L Gondhali, 181 Suryawanshi, A S., 651 Sushil Charpe, 369 Suyash Ail, 151 T Taha Kadaka, 133 Tejas Jeurkar, 69 Tejas Shinde, 389 Teli, S N., 195, 279 Thakare, P S., Thakre, S B., 333 Thakur Tilak, 551 Tushar Y Badgujar, 521 U Umesh Sable, 511 723 V Vaibhav S Narwane, 485 Venkateshwar Reddy, C., 355 Venkumar, P., 583 Vijay P Wani, 521 Vijaya Kumar N Kottur, 315, 541 Vikas Phalle, 205 Vikesh P Kumawat, 89 Vinay V Panicker, 229, 623 Vinil Punjani, 401 Vivek P Warade, 15 Vivek Sunnapwar, 679 Vivekanand Bagal, 345 W Wankhede, D M., 267 Y Yogita S Patil, 369 ... Editors Proceedings of International Conference on Intelligent Manufacturing and Automation ICIMA 2018 123 Editors Hari Vasudevan Department of Production Engineering Dwarkadas J Sanghvi College of. .. Automation 2018 (ICIMA 2018) was therefore designed to encourage discussions and research on advancements and applications in the areas of manufacturing and automation The primary focus of this conference. .. organisation It is also the time when the governments across the globe have started to focus more on the manufacturing sector The International Conference on Intelligent Manufacturing and Automation 2018