scholarly journals Optimization of advanced manufacturing processes using socio inspired cohort intelligence algorithm

2022 ◽  
Vol 13 ◽  
pp. 6
Author(s):  
Ishaan R. Kale ◽  
Mayur A. Pachpande ◽  
Swapnil P. Naikwadi ◽  
Mayur N. Narkhede
Keyword(s):  
Process Parameters ◽  
Removal Rate ◽  
Harmony Search ◽  
Optimization Techniques ◽  
Technological Advancement ◽  
Machining Processes ◽  
Bee Colony ◽  
Optimization Of Process Parameters ◽  
Advanced Machining ◽  
Linear And Nonlinear Constraints

The demand of Advanced Machining Processes (AMP) is continuously increasing owing to the technological advancement. The problems based on AMP are complex in nature as it consisted of parameters which are interdependent. These problems also consisted of linear and nonlinear constraints. This makes the problem complex which may not be solved using traditional optimization techniques. The optimization of process parameters is indispensable to use AMP's at its aptness and to make it economical to use. This paper states the optimization of process parameters of Ultrasonic machining (USM) and Abrasive water jet machining (AWJM) processes to maximize the Material Removal Rate (MRR) using a socio inspired Cohort Intelligent (CI) algorithm. The constraints involved with these problems are handled using static penalty function approach. The solutions are compared with other contemporary techniques such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Modified Harmony Search (HS_M) and Genetic Algorithm (GA).

scholarly journals Optimization of Process Parameters During Electrochemical Machining

2019 ◽  
Vol 8 (12) ◽  
pp. 2683-2687
Keyword(s):  
Process Parameters ◽  
Removal Rate ◽  
Research Work ◽  
Electrochemical Machining ◽  
Machining Processes ◽  
Electrode Gap ◽  
Optimization Of Process Parameters ◽  
Finishing Process ◽  
Nimonic 80A ◽  
The Right

Electrochemical machining is one of the most efficient machining processes due to its ability to produce completely stress-free machined components without any need of further finishing process. However, the right understanding of the effects of key factors during machining of various materials is very important to carry out the machining. It is one of the most efficient way of cutting present in modern era. This present paper deals with the electrochemical machining of Nimonic 80A. Design of the experiments are done by using response surface methodology to study the material removal rate and surface roughness. Process parameters such as voltage, tool feed rate, inter-electrode gap and electrolyte concentration has been optimized by using the ANOVA. The regression models are developed to be used as predictive tools. The confirmation test was conducted to validate the results achieved by GRA approach. This research work helps the industrialist for selecting parameters to attain desired outputs.

Utilization of Soft Computing Techniques in Sputtering Processes: A Review

2013 ◽  
Vol 832 ◽  
pp. 260-265
Author(s):  
Norlina M. Sabri ◽  
Mazidah Puteh ◽  
Mohamad Rusop Mahmood
Keyword(s):  
Soft Computing ◽  
Process Parameters ◽  
Optimization Techniques ◽  
Future Research ◽  
Film Properties ◽  
Reliable Technique ◽  
Computing Technique ◽  
Optimization Of Process Parameters ◽  
Soft Computing Techniques ◽  
Soft Computing Technique

This paper presents an overview of research works on the utilizing of soft computing in the optimization of process parameters and in the prediction of thin film properties in sputtering processes. The papers from this review were obtained from relevant databases and from various scientific journals. The papers collected were published from 2008 to 2012. The focus of the review is to provide an outlook on the utilization of soft computing techniques in sputtering processes. Based on the review, the soft computing techniques which have been applied so far are ANN, GA and Fuzzy Logic. The first finding of this review is that soft computing technique is a promising and more reliable approach to optimize and predict process parameters compared to the traditional methods. The second finding is that the utilizing of soft computing techniques in sputtering processes are still limited and still in exploratory phase as they have not yet been extensively and stably applied. The techniques applied are also limited to ANN, GA and Fuzzy, whereas the exploration into other techniques is also necessary to be conducted in order to seek the most reliable technique and so as to expand the application of soft computing approach. Future research could focus on the exploration of other soft computing techniques for optimization in order to find the best optimization techniques based on the specific processes.

Optimization of Process Parameters in Plasma Arc Cutting Applying Genetic Algorithm and Fuzzy Logic

2018 ◽  
pp. 123-139
Author(s):  
Nehal Dash ◽  
Apurba Kumar Roy ◽  
Sanghamitra Debta ◽  
Kaushik Kumar
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
Fuzzy Logic ◽  
Process Parameters ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Plasma Arc ◽  
Plasma Arc Cutting ◽  
Optimization Of Process Parameters

Plasma Arc Cutting (PAC) process is a widely used machining process in several fabrication, construction and repair work applications. Considering gas pressure, arc current and torch height as the inputs and among all possible outputs, in the present work Material Removal Rate and Surface Roughness would be considered as factors that determines the quality, machining time and machining cost. In order to reduce the number of experiments Design of Experiments (DOE) would be carried out. In later stages applications of Genetic Algorithm (GA) and Fuzzy Logic would be used for Optimization of process parameters in Plasma Arc Cutting (PAC). The output obtained would be minimized and maximized for Surface Roughness and Material Removal Rate respectively using Genetic Algorithm (GA) and Fuzzy Logic.

Process Optimization in Non-Conventional Processes

2017 ◽  
pp. 82-119
Author(s):  
Milan Kumar Das ◽  
Tapan Kumar Barman ◽  
Prasanta Sahoo ◽  
Kaushik Kumar
Keyword(s):  
Process Parameters ◽  
Morphological Study ◽  
Artificial Bee Colony ◽  
Removal Rate ◽  
Work Piece ◽  
Bee Colony ◽  
Hard Metals ◽  
Optimal Values ◽  
Grey Relational ◽  
Conventional Machining

Conventional machining becomes non-efficient and non-effective in case of intricate shape and also while working with hard metals and alloys due to excessive tool wear. In such situations non-conventional machining, in contrast becomes more appropriate due to non-contact between tool and work-piece. In the present study, EN31 steel was machined using Plasma Arc Cutting with pre-defined process parameters. Material Removal Rate and Surface roughness were considered as responses for the study. The responses were optimized both as single and multi-response. Considering the complexities of this present problem, experimental data were generated and the results were analyzed by using Taguchi, Grey Relational Analysis and Artificial Bee Colony (ABC) Algorithm. Responses variances with the variation of process parameters were thoroughly studied and analyzed and ‘best optimal values' were identified. The result were verified by the morphological study. It was observed that there was an improvement in responses from mean to optimal values of process parameters.

A Multiobjective Genetic-Algorithm-Based Optimization of Micro-Electrical Discharge Drilling

2021 ◽  
pp. 676-698
Author(s):  
Deepak Rajendra Unune ◽  
Amit Aherwar
Keyword(s):  
Genetic Algorithm ◽  
Process Parameters ◽  
Inconel 718 ◽  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Machining Processes ◽  
Micro Electrical Discharge Machining ◽  
Electrical Discharge Drilling ◽  
Machining Rate

Inconel 718 superalloy finds wide range of applications in various industries due to its superior mechanical properties including high strength, high hardness, resistance to corrosion, etc. Though poor machinability especially in micro-domain by conventional machining processes makes it one of the “difficult-to-cut” material. The micro-electrical discharge machining (µ-EDM) is appropriate process for machining any conductive material, although selection of machining parameters for higher machining rate and accuracy is difficult task. The present study attempts to optimize parameters in micro-electrical discharge drilling (µ-EDD) of Inconel 718. The material removal rate, electrode wear ratio, overcut, and taper angle have been selected as performance measures while gap voltage, capacitance, electrode rotational speed, and feed rate have been selected as process parameters. The optimum setting of process parameters has been obtained using Genetic Algorithm based multi-objective optimization and verified experimentally.

Standard Deviation Method Based PSO

2016 ◽  
Vol 7 (2) ◽  
pp. 15-35
Author(s):  
Arindam Majumder ◽  
Abhishek Majumder
Keyword(s):  
Process Parameters ◽  
Performance Index ◽  
Manufacturing Process ◽  
Pso Algorithm ◽  
Optimization Techniques ◽  
Advanced Manufacturing ◽  
Optimization Of Process Parameters ◽  
Optimal Value ◽  
Single Attribute ◽  
Deviation Method

Nowadays, optimization of process parameters in manufacturing process deals with a number of objectives. However, the optimization of such process becomes more complex if selected attributes are conflicting in nature. Therefore, to overcome this problem in this study a SDM based PSO algorithm is proposed for optimizing the manufacturing process having multi attribute. In this proposed approach the SDM is used to convert multi attributes into single attribute, named as multi performance index, while the optimal value of this multi performance index is predicted by PSO. Finally, three instances related to optimization of advanced manufacturing process parameters are solved by the proposed approach and are compared with the results of the other established optimization techniques such as Desirability based RSM, SDM-GA and SDM-CACO. From the comparison it has been revealed that the proposed approach performs better as compare to the existing approaches.

Parameters optimization in FSW of polypropylene based on RSM

2015 ◽  
Vol 11 (1) ◽  
pp. 32-42 ◽  
Author(s):  
K Panneerselvam ◽  
Kasirajan Lenin
Keyword(s):  
Process Parameters ◽  
Rotational Speed ◽  
Optimization Techniques ◽  
Parameters Optimization ◽  
Tool Rotational Speed ◽  
Content Type ◽  
Friction Stir ◽  
Optimization Of Process Parameters ◽  
Output Characteristics ◽  
Tool Pin

Purpose – The purpose of this paper is to weld polypropylene (PP) material by friction stir welding (FSW) process. The input process parameters considered were: tool pin profile, feed rate and tool rotational speed and the process output characteristics were tensile strength, Shore-D hardness, Rockwell hardness, Izod strength, Charpy strength and nugget area. Design/methodology/approach – Optimization of process parameters were carried out based on response surface methodology (RSM) and significant parameters were obtained by performing analysis of variance (ANOVA). Findings – The optimized results were the threaded pin profile for feed of 60 mm/min and tool rotational speed of 1,500 rpm. A confirmation test was carried out to verify the optimized results. Originality/value – In this paper, the process parameters were optimized based on RSM. This is newly adopted optimization techniques in the FSW process of PP materials and also it gives better results.

Abrasive Assisted Electro Chemical Machining of Aluminum-Boron Carbide-Graphite Hybrid Composite

2014 ◽  
Vol 591 ◽  
pp. 89-93 ◽  
Author(s):  
M. Sankar ◽  
R. Baskaran ◽  
K. Rajkumar ◽  
A. Gnanavelbabu
Keyword(s):  
Boron Carbide ◽  
Process Parameters ◽  
Material Removal Rate ◽  
Material Removal ◽  
Medium Optimization ◽  
Removal Rate ◽  
Statistical Techniques ◽  
Graphite Composite ◽  
Abrasive Medium ◽  
Optimization Of Process Parameters

In this paper, attempts have been made to model and optimize process parameters in Abrasive assisted Electro-Chemical Machining (AECM) of Aluminium-Boron carbide-Graphite composite using cylindrical copper tool electrodes with SiC abrasive medium. Optimization of process parameters is based on the statistical techniques with four independent input parameters such as voltage, current, reinforcement and feed rate were used to assess the AECM process performance in terms of material removal rate. The obtained results are compared with without abrasive assisted electro chemical machining of Aluminium-Boron carbide-Graphite composite. Abrasive assisted ECM process exhibited higher material removal rate from composite material when compared with without abrasive assisted ECM.

scholarly journals Multi Response Optimization of Process Parameters during Machining of Aluminium by Satisfaction Function

2019 ◽  
Vol 8 (3) ◽  
pp. 8591-8596
Keyword(s):  
Process Parameters ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Satisfaction Function ◽  
Performance Factors ◽  
Optimization Of Process Parameters ◽  
Lathe Machine ◽  
Response Optimization ◽  
Manufacturing Environments

Economical machining operation plays an important part in competitiveness in the industries therefore selection of optimum cutting parameters is of great concern in manufacturing environments. The current research is aimed at fixing the optimal process parameters for hard turning of aluminum in CNC lathe machine using carbide tool as per the machinability aspects. On the basis of three-factor-three-level L9 orthogonal array, trials are performed by varying the manageable process constraints which are spindle speed, depth of cut and feed rate. Performance of machining has been figured by several responses like Surface Roughness and Material Removal Rate. Towards optimization above mentioned performance factors are treated as purpose functions, assuming that it corresponds to Higher-is-better and Lower-is-Better requirement respectively. Satisfaction function approach is used in this paper to find out satisfactions of individual responses enabling gathering of multi-responses into a comparable sole index

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