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Title: Development of a Hybrid Recurrent Neural Network Based Intelligent Decision Support System with Reverse Mapping for CNC Machining
Authors: Malghan, Rashmi Laxmikant
Supervisors: S. Rao, Shrikantha
D’Souza, R. J.
Keywords: Department of Mechanical Engineering;Artificial Neural Network;Forward Mapping;Reverse Mapping;Hybrid Recurrent Neural Network;Particle Swarm Optimization
Issue Date: 2018
Publisher: National Institute of Technology Karnataka, Surathkal
Abstract: The growth of consumer demands for better quality metal cutting related products has motivated the metal cutting industry to continuously enhance quality control of metal cutting processes. Of the several processes, the face-milling is one of the most fundamental metal removal operations used. It is affected by machining process parameters like cutting force, ambient conditions, coolant type, tool parameters and material properties. Nowadays, diverse types of materials have been used based on the condition requirements like strength, weight, corrosion resistance, etc. Metal reinforced composites have tailorable properties which widen their applications. Machining of composite materials is difficult to carry out due to the anisotropic and non-homogeneous structure of composites and the high abrasiveness of their reinforcing constituents. In this study on milling of AA6061 and AA6061-4.5%Cu-5%SiCp composite, formation of unwanted scratches on the surface of the material were witnessed due to presence of hard particles, resulting in increased surface roughness. Design of experiment is used to analyse the machining process parameters. Taguchi orthogonal array design is used to analyse the levels of the experiment. The Analysis of Variance (ANOVA) is also used to evaluate the contribution of process parameters on milling process output variables for both alloys and composites. The mathematical models for cutting force, surface roughness and power consumption are developed using response surface methodology(RSM). Under utilization of machine capacity limits the efficient use of machines and is presently continually being run at sub-optimal conditions. In this study, a novel technique is introduced wherein the desired depth of cut is achieved with lesser number of passes, lesser time and also by consuming lesser power. Planning a strategy for better machine utilization based on power constraint in machining using PID logic. Further, prediction of responses of milling process are carried out using artificial neural network (ANN) with feed forward architecture using error back propagation learning algorithm. A reverse mapping neural network (NN) has been implemented as a novel architecture, which can derive the input responses, based on the desired system outputs. Reverse mapping approach can be treated as advisory system in absence of human experts, can predict the settings of various process parameters in a milling process to achieve the desired responses as per the requirements of end user. Further this model can be implemented to adjust the process parameters in on-line control of the milling quality. The validity of themodels is established. The ANN models formulated for cutting force, surface roughness and power consumption are found to predict the corresponding responses quite accurately, within the acceptable limits of prediction errors. To explore the dynamic learning capacity of Elman Simple Recurrent Neural Network as advancement over ANN model, the corresponding RNN model was developed. The convergence problem of RNN model was overcome by an innovative way by using Hybrid Recurrent Neural Network (HRNN). The biases and weights are borrowed in a HRNN model with feedback connections, from a partially trained ANN model having similar architecture. The HRNN formulated using this methodology is able to predict the relationship between input and output data and a good correlation is achieved. With reduced learning time, it is observed that an HRNN modelled from a partially trained ANN has equivalent prediction capability and is superior to ANN in terms of computational time. It is noteworthy that, prediction helps the investigator to determine the outputs as well as inputs, but since it fails to estimate the global extreme values of the response responsible for the best product quality (minimum defects). Identifying the extreme values for the conflicting outputs poses difficulties. Traditional methods (DOE, RSM, Grey Relational Analysis and Classical engineering approach) might fail to determine the global optimum values as searches are carried out in single direction. Evolutionary algorithms (Particle Swarm Optimization(PSO)) through their heuristic search mechanisms determine the global solutions at many distinct locations in multi-dimensional space, simultaneously. The lower and upper levels of machining parameters were opted as constraints. The optimized results were cross verified with experiments and found to have good agreement with the experimental values. PSO outperforms Grey Relational Analysis and RSM thus can be utilized as a tool to optimize and predict results during machining of AA6061 and AA6061-4.5%CU-5%SiCp. Graphical user interface (GUI) has been designed using available API libraries which include two main modules, namely, Prediction (both forward and reverse mapping) and Optimization. Each model has the sub components for prediction of cutting force, surface roughness and power consumption. There is provision to obtain outputs by manually feeding the inputs as well for plotting bar graphs by varying one parameter at a time, keeping others constant.
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