scholarly journals Optimization of machining parameters during Drilling by Taguchi based Design of Experiments and Validation by Neural Network

2018 ◽  
Vol 15 (2) ◽  
pp. 294-301
Author(s):  
Reddy Sreenivasulu ◽  
Chalamalasetti SrinivasaRao
Keyword(s):  
Neural Network ◽  
Design Of Experiments ◽  
Computational Cost ◽  
Machining Parameters ◽  
Burr Size ◽  
Taguchi Design Of Experiments ◽  
The Neural Network ◽  
Input Parameters ◽  
Hole Making ◽  
The Cost

Drilling is a hole making process on machine components at the time of assembly work, which are identify everywhere. In precise applications, quality and accuracy play a wide role. Nowadays’ industries suffer due to the cost incurred during deburring, especially in precise assemblies such as aerospace/aircraft body structures, marine works and automobile industries. Burrs produced during drilling causes dimensional errors, jamming of parts and misalignment. Therefore, deburring operation after drilling is often required. Now, reducing burr size is a serious topic. In this study experiments are conducted by choosing various input parameters selected from previous researchers. The effect of alteration of drill geometry on thrust force and burr size of drilled hole was investigated by the Taguchi design of experiments and found an optimum combination of the most significant input parameters from ANOVA to get optimum reduction in terms of burr size by design expert software. Drill thrust influences more on burr size. The clearance angle of the drill bit causes variation in thrust. The burr height is observed in this study.  These output results are compared with the neural network software @easy NN plus. Finally, it is concluded that by increasing the number of nodes the computational cost increases and the error in nueral network decreases. Good agreement was shown between the predictive model results and the experimental responses.  

scholarly journals Investigation of Influence of Drill Geometry and Thrust Force During Drilling of Aluminium 2014 Alloy on Burr Formation Through Design of Experiments and Nueral Network

2018 ◽  
Vol 15 (1) ◽  
pp. 5-10
Author(s):  
Reddy Sreenivasulu ◽  
Chalamalasetti Srinivasa Rao
Keyword(s):  
Design Of Experiments ◽  
Thrust Force ◽  
Computational Cost ◽  
Burr Formation ◽  
Burr Size ◽  
Taguchi Design Of Experiments ◽  
Input Parameters ◽  
Hole Making ◽  
Dimensional Errors ◽  
Network Good

Abstract Drilling is a hole making process on machine components at the time of assembly work, which were identify everywhere. In precise applications quality and accuracy play wide role. Now a day’s industries are suffered by cost incurred during deburring, especially in precise assemblies such as aerospace/aircraft body structures, marine works and automobile industries. Burrs produced during drilling causes dimensional errors, jamming of parts and misalignment. Therefore, deburring operation after drilling is often required. Now, reducing burr size is a burning topic. In this study experiments are conducted by choosing various input parameters selected from previous researchers. Effect of alteration of drill geometry on thrust force and burr size of drilled hole investigated as per taguchi design of experiments and found optimum combination of most significant input parameters from ANOVA to get optimum reduction in burr size by design expert software. Drill thrust influences more on burr size, clearance angle of drill bit causes variation in thrust, burr height observed in this study. Compare these output results with neural network software @easy NN plus. Finally, concluded that increasing the number of nodes increases the computational cost and decreases the error in nueral network. Good agreement was shown between the predictive model results and the experimental responses.

scholarly journals UTILIZING SITE CHARACTERISTICS IN NEURAL NETWORK MODELLING OF PERCENTAGE COST-TIME OVERRUN OF BUILDING PROJECTS

2017 ◽  
Vol 8 (1) ◽  
pp. 1-15
Author(s):  
A. O. Ujene ◽  
A. A. Umoh
Keyword(s):  
Neural Network ◽  
Cost Overrun ◽  
Construction Costs ◽  
Network Modelling ◽  
Building Projects ◽  
The Neural Network ◽  
Site Characteristics ◽  
Neural Network Prediction ◽  
The Cost ◽  
Prediction Approach

This study evaluated the site characteristics influencing the time and cost delivery of building projects, determined the range of percentage cost and time overrun and developed a neural network model for predicting the percentage cost and time overrun using the site characteristics of building projects. The study evaluated twelve site characteristics and two performance indicators obtained from records of construction costs, contract documents, and valuation reports of 126 purposively sampled building projects spread across several cities in Nigeria. Analyses were with descriptive and artificial neural network. It was concluded that with fairly favourable site characteristics, cost overrun range reached 77.95% with a mean variation of 44.36%, while time overrun range reached 51.23% with a mean variation of 26.77%. It was found that the accuracy performance levels of 91.93% and 91.43% for the cost and time overrun predictions respectively were very high for the optimum models. Building projects have eight significant site characteristics which can be used to reliably predict the percentage overrun, among which the ground water level, level of available infrastructure and labour proximity around the site are the most important predictors of cost and time overrun. The study recommended that project owners, consultants, contractors and other stakeholders should always use the eight identified site characteristics in predicting percentage cost and time overrun, with more priority on the first three characteristics. The study also recommended the neural network prediction approach due to its prediction accuracy.

Tunicate swarm algorithm-trained multi-layered perceptron for data centre energy demand forecasting and relative percentage contribution analysis of input parameters

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Oluwafemi Ajayi ◽  
Reolyn Heymann
Keyword(s):  
Neural Network ◽  
Energy Management ◽  
Energy Demand ◽  
Mean Squared Error ◽  
Data Set ◽  
Content Type ◽  
Demand Pattern ◽  
The Neural Network ◽  
Input Parameters ◽  
Demand Profile

Purpose Energy management is critical to data centres (DCs) majorly because they are high energy-consuming facilities and demand for their services continue to rise due to rapidly increasing global demand for cloud services and other technological services. This projected sectoral growth is expected to translate into increased energy demand from the sector, which is already considered a major energy consumer unless innovative steps are used to drive effective energy management systems. The purpose of this study is to provide insights into the expected energy demand of the DC and the impact each measured parameter has on the building's energy demand profile. This serves as a basis for the design of an effective energy management system. Design/methodology/approach This study proposes novel tunicate swarm algorithm (TSA) for training an artificial neural network model used for predicting the energy demand of a DC. The objective is to find the optimal weights and biases of the model while avoiding commonly faced challenges when using the backpropagation algorithm. The model implementation is based on historical energy consumption data of an anonymous DC operator in Cape Town, South Africa. The data set provided consists of variables such as ambient temperature, ambient relative humidity, chiller output temperature and computer room air conditioning air supply temperature, which serve as inputs to the neural network that is designed to predict the DC’s hourly energy consumption for July 2020. Upon preprocessing of the data set, total sample number for each represented variable was 464. The 80:20 splitting ratio was used to divide the data set into training and testing set respectively, making 452 samples for the training set and 112 samples for the testing set. A weights-based approach has also been used to analyze the relative impact of the model’s input parameters on the DC’s energy demand pattern. Findings The performance of the proposed model has been compared with those of neural network models trained using state of the art algorithms such as moth flame optimization, whale optimization algorithm and ant lion optimizer. From analysis, it was found that the proposed TSA outperformed the other methods in training the model based on their mean squared error, root mean squared error, mean absolute error, mean absolute percentage error and prediction accuracy. Analyzing the relative percentage contribution of the model's input parameters based on the weights of the neural network also shows that the ambient temperature of the DC has the highest impact on the building’s energy demand pattern. Research limitations/implications The proposed novel model can be applied to solving other complex engineering problems such as regression and classification. The methodology for optimizing the multi-layered perceptron neural network can also be further applied to other forms of neural networks for improved performance. Practical implications Based on the forecasted energy demand of the DC and an understanding of how the input parameters impact the building's energy demand pattern, neural networks can be deployed to optimize the cooling systems of the DC for reduced energy cost. Originality/value The use of TSA for optimizing the weights and biases of a neural network is a novel study. The application context of this study which is DCs is quite untapped in the literature, leaving many gaps for further research. The proposed prediction model can be further applied to other regression tasks and classification tasks. Another contribution of this study is the analysis of the neural network's input parameters, which provides insight into the level to which each parameter influences the DC’s energy demand profile.

scholarly journals Effect of different parameters of heterogeneous dams on safety factor using the neural network. Case study: Marvan dam

2019 ◽  
Vol 32 (02) ◽  
pp. 126-138
Author(s):  
B. Beiranvand ◽  
A. Mohammadzade ◽  
M. Komasi
Keyword(s):  
Neural Network ◽  
Factor Of Safety ◽  
Pore Water Pressure ◽  
Water Pressure ◽  
Drainage System ◽  
Friction Angle ◽  
The Body ◽  
The Neural Network ◽  
The Cost

The drainage system is used to guide the flow of water in the earth dams. Construction of drainage in the dam body to collect and direct the drainage formed in the dam body to keep the slope dry and prevent the increase of pore water pressure in the body. One of the main goals of the designers is to find the minimum factor of safety and, consequently, reduce the cost of construction. In this study, the Marvak dam is modeled with the actual characteristics of the materials in the Geostudio software, and with the change in the dimensions of the drain, the material and the slope of the dam body, the minimum Factor of safety of the dam is obtained. In order to predict the minimum Factor of safety, a two-layer neural network has been used. With the training of the neural network based on the data obtained from heterogeneous dams, a minimum Factor of safety has been extracted for optimization of drainage. Finally, it was determined that the internal friction angle of the body material and the slope of the dam have the greatest effect on the dam factor of safety.

scholarly journals Quantum Inspired Genetic Programming Model to Predict Toxicity Degree for Chemical Compounds

2018 ◽  
Vol 3 (1) ◽  
Author(s):  
Saad Mohamed Darwish
Keyword(s):  
Neural Network ◽  
Genetic Programming ◽  
Goodness Of Fit ◽  
Programming Model ◽  
Computational Cost ◽  
Chemical Compounds ◽  
Vital Role ◽  
Internal Validation ◽  
Fit Statistics ◽  
The Neural Network

Cheminformatics plays a vital role to maintain a large amount of chemical data. A reliable prediction of toxic effects of chemicals in living systems is highly desirable in domains such as cosmetics, drug design, food safety, and manufacturing chemical compounds. Toxicity prediction topic requires several new approaches for knowledge discovery from data to paradigm composite associations between the modules of the chemical compound; such techniques need more computational cost as the number of chemical compounds increases. State-of-the-art prediction methods such as neural network and multi-layer regression that requires either tuning parameters or complex transformations of predictor or outcome variables are not achieving high accuracy results.  This paper proposes a Quantum Inspired Genetic Programming “QIGP” model to improve the prediction accuracy. Genetic Programming is utilized to give a linear equation for calculating toxicity degree more accurately. Quantum computing is employed to improve the selection of the best-of-run individuals and handles parsimony pressure to reduce the complexity of the solutions. The results of the internal validation analysis indicated that the QIGP model has the better goodness of fit statistics and significantly outperforms the Neural Network model.

Using Artificial Neural Networks to Predict the Effect of Input Parameters on Weld Bead Geometry for SAW Process

2021 ◽  
Vol 54 (2) ◽  
pp. 309-315
Author(s):  
Majid Midhat Saeed ◽  
Ziad Shakeeb Al Sarraf
Keyword(s):  
Neural Network ◽  
Arc Welding ◽  
Welding Process ◽  
Submerged Arc Welding ◽  
Weld Bead ◽  
Bead Geometry ◽  
Weld Bead Geometry ◽  
The Neural Network ◽  
Input Parameters ◽  
Submerged Arc

Based on high quality and reliability, one of the most efficient methods for joining metals is Submerged Arc Welding (SAW). In this presented work, an attempt has been successfully taken to develop a model to predict the effect of input parameters on weld bead geometry of submerged arc welding process with the help of neural network technique and analysis of various process control variables and important of weld bead parameters in submerged arc welding. The complexity non-linear relationships of input / output variables for any computational models can be addressed by using artificial neural networks (ANN). Today, ANN represents a powerful modeling technique, that depend on statistical approach, presently practiced in many fields of engineering for modeling complex relationships that other physical models cannot be explained it easily. A welding process with automatic or semiautomatic is required to complete the weld through using tubular electrode with consumable flux. Parameters such as welding current, welding speed and voltage are influenced on the quality of the joints. The work conducts many experiments; these are basically depending on many factors and levels. A selection of 2205 duplex stainless steel is carried out in this study to conduct three factors and five levels of central composite design. Neural network model structure having number of neurons layers such as (3 input layers, 1 hidden layer and 3 output layers) with back propagation algorithm has been successfully applied to extract weld bead geometry from predicting the effect of input parameters. Good agreement was obtained between predicted and experiment results, however process parameters such as speed shows opposite effect on all weld parameters. It was seen that weld height and width are proportional to the amount of input current. The prediction of the neural network model showed excellent agreement with the actual results, which indicate that the neural network is viable means for predicting of not only weld bead geometry, but also other parameters such as polarity, current type and flux geometry. This recommends setting the neural network to be applicable for real time work.

CHARACTERIZING ONE-LAYER ASSOCIATIVE NEURAL NETWORKS WITH OPTIMAL NOISE-REDUCTION ABILITY

1992 ◽  
Vol 06 (05) ◽  
pp. 1009-1025 ◽  
Author(s):  
TAO WANG ◽  
XIAOLIANG XING ◽  
XINHUA ZHUANG
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Cost Function ◽  
Noise Reduction ◽  
Gradient Descent ◽  
Storage Capacity ◽  
Learning Algorithm ◽  
Optimal Learning ◽  
The Neural Network ◽  
The Cost

In this paper, we describe an optimal learning algorithm for designing one-layer neural networks by means of global minimization. Taking the properties of a well-defined neural network into account, we derive a cost function to measure the goodness of the network quantitatively. The connection weights are determined by the gradient descent rule to minimize the cost function. The optimal learning algorithm is formed as either the unconstraint-based or the constraint-based minimization problem. It ensures the realization of each desired associative mapping with the best noise reduction ability in the sense of optimization. We also investigate the storage capacity of the neural network, the degree of noise reduction for a desired associative mapping, and the convergence of the learning algorithm in an analytic way. Finally, a large number of computer experimental results are presented.

Prediction of Cutting Tool Life Based on ACO-BP Meural Network

2013 ◽  
Vol 655-657 ◽  
pp. 1714-1717 ◽  
Author(s):  
Tie Liu Wang ◽  
Xian Ming Chen ◽  
Shui Bin Chen
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Tool Life ◽  
Bp Neural Network ◽  
Cutting Tool ◽  
Back Propagation ◽  
Global Search ◽  
Machining Parameters ◽  
The Cost ◽  
Search Capability

For predicting the tool life combine the ant colony optimization(ACO) with the back propagation (BP) neural networks, use the the ACO to train BP neural network, build the prediction model based ACO-BP neural network. Some disadvantages are overcame in the BP algorithm, such as the low convergence speed, easily falling into local minimum point and weak global search capablity in the prediction process. Satisfies the requirement of global search capability and the robustness of the model. The experiment results show the prediction model has high precision in predicting the tool life. By the prediction model can provide a reasonable basis for planing production schedule and cutting tool requirement, calculating the cost, selecting the machining parameters,etc.

scholarly journals Research on Network Layer Recursive Reduction Model Compression for Image Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Hongfei Ling ◽  
Weiwei Zhang ◽  
Yingjie Tao ◽  
Mi Zhou
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Model ◽  
Computational Cost ◽  
Network Size ◽  
Compression Technique ◽  
Network Layer ◽  
Network Layers ◽  
Model Compression ◽  
The Neural Network

ResNet has been widely used in the field of machine learning since it was proposed. This network model is successful in extracting features from input data by superimposing multiple layers of neural networks and thus achieves high accuracy in many applications. However, the superposition of multilayer neural networks increases their computational cost. For this reason, we propose a network model compression technique that removes multiple neural network layers from ResNet without decreasing the accuracy rate. The key idea is to provide a priority term to identify the importance of each neural network layer, and then select the unimportant layers to be removed during the training process based on the priority of the neural network layers. In addition, this paper also retrains the network model to avoid the accuracy degradation caused by the deletion of network layers. Experiments demonstrate that the network size can be reduced by 24.00%–42.86% of the number of layers without reducing the classification accuracy when classification is performed on CIFAR-10/100 and ImageNet.

scholarly journals Using Multilayer Perceptron Artificial Neural Network for Predicting and Modeling the Chemical Oxygen Demand of the Gamasiab River

2018 ◽  
Vol 5 (1) ◽  
pp. 15-20
Author(s):  
Mohamad Parsimehr ◽  
Kamran Shayesteh ◽  
Kazem Godini ◽  
Maryam Bayat Varkeshi
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Chemical Oxygen Demand ◽  
Correlation Coefficient ◽  
Multilayer Perceptron ◽  
Oxygen Demand ◽  
Input Parameters ◽  
Artificial Neural ◽  
The Cost

Concerns about water quality have widely increased in the last three decades; thus, water quality is now as important as its quantity. To study and model the quality of the Gamasiab River, its data, including chemical oxygen demand (COD), biological oxygen demand (BOD), dissolved oxygen (DO), total dissolved solids (TDS), total suspended solids in water, acidity, temperature, turbidity, and cations and anions were measured at four stations. Then, the correlations between these parameters and COD were measured using Pearson’s correlation coefficient and modeled by multilayer perceptron artificial neural network. In order to minimize the cost of the experiments performed and to provide the input parameters to the artificial neural network based on the correlations between the data and COD, the number of input parameters was reduced and finally, model No.3, with the Momentum training function and the TanhAxon activation function with the validation correlation coefficient of 0.97, mean absolute error of 2.88, and normalized root mean square error of 0.11 was identified as the most accurate model with the lowest cost. The results of the present study showed that the multilayer perceptron neural network has high ability in modeling the COD of the river, and those data correlated with each other have the greatest effect on the model. Moreover, the number of input parameters can be reduced in order to lower the cost of experiments while the performance of the model is not undermined.

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