Artificial neural networks for the cost estimation of stamping dies

2014 ◽  
Vol 25 (3-4) ◽  
pp. 717-726 ◽  
Author(s):  
Burcu Özcan ◽  
Alpaslan Fığlalı
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cost Estimation ◽  
Stamping Dies ◽  
Artificial Neural ◽  
The Cost

scholarly journals Improved Effort and Cost Estimation Model Using Artificial Neural Networks and Taguchi Method with Different Activation Functions

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 854
Author(s):  
Nevena Rankovic ◽  
Dragica Rankovic ◽  
Mirjana Ivanovic ◽  
Ljubomir Lazic
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cost Estimation ◽  
Time Estimation ◽  
Effort Estimation ◽  
Activation Functions ◽  
Estimation Model ◽  
Wide Range ◽  
Software Product ◽  
Artificial Neural

Software estimation involves meeting a huge number of different requirements, such as resource allocation, cost estimation, effort estimation, time estimation, and the changing demands of software product customers. Numerous estimation models try to solve these problems. In our experiment, a clustering method of input values to mitigate the heterogeneous nature of selected projects was used. Additionally, homogeneity of the data was achieved with the fuzzification method, and we proposed two different activation functions inside a hidden layer, during the construction of artificial neural networks (ANNs). In this research, we present an experiment that uses two different architectures of ANNs, based on Taguchi’s orthogonal vector plans, to satisfy the set conditions, with additional methods and criteria for validation of the proposed model, in this approach. The aim of this paper is the comparative analysis of the obtained results of mean magnitude relative error (MMRE) values. At the same time, our goal is also to find a relatively simple architecture that minimizes the error value while covering a wide range of different software projects. For this purpose, six different datasets are divided into four chosen clusters. The obtained results show that the estimation of diverse projects by dividing them into clusters can contribute to an efficient, reliable, and accurate software product assessment. The contribution of this paper is in the discovered solution that enables the execution of a small number of iterations, which reduces the execution time and achieves the minimum error.

Application of a Neural Manufacturing Concept to Process Modeling, Monitoring and Control

1995 ◽  
Vol 387 ◽  
Author(s):  
Chi Yung Fu ◽  
Loren Petrich ◽  
Benjamin Law
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Process Modeling ◽  
Lawrence Livermore National Laboratory ◽  
National Laboratory ◽  
Monitoring And Control ◽  
Artificial Neural ◽  
The Cost ◽  
And Control ◽  
Capital Intensive

AbstractThe cost of a fabrication line, such as one in a semiconductor house, has increased dramatically over the years, and it is possibly already past the point that some new start-up company can have sufficient capital to build a new fabrication line. Such capital-intensive manufacturing needs better utilization of resources and management of equipment to maximize its productivity. In order to maximize the return from such a capital-intensive manufacturing line, we need to work on the following: 1) increasing the yield, 2) enhancing the flexibility of the fabrication line, 3) improving quality, and finally 4) minimizing the down time of the processing equipment. Because of the significant advances now made in the fields of artificial neural networks, fuzzy logic, machine learning and genetic algorithms, we advocate the use of these new tools in manufacturing. We term the applications to manufacturing of these and other such tools that mimic human intelligence neural manufacturing. This paper describes the effort at the Lawrence Livermore National Laboratory (LLNL) [1] to use artificial neural networks to address certain semiconductor process modeling, monitoring and control questions.

Crack Width Estimation Using Feed and Cascade Forward Back Propagation Artificial Neural Networks

2018 ◽  
Vol 786 ◽  
pp. 293-301 ◽  
Author(s):  
Hesham M. Shehata ◽  
Yasser S. Mohamed ◽  
Mohamed Abdellatif ◽  
Taher H. Awad
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Crack Width ◽  
Laser Scanning ◽  
Back Propagation ◽  
Maximum Width ◽  
The Neural Networks ◽  
Maximum Crack ◽  
Artificial Neural ◽  
The Cost

Automatic crack inspection techniques that limit the necessity of human have the potential to lower the cost and time of the process. In this study, a maximum crack width estimation approach is presented. Seventy nine segments of cracks are used for training the neural networks and twenty six segments are used for examination. The maximum width for each segment is measured using laser scanning microscope and segment image is captured and magnified using the microscope camera in order to obtain the extracted crack profile number of pixels. Feed and cascade forward back propagation artificial neural networks are designed and constructed. The input and output for the networks are the crack width in terms of number of pixels and the maximum estimated crack width respectively. It is shown that, the artificial neural networks technique can effectively be used to estimate the crack width. The feedforward back propagation structure which is designed with two layers and training function TRAINLM gives the best results in examination.

scholarly journals ANN Based Approach for Estimation of Construction Costs of Sports Fields

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Michał Juszczyk ◽  
Agnieszka Leśniak ◽  
Krzysztof Zima
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cost Estimation ◽  
Construction Projects ◽  
Construction Costs ◽  
General Applicability ◽  
Artificial Neural ◽  
Multilayer Perceptron Networks ◽  
Cost Of Construction ◽  
Sports Fields

Cost estimates are essential for the success of construction projects. Neural networks, as the tools of artificial intelligence, offer a significant potential in this field. Applying neural networks, however, requires respective studies due to the specifics of different kinds of facilities. This paper presents the proposal of an approach to the estimation of construction costs of sports fields which is based on neural networks. The general applicability of artificial neural networks in the formulated problem with cost estimation is investigated. An applicability of multilayer perceptron networks is confirmed by the results of the initial training of a set of various artificial neural networks. Moreover, one network was tailored for mapping a relationship between the total cost of construction works and the selected cost predictors which are characteristic of sports fields. Its prediction quality and accuracy were assessed positively. The research results legitimatize the proposed approach.

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