A multi-sensor integration method of signals in a metal cutting operation via application of Multi-layer Perceptron neural networks

1997 ◽  
Author(s):  
D.E. Dimia
Keyword(s):  
Neural Networks ◽  
Metal Cutting ◽  
Integration Method ◽  
Sensor Integration ◽  
Multi Layer Perceptron ◽  
Cutting Operation

On the Effect of Electrodischarge Drilling on the Fatigue Life of Inconel 718

2014 ◽  
Vol 891-892 ◽  
pp. 1451-1456
Author(s):  
Elena Bassoli ◽  
Andrea Baldini ◽  
Andrea Gatto ◽  
Antonio Strozzi ◽  
Lucia Denti
Keyword(s):  
Fatigue Life ◽  
Inconel 718 ◽  
Metal Cutting ◽  
Fatigue Behavior ◽  
Fatigue Tests ◽  
Aerospace Applications ◽  
Rotating Bending Fatigue ◽  
Cutting Operation ◽  
Rotating Bending ◽  
Superposition Effect

Difficult-to cut-materials are associated with premature tool failure, most likely in the case of complex geometries and this shapes. However, Nickel-based alloys are commonly used in high-temperature and aerospace applications, where thin deep holes are often required. Then, the only viable manufacturing solution relies on non-contact processes, like electrodischarge (ED) drilling. Morphology of ED machined surfaces is significantly different than obtained by metal-cutting operation and is known to jeopardize fatigue strength, but the extent needs to be gauged and related to the process parameters. Aim of the paper is to study the effect of holes (0.8 mm diameter, aspect ratio 10) produced by ED drilling on the fatigue life of Inconel 718. Rotating bending fatigue tests are carried out on specimens drilled under two ED setups, as well as with a traditional cutting tool. Specimens free from holes are fatigued under the same conditions for comparison. Based on previous studies, extremal ED parameters are selected, giving best surface finish versus highest productivity. S-N curves show that the ED process causes a decrease of the fatigue resistance with respect to traditional drilling, whereas the effect of different ED setups is negligible. Maximum productivity can thus be pursued with no threat to fatigue performance. The fatigue limit variation is quantified by using the superposition effect principle: ED drilling causes an increase of the stress concentration factor around 25% if compared to traditional drilling. The macroscopic fatigue behavior is integrated with a study of the effects of the different drilling processes in the micro-scale, by means of a microstructural and fractographic analysis.

Experimental Analysis of Different Coolants Used in Plasma Cutting Operation for the Improved Electrode Life

2015 ◽  
Vol 719-720 ◽  
pp. 46-49 ◽  
Author(s):  
Ginka Ranga Janardhana ◽  
Mani Senthil Kumar ◽  
B. Dhanasekar
Keyword(s):  
Temperature Rise ◽  
Metal Cutting ◽  
Cooling System ◽  
Heat Removal ◽  
Plasma Cutting ◽  
Life Test ◽  
Flow Rates ◽  
Optimal Flow ◽  
Cutting Operation ◽  
The Impact

The plasma cutting technology has been emerged as a developing technology which finds tremendous potential in fabrication and metal cutting industries. Thus for the cutting operation, the electrode inside the plasma torch plays a vital role for the plasma arc generation. The temperature of the arc is very high and at the electrode is around 3500°C. The cutting torch requires proper cooling system in order to prevent the electrode from quick wear due to the existence of high thermal gradient. The presented work aimed to study the impact of three coolants propylene glycol, ethylene glycol and de-ionized water flow over the electrode life. The experimental setups were arranged to study the heat transfer capabilities of the three coolants for different flow values and aimed to achieve the optimal flow rates for the efficient heat removal. The electrode life test trials were conducted to measure the electrode life for the flow values of three coolants in the temperature rise test. The optimal flow rates arrived from temperature rise test and the electrode life measured from life test are compared for the three coolant cases considered.

scholarly journals Dynamic versus static neural network model for rainfall forecasting at Klang River Basin, Malaysia

2012 ◽  
Vol 16 (4) ◽  
pp. 1151-1169 ◽  
Author(s):  
A. El-Shafie ◽  
A. Noureldin ◽  
M. Taha ◽  
A. Hussain ◽  
M. Mukhlisin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Network Architecture ◽  
Rainfall Time Series ◽  
Hydrological Process ◽  
Multi Layer Perceptron ◽  
Rainfall Forecasting ◽  
Unseen Data

Abstract. Rainfall is considered as one of the major components of the hydrological process; it takes significant part in evaluating drought and flooding events. Therefore, it is important to have an accurate model for rainfall forecasting. Recently, several data-driven modeling approaches have been investigated to perform such forecasting tasks as multi-layer perceptron neural networks (MLP-NN). In fact, the rainfall time series modeling involves an important temporal dimension. On the other hand, the classical MLP-NN is a static and has a memoryless network architecture that is effective for complex nonlinear static mapping. This research focuses on investigating the potential of introducing a neural network that could address the temporal relationships of the rainfall series. Two different static neural networks and one dynamic neural network, namely the multi-layer perceptron neural network (MLP-NN), radial basis function neural network (RBFNN) and input delay neural network (IDNN), respectively, have been examined in this study. Those models had been developed for the two time horizons for monthly and weekly rainfall forecasting at Klang River, Malaysia. Data collected over 12 yr (1997–2008) on a weekly basis and 22 yr (1987–2008) on a monthly basis were used to develop and examine the performance of the proposed models. Comprehensive comparison analyses were carried out to evaluate the performance of the proposed static and dynamic neural networks. Results showed that the MLP-NN neural network model is able to follow trends of the actual rainfall, however, not very accurately. RBFNN model achieved better accuracy than the MLP-NN model. Moreover, the forecasting accuracy of the IDNN model was better than that of static network during both training and testing stages, which proves a consistent level of accuracy with seen and unseen data.

Role of energy consumption, cutting tool and workpiece materials towards environmentally conscious machining: A comprehensive review

2019 ◽  
Vol 234 (3) ◽  
pp. 335-354 ◽  
Author(s):  
Salman Pervaiz ◽  
Sathish Kannan ◽  
Ibrahim Deiab ◽  
Hossam Kishawy
Keyword(s):  
Energy Consumption ◽  
Metal Cutting ◽  
Cutting Tool ◽  
Cutting Process ◽  
Machining Process ◽  
Workpiece Material ◽  
Tool Materials ◽  
Wide Range ◽  
Cutting Operation

Metal-cutting process deals with the removal of material using the shearing operation with the help of hard cutting tools. Machining operations are famous in the manufacturing sector due to their capability to manufacture tight tolerances and high dimensional accuracy while simultaneously maintaining the cost-effectiveness for higher production levels. As metal-cutting processes consume a great amount of input resources and generate some material-based waste streams, these processes are highly criticized due to their high and negative environmental impacts. Researchers in the metal-cutting sector are currently exploring and benchmarking different activities and best practices to make the cutting operation environment friendly in nature. These eco-friendly practices mainly cover the wide range of activities directly or indirectly associated with the metal-cutting operation. Most of the literature for sustainable metal-cutting activities revolves around the sustainable lubrication techniques to minimize the negative influence of cutting fluids on the environment. However, there is a need to enlarge the assessment domain for the metal-cutting process and other directly and indirectly associated practices such as enhancing sustainability through innovative methods for workpiece and cutting tool materials, and approaches to optimize energy consumption should also be explored. The aim of this article is to explore the role of energy consumption and the influence of workpiece and tool materials towards the sustainability of machining process. The article concludes that sustainability of the machining process can be improved by incorporating different innovative approaches related to the energy and tool–workpiece material consumptions.

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