Mind Recognition – A Novel Approach to Retro Fitting CNC Machines
Balamurugan L,
Department of Mechanical Engineering,
Sri Chandrasekharendra Saraswathi Viswa Maha Vidyalaya, Kanchipuram – 631561, Tamilnadu, India, E-mail:
National Conference on Advanced Technologies in Mechanical Engineering, SVCET, August 2012. Page 4
Abstract -The Artificial Neural Network is to be used in the Human Brain to sense the Neural Signals using Brain Computer Interface such as Electro Encephalo Gram/Magnetic Resonance Imaging and Signal Processing with Lab View/Signal Synchronization Techniques. The processed brain signals are used to control Retro-Fitted electro-mechanical appliances/print the text (G-Codes & M – Codes to control the CNC Machine) that one thinks. It can also be applied to monitor Seizures/Heart attack/varying BP and multiple medicinal and Industrial applications.
Keywords – Nerve Conduction Velocity, AT89S52 Micro-controller, RS 232 convertor, Electromyography, Functional MRI.
National Conference on Advanced Technologies in Mechanical Engineering, SVCET, August 2012. Page 4
1. INTRODUCTION
Competition in the market is getting more and more globalized and the fast technological changes force manufacturers to search for new ways to stimulate the productive system to the market requirements.
Several proposals have been put forward in an attempt to solve the problem of the control of productive systems distributed whenever it is necessary to include a new process in the system.
To reach this goal, it is necessary to develop a new model which includes agile manufacturing. The purpose of this presentation is to project the control of agile productive systems with Computerized Numerical Control (CNC) using its Open Architecture; it is connected with the Database which will be connected with the Brain Computer Interface. When the User thinks, his job is done. This effect is achieved by AT89S52 microcontroller encapsulated with the database management software which acts the testing phase in the Artificial Neural Network. Whenever the user thinks his desired objective, the operation to be done is initially mated with the sampling database and then it is checked with the hidden layers of the artificial neural networks for its precision and accuracy.
2. FEATURE EXTRACTION
Electroencephalography (EEG) is the recording of electrical activity along the scalp produced by the firing of neurons within the brain. In clinical contexts, EEG refers to the recording of the brains spontaneous electrical activity over a short period of time, usually 20–40 minutes, as recorded from multiple electrodes placed on the scalp. In neurology, the main diagnostic application of EEG is in the case of epilepsy, as epileptic activity can create clear abnormalities on a standard EEG study. A secondary clinical use of EEG is in the diagnosis of coma, encephalopathy, and brain death. EEG used to be a first-line method for the diagnosis of tumors, stroke and other focal brain disorders, but this use has decreased with the advent of anatomical imaging techniques such as MRI and CT. Derivatives of the EEG technique include evoked potentials (EP), which involves averaging the EEG activity time-locked to the presentation of a stimulus of some sort (visual, somatosensory, or auditory). Event-related potentials refer to averaged EEG responses that are time-locked to more complex processing of stimuli; this technique is used in cognitive science, cognitive psychology, and Psychophysiological research. The electrical activity of the brain can be described in spatial scales from the currents within a single dendrites spine to the relatively gross potentials that the EEG records from the scalp, much the same way that economics can be studied from the level of a single individual's personal finances to the macro-economics of nations. Neurons, or nerve cells, are electrically active cells that are primarily responsible for carrying out the brain's functions. Neurons create action potentials, which are discrete electrical signals that travel down axons and cause the release of chemical neurotransmitters at the synapse, which is an area of near contact between two neurons. This neurotransmitter then activates a receptor in the dendrite or body of the neuron that is on the other side of the synapse, the post-synaptic neuron.
The neurotransmitter, when combined with the receptor, typically causes an electric current within the dendrite or body of the post-synaptic neuron. Thousands of post-synaptic currents from a single neuron's dendrites and body then sum up to cause the neuron to generate an action potential. This neuron then synapses on other neurons, and so on. EEG reflects correlated synaptic activity caused by post-synaptic potentials of cortical neurons.
The ionic currents involved in the generation of fast action potentials may not contribute greatly to the averaged field potentials representing the EEG. More specifically, the scalp electrical potentials that produce EEG are generally thought to be caused by the extracellular ionic currents caused by dendrite electrical activity, whereas the fields producing Magneto encephalographic signals are associated with intracellular ionic currents.
3. BIOLOGICAL NEURAL NETWORK
In neuroscience, a biological neural network (sometimes called a neural pathway) is a series of interconnected neurons whose activation defines a recognizable linear pathway. The interface through which neurons interact with their neighbors usually consists of several axon terminals connected via synapses to dendrites on other neurons. If the sum of the input signals into one neuron surpasses a certain threshold, the neuron sends an action potential (AP) at the axon hillock and transmits this electrical signal along the axon.
Different neuroimaging techniques have been developed to investigate the activity of neural networks. The use of "brain scanners" or functional neuroimaging to investigate the structure or function of the brain is common, either as simply a way of better assessing brain injury with high resolution pictures, or by examining the relative activations of different brain areas. Such technologies may include fMRI (functional magnetic resonance imaging), PET (positron emission tomography) and CAT (computed axial tomography). Functional neuro imaging uses specific brain imaging technologies to take scans from the brain, usually when a person is doing a particular task, in an attempt to understand how the activation of particular brain areas is related to the task. In functional neuroimaging, especially fMRI, which measures hemodynamic activity that is closely linked to neural activity, PET, and electroencephalography (EEG), is used.
Connectionist models serve as a test platform for different hypotheses of representation, information processing, and signal transmission. Lesioning studies in such models, e.g. artificial neural networks, where parts of the nodes are deliberately destroyed to see how the network performs, can also yield important insights in the working of several cell assemblies. Similarly, simulations of dysfunctional neurotransmitters in neurological conditions (e.g., dopamine in the basal ganglia of Parkinson's patients) can yield insights into the underlying mechanisms for patterns of cognitive deficits observed in the particular patient group. Predictions from these models can be tested in patients and/or via pharmacological manipulations, and these studies can in turn be used to inform the models, making the process recursive.
4. ARTIFICIAL NEURAL NETWORK
The user should know algebra and the handling of functions and vectors. Differential calculus is recommendable, but not necessary. The contents of this package should be understood by people with high school education. It would be useful for people who are just curious about what are ANNs, or for people who want to become familiar with them, so when they study them more fully, they will already have clear notions of ANNs. Also, people who only want to apply the back propagation algorithm without a detailed and formal explanation of it will find this material useful. This work should not be seen as “Nets for dummies”, but of course it is not a treatise. Much of the formality is skipped for the sake of simplicity. Detailed explanations and demonstrations can be found in the referred readings. The included exercises complement the understanding of the theory. The on-line resources are highly recommended for extending this brief introduction. One efficient way of solving complex problems is following the lemma “divide and conquer”. A complex system may be decomposed into simpler elements, in order to be able to understand it. Also simple elements may be gathered to produce a complex system (Bar Yam, 1997). Networks are one approach for achieving this. There are a large number of different types of networks, but they all are characterized by the following components: a set of nodes, and connections between nodes.
The nodes can be seen as computational units. They receive inputs, and process them to obtain an output. This processing might be very simple (such as summing the inputs), or quite complex (a node might contain another network...) the connections determine the information flow between nodes. They can be unidirectional, when the information flows only in one sense, and bidirectional, when the information flows in either sense. The interactions of nodes though the connections lead to a global behavior of the network, which cannot be observed in the elements of the network. This global behavior is said to be emergent. This means that the abilities of the network super cede the ones of its elements, making networks a very powerful tool.
5. RETRO FITTING CNC
CNC system refers to the automation of machine tools that are operated by abstractly programmed commands encoded on storage medium, as opposed to manually controlled. In manufacturing sectors like power equipment manufacturing, automobiles, process industry etc CNC System based machines are used for various cutting applications like drilling, milling, turning, boring, punching, notching and for special purposes like winding, pressing, taping etc. Due to the ageing of the machine, technological obsolescence, reduced accuracy, increased number of breakdown make it necessary to think for the Retrofitting/Reconditioning/Upgradation of the machine, but there is no widely accepted model for the estimation of cost of retrofitting/reconditioning/ upgradation. Presently the practice is to do the costing based on salvage value of the machine based on depreciation and other financial factors but it completely neglects the factors mechanical condition of the machine, technological obsolescence of the control system, ratio of estimated life of the machine after retrofitting and life of a new machine. To incorporate these factors and to find out how these factors affect the cost of retrofitting, we used neural network with database management system with data abstraction system to control the CNC Machines.
5.1 Interfacing CNC with labview software
LabVIEW ties the creation of user interfaces (called front panels) into the development cycle. LabVIEW programs/subroutines are called virtual instruments (VIs). Each VI has three components: a block diagram, a front panel and a connector panel. The last is used to represent the VI in the block diagrams of other, calling VIs. Controls and indicators on the front panel allow an operator to input data into or extract data from a running virtual instrument. However, the front panel can also serve as a programmatic interface. Thus a virtual instrument can either be run as a program, with the front panel serving as a user interface, or, when dropped as a node onto the block diagram, the front panel defines the inputs and outputs for the given node through the connector panel. This implies each VI can be easily tested before being embedded as a subroutine into a larger program.
The graphical approach also allows non-programmers to build programs by dragging and dropping virtual representations of lab equipment with which they are already familiar. The LabVIEW programming environment, with the included examples and documentation, makes it simple to create small applications. This is a benefit on one side, but there is also a certain danger of underestimating the expertise needed for high-quality G programming. The most advanced LabVIEW development systems offer the possibility of building stand-alone applications. Furthermore, it is possible to create distributed applications, which communicate by a client/server scheme, and are therefore easier to implement due to the inherently parallel nature of G.
The image above is an illustration of a simple LabVIEW program showing the dataflow source code in the form of the block diagram in the lower left frame and the input and output variables as graphical objects in the upper right frame. The two are the essential components of a LabVIEW program referred to as a Virtual Instrument.
6. ADVANCED TECHNOLOGIES IN HANDLING CNC
Near Field Communication technology is used in the QR codes to encode the G codes and M codes to reduce the standard time of the production of the CNC Machine like kanban System in FMS. Human Area Networking using Red Tact Photonic Super Sensitive Sensor decreases the standard time still more and then increases the productivity of the Industry.
7. CONCLUSION
Thus, the Artificial Neural Network is used in the Human Brain to sense the Neural Signals using Brain Computer Interface such as Electro Encephalo Gram/Magnetic Resonance Imaging and Signal Processing with Lab View/Signal Synchronization Techniques with the database. The processed brain signals are used to control Retro-Fitted electro-mechanical appliance and also to print the text (G-Codes & M – Codes to control the CNC Machine) that one thinks. It can also be applied to monitor Seizures/Heart attack/varying BP and multiple medicinal and Industrial applications.
8. REFERENCES
[1] E. C. Davis, M. P. Branes, “CNC Complete Reference”, 143-149, 2000.
[2] M. F. Brin, “Microprocessors and Micro controllers”, 124-154, 2005.
[3] Bolton, “Sensors and transducers”, 274-387, 2007.
National Conference on Advanced Technologies in Mechanical Engineering, SVCET, August 2012. Page 4