Analysis and Optimization of Machining Process Using Evolutionary Algorithms

To ensure quality of machined products at minimum machining costs and maximum machining effectiveness, it is very important to select optimum parameters when metal cutting machine tools are employed. Traditionally, the experience of the operator plays a major role in the selection of optimum metal cutting conditions. However, attaining optimum values each time by even a skilled operator is difficult. The non-linear nature of the machining process has compelled engineers to search for more effective methods to attain optimization. The design objective preceding most engineering design activities is simply to minimize the cost of production or to maximize the production efficiency. The main aim of research work reported here is to build robust optimization algorithms by exploiting ideas that nature has to offer from its backyard and using it to solve real world optimization problems in manufacturing processes.In this thesis, after conducting an exhaustive literature review, several optimization techniques used in various manufacturing processes have been identified. The selection of optimal cutting parameters, like depth of cut, feed and speed is a very important issue for every machining process. Experiments have been designed using Taguchi technique and dry turning of SS420 has been performed on Kirlosker turn master 35 lathe. Analysis using S/N and ANOVA were performed to find the optimum level and percentage of contribution of each parameter. By using S/N analysis the optimum machining parameters from the experimentation is obtained.Optimization algorithms begin with one or more design solutions supplied by the user and then iteratively check new design solutions, relative search spaces in order to achieve the true optimum solution. A mathematical model has been developed using response surface analysis for surface roughness and the model was validated using published results from literature.Methodologies in optimization such as Simulated annealing (SA), Particle Swarm Optimization (PSO), Conventional Genetic Algorithm (CGA) and Improved Genetic Algorithm (IGA) are applied to optimize machining parameters while dry turning of SS420 material. All the above algorithms were tested for their efficiency, robustness and accuracy and observe how they often outperform conventional optimization method applied to difficult real world problems. The SA, PSO, CGA and IGA codes were developed using MATLAB. For each evolutionary algorithmic method, optimum cutting conditions are provided to achieve better surface finish.The computational results using SA clearly demonstrated that the proposed solution procedure is quite capable in solving such complicated problems effectively and efficiently. Particle Swarm Optimization (PSO) is a relatively recent heuristic search method whose mechanics are inspired by the swarming or collaborative behavior of biological populations. From the results it has been observed that PSO provides better results and also more computationally efficient.Based on the results obtained using CGA and IGA for the optimization of machining process, the proposed IGA provides better results than the conventional GA. The improved genetic algorithm incorporating a stochastic crossover technique and an artificial initial population scheme is developed to provide a faster search mechanism. Finally, a comparison among these algorithms were made for the specific example of dry turning of SS 420 material and arriving at optimum machining parameters of feed, cutting speed, depth of cut and tool nose radius for minimum surface roughness as the criterion. To summarize, the research work fills in conspicuous gaps between research prototypes and industry requirements, by simulating evolutionary procedures seen in nature that optimize its own systems.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

Document Image Segmentation and Compression using Artificial Neural Networks and Evolutionary Methods

The work is intended to study the following important aspects of document image processing and develop new methods. (1) Segmentation ofdocument images using adaptive interval valued neuro-fuzzy method. (2) Improving the segmentation procedure using Simulated Annealing technique. (3) Development of optimized compression algorithms using Genetic Algorithm and parallel Genetic Algorithm (4) Feature extraction of document images (5) Development of IV fuzzy rules. This work also helps for feature extraction and foreground and background identification. The proposed work incorporates Evolutionary and hybrid methods for segmentation and compression of document images. A study of different neural networks used in image processing, the study of developments in the area of fuzzy logic etc is carried out in this work

A Reinforcement Learning Approach to Economic Dispatch using Neural Networks

This paper presents a Reinforcement Learning (RL) approach to economic dispatch (ED) using Radial Basis Function neural network. We formulate the ED as an N stage decision making problem. We propose a novel architecture to store Qvalues and present a learning algorithm to learn the weights of the neural network. Even though many stochastic search techniques like simulated annealing, genetic algorithm and evolutionary programming have been applied to ED, they require searching for the optimal solution for each load demand. Also they find limitation in handling stochastic cost functions. In our approach once we learn the Q-values, we can find the dispatch for any load demand. We have recently proposed a RL approach to ED. In that approach, we could find only the optimum dispatch for a set of specified discrete values of power demand. The performance of the proposed algorithm is validated by taking IEEE 6 bus system, considering transmission losses

New Simulated Corrugated Scaltering Surface Giving Widebandcharacter I Sti Cs

Simultaneous elimination of specular reflection and backscattered power from a plane metallic surface by simulated corrugated surfaces of constant period and variable strip width for TM polarisation is reported. This new configuration offers almost a ten-fold frequency bandwidth compared with a regularly spaced strip grating of the same size.

Array pattern nulling by element position perturbations using a genetic algorithm

A genetic algorithm has been used for null steering in phased and adaptive arrays . It has been shown that it is possible to steer the array null s precisely to the required interference directions and to achieve any prescribed null depths . A comparison with the results obtained from the analytic solution shows the advantages of using the genetic algorithm for null steering in linear array patterns

Aircraft down-range profiles formed from simulated and compact-range radar backscatter

Compact-range radar backscatter measurements are taken of aircraft scale models. In addition, computer software is used to predict the RCS of the aircraft. Synthetic down-range profiles formed from the two sources of backscatter data are compared and visualized in an innovative manner. Similar discrimination rates between the two aircraft are obtained on data from both source

Effect of annealing on the spectral and nonlinear optical characteristics of thin films of nano-ZnO

The annealing effect on the spectral and nonlinear optical NLO characteristics of ZnO thin films deposited on quartz substrates by sol-gel process is investigated. As the annealing temperature increases from 300–1050 °C, there is a decrease in the band gap, which indicates the changes of the interface of ZnO. ZnO is reported to show two emission bands, an ultraviolet UV emission band and another in the green region. The intensity of the UV peak remains the same while the intensity of the visible peak increases with increase in annealing temperature. The role of oxygen in ZnO thin films during the annealing process is important to the change in optical properties. The mechanism of the luminescence suggests that UV luminescence of ZnO thin films is related to the transition from conduction band edge to valence band, and green luminescence is caused by the transition from deep donor level to valence band due to oxygen vacancies. The NLO response of these samples is studied using nanosecond laser pulses at off-resonance wavelengths. The nonlinear absorption coefficient increases from 2.9 ×10−6 to 1.0 ×10−4 m/W when the annealing temperature is increased from 300 to 1050 °C, mainly due to the enhancement of interfacial state and exciton oscillator strength. The third order optical susceptibility x(3) increases with increase in annealing temperature (T) within the range of our investigations. In the weak confinement regime, T2.4 dependence of x(3) is obtained for ZnO thin films. The role of annealing temperature on the optical limiting response is also studied.

Fabrication of thin films and nano columnar structures of Fe-Ni amorphous alloys and modification of its surface properties by thermal annealing and swift heavy ion irradiation for tailoring the magnetic properties

Department of Physics, Cochin University of Science and Technology

Development of a new type of simulated scalar feed

This thesis presents the results of an investigation conducted for the development of a new type of feed horn antenna called "Simulated Scalar Feed". A schematic presentation of the work is given below. A review of the past important work done in the field of conventional/multimode electromagnetic horn antennas is presented in the first part of the second chapter. The work carried out on corrugated horns and surfaces are included in the second part of the review. In the third part, work on dielectric and dielectric loaded metal horns are reviewed. In all the parts of the review, special emphasis is given to theoretical design considerations. The methodology adopted for the experimental investigations is presented in the third chapter. The instrumentation utilized and thThis thesis presents the results of an investigation conducted for the development of a new type of feed horn antenna called "Simulated Scalar Feed". A schematic presentation of the work is given below. A review of the past important work done in the field of conventional/multimode electromagnetic horn antennas is presented in the first part of the second chapter. The work carried out on corrugated horns and surfaces are included in the second part of the review. In the third part, work on dielectric and dielectric loaded metal horns are reviewed. In all the parts of the review, special emphasis is given to theoretical design considerations. The methodology adopted for the experimental investigations is presented in the third chapter. The instrumentation utilized and the details of fabrication ofe details of fabrication of the new simulated scalar feed are described. The method of measurements of radiation characteristics of the antenna are also explained in this chapter. In the fourth chapter the outcome of the experimental results of the investigations carried out on horn antennas fabricated with different physical dimensions and different parameters for the E—plane boundary walls are highlighted. The theoretical explanation used to explain the experimental results is given in the fifth chapter of the thesis. A comparison between the experimental and the theoretical results is also presented in this chapter. In chapter six, the conclusions drawn from the experimental as well as the theoretical investigations are discussed. The advantages and features of the newly developed simulated scalar feed is examined in this chapter. Scope of further investigations in this field is also discussed at the end of this chapter.

On the Tailoring of Magnetic Properties of Fe-Based Alloy Thin Films by Swift Heavy Ion Irradiation and Thermal Annealing

The development of new materials has been the hall mark of human civilization. The quest for making new devices and new materials has prompted humanity to pursue new methods and techniques that eventually has given birth to modern science and technology. With the advent of nanoscience and nanotechnology, scientists are trying hard to tailor materials by varying their size and shape rather than playing with the composition of the material. This, along with the discovery of new and sophisticated imaging tools, has led to the discovery of several new classes of materials like (3D) Graphite, (2D) graphene, (1D) carbon nanotubes, (0D) fullerenes etc. Magnetic materials are in the forefront of applications and have beencontributing their share to remove obsolescence and bring in new devices based on magnetism and magnetic materials. They find applications in various devices such as electromagnets, read heads, sensors, antennas, lubricants etc. Ferromagnetic as well as ferrimagnetic materials have been in use in the form of various devices. Among the ferromagnetic materials iron, cobalt and nickel occupy an important position while various ferrites finds applications in devices ranging from magnetic cores to sensors.

Multi-objective assembly job shop scheduling using genetic algorithm and tabu search

Assembly job shop scheduling problem (AJSP) is one of the most complicated combinatorial optimization problem that involves simultaneously scheduling the processing and assembly operations of complex structured products. The problem becomes even more complicated if a combination of two or more optimization criteria is considered. This thesis addresses an assembly job shop scheduling problem with multiple objectives. The objectives considered are to simultaneously minimizing makespan and total tardiness. In this thesis, two approaches viz., weighted approach and Pareto approach are used for solving the problem. However, it is quite difficult to achieve an optimal solution to this problem with traditional optimization approaches owing to the high computational complexity. Two metaheuristic techniques namely, genetic algorithm and tabu search are investigated in this thesis for solving the multiobjective assembly job shop scheduling problems. Three algorithms based on the two metaheuristic techniques for weighted approach and Pareto approach are proposed for the multi-objective assembly job shop scheduling problem (MOAJSP). A new pairing mechanism is developed for crossover operation in genetic algorithm which leads to improved solutions and faster convergence. The performances of the proposed algorithms are evaluated through a set of test problems and the results are reported. The results reveal that the proposed algorithms based on weighted approach are feasible and effective for solving MOAJSP instances according to the weight assigned to each objective criterion and the proposed algorithms based on Pareto approach are capable of producing a number of good Pareto optimal scheduling plans for MOAJSP instances.

Fixed Point Decimal Multiplication Using RPS Algorithm

Decimal multiplication is an integral part of financial, commercial, and internet-based computations. A novel design for single digit decimal multiplication that reduces the critical path delay and area for an iterative multiplier is proposed in this research. The partial products are generated using single digit multipliers, and are accumulated based on a novel RPS algorithm. This design uses n single digit multipliers for an n × n multiplication. The latency for the multiplication of two n-digit Binary Coded Decimal (BCD) operands is (n + 1) cycles and a new multiplication can begin every n cycle. The accumulation of final partial products and the first iteration of partial product generation for next set of inputs are done simultaneously. This iterative decimal multiplier offers low latency and high throughput, and can be extended for decimal floating-point multiplication.

A Novel Decision Tree Algorithm for Numeric Datasets - C 4.5*Stat

Decision trees are very powerful tools for classification in data mining tasks that involves different types of attributes. When coming to handling numeric data sets, usually they are converted first to categorical types and then classified using information gain concepts. Information gain is a very popular and useful concept which tells you, whether any benefit occurs after splitting with a given attribute as far as information content is concerned. But this process is computationally intensive for large data sets. Also popular decision tree algorithms like ID3 cannot handle numeric data sets. This paper proposes statistical variance as an alternative to information gain as well as statistical mean to split attributes in completely numerical data sets. The new algorithm has been proved to be competent with respect to its information gain counterpart C4.5 and competent with many existing decision tree algorithms against the standard UCI benchmarking datasets using the ANOVA test in statistics. The specific advantages of this proposed new algorithm are that it avoids the computational overhead of information gain computation for large data sets with many attributes, as well as it avoids the conversion to categorical data from huge numeric data sets which also is a time consuming task. So as a summary, huge numeric datasets can be directly submitted to this algorithm without any attribute mappings or information gain computations. It also blends the two closely related fields statistics and data mining

Comparative efficacy of MS-222 and benzocaine as anaesthetics under simulated transport conditions of a tropical ornamental fish Puntius filamentosus(Valenciennes)

There is a growing commercial interest in the ¢sh, Puntius ¢lamentosus, in the ornamental ¢sh trade in India and elsewhere.The trade is, however, hampered by severe mortalities during transport of the ¢sh owing to insu⁄cient data available on the use of anaesthetics. To resolve this problem, we evaluated the e⁄cacy of two anaesthetics, MS-222 and benzocaine, in sedating P. ¢lamentosus in simulated transportation experiments and used stress response parameters such as cortisol and blood glucose levels to perform assessments. We observed that MS-222 at 40 mg L 1 and benzocaine at 20mg L 1 were su⁄- cient to induce sedation for 48 h. Above these concentrations, both the anaesthetics adversely a¡ected the ¢sh and resulted inmortalities. Both anaesthetics signi¢cantly lowered the blood cortisol and glucose levels compared with the unsedated controls. Importantly, the anaesthetics treatment signi¢cantly lowered the post-transport mortality in the ¢sh. The results of the study show that MS-222 and benzocaine could be used as sedatives to alleviate transport- related stress in P. ¢lamentosus to improve their post-transport survival and hence reduce economic loss.