Automatic Threshold Selections by exploration and exploitation of optimization algorithm in Record Deduplication

A deduplication process uses similarity function to identify the two entries are duplicate or not by setting the threshold.  This threshold setting is an important issue to achieve more accuracy and it relies more on human intervention. Swarm Intelligence algorithm such as PSO and ABC have been used for automatic detection of threshold to find the duplicate records. Though the algorithms performed well there is still an insufficiency regarding the solution search equation, which is used to generate new candidate solutions based on the information of previous solutions.  The proposed work addressed two problems: first to find the optimal equation using Genetic Algorithm(GA) and next it adopts an modified  Artificial Bee Colony (ABC) to get the optimal threshold to detect the duplicate records more accurately and also it reduces human intervention. CORA dataset is considered to analyze the proposed algorithm

Unexpected Event Prediction in Wire Electrical Discharge Machining Using Deep Learning Techniques

Theoretical models of manufacturing processes provide a valuable insight into physical phenomena but their application to practical industrial situations is sometimes difficult. In the context of Industry 4.0, artificial intelligence techniques can provide efficient solutions to actual manufacturing problems when big data are available. Within the field of artificial intelligence, the use of deep learning is growing exponentially in solving many problems related to information and communication technologies (ICTs) but it still remains scarce or even rare in the field of manufacturing. In this work, deep learning is used to efficiently predict unexpected events in wire electrical discharge machining (WEDM), an advanced machining process largely used for aerospace components. The occurrence of an unexpected event, namely the change of thickness of the machined part, can be effectively predicted by recognizing hidden patterns from process signals. Based on WEDM experiments, different deep learning architectures were tested. By using a combination of a convolutional layer with gated recurrent units, thickness variation in the machined component could be predicted in 97.4% of cases, at least 2 mm in advance, which is extremely fast, acting before the process has degraded. New possibilities of deep learning for high-performance machine tools must be examined in the near future.The authors gratefully acknowledge the funding support received from the Spanish Ministry of Economy and Competitiveness and the FEDER operation program for funding the project "Scientific models and machine-tool advanced sensing techniques for efficient machining of precision components of Low Pressure Turbines" (DPI2017-82239-P) and UPV/EHU (UFI 11/29). The authors would also like to thank Euskampus and ONA-EDM for their support in this project

Process Parameters Optimization Of Micro Electric Discharge Machining Process Using Genetic Algorithm

Micro Electric Discharge Machining (micro EDM) is a non-traditional machining process which can be used for drilling micro holes in high strength to weight ratio materials like Titanium super alloy. However, the process control parameters of the machine have to be set at an optimal setting in order to achieve the desired responses. This present research study deals with the single and multiobjective optimization of micro EDM process using Genetic Algorithm. Mathematical models using Response Surface Methodology (RSM) is used to correlate the response and the parameters. The desired responses are minimum tool wear rate and minimum overcut while the independent control parameters considered are pulse on time, peak current and flushing pressure. In the multiobjective problem, the responses conflict with each other. This research provides a Pareto optimal set of solution points where each solution is a non dominated solution among the group of predicted solution points thus allowing flexibility in operating the machine while maintaining the standard quality

Optimization of machining processes using pattern search algorithm

Optimization of machining processes not only increases machining efficiency and economics, but also the end product quality. In recent years, among the traditional optimization methods, stochastic direct search optimization methods such as meta-heuristic algorithms are being increasingly applied for solving machining optimization problems. Their ability to deal with complex, multi-dimensional and ill-behaved optimization problems made them the preferred optimization tool by most researchers and practitioners. This paper introduces the use of pattern search (PS) algorithm, as a deterministic direct search optimization method, for solving machining optimization problems. To analyze the applicability and performance of the PS algorithm, six case studies of machining optimization problems, both single and multi-objective, were considered. The PS algorithm was employed to determine optimal combinations of machining parameters for different machining processes such as abrasive waterjet machining, turning, turn-milling, drilling, electrical discharge machining and wire electrical discharge machining. In each case study the optimization solutions obtained by the PS algorithm were compared with the optimization solutions that had been determined by past researchers using meta-heuristic algorithms. Analysis of obtained optimization results indicates that the PS algorithm is very applicable for solving machining optimization problems showing good competitive potential against stochastic direct search methods such as meta-heuristic algorithms. Specific features and merits of the PS algorithm were also discussed

Experimental Studies on Abrasive Water Jet Cutting of Nano SiC Particles Filled Hybrid Basalt-Glass Fibre-Reinforced Epoxy Composites

Abrasive water jet machining (AWJM) is extensively beneficial in machining materials that are hard to cut. This investigation deals with AWJM of Nano SiC filled Epoxy reinforced with basalt-glass fiber hybrid composite. The composite is prepared by compression moulding technique. Experimental trails are performed to evaluate the impact of every process parameter on the responses i.e., surface roughness (Ra) and Material Removal Rate (MRR). The experiments are conducted by changing the standoff distance (SD), traverse speed (TS) and water pressure. The performance of the conducted experiment is analysed using a Swarm intelligence algorithm. Surface roughness and MRR are maximized by using the combination of optimum process parameter levels of 9.72 mm/min speed, 5.78 mm stand-off distance and 553 MPa jet pressure. Scanning Electron Microscopic (SEM) images are employed in detecting the morphology of machined surface and confirmed the presence of voids and fibre pull-out

The design and applications of the african buffalo algorithm for general optimization problems

Optimization, basically, is the economics of science. It is concerned with the need to maximize profit and minimize cost in terms of time and resources needed to execute a given project in any field of human endeavor. There have been several scientific investigations in the past several decades on discovering effective and efficient algorithms to providing solutions to the optimization needs of mankind leading to the development of deterministic algorithms that provide exact solutions to optimization problems. In the past five decades, however, the attention of scientists has shifted from the deterministic algorithms to the stochastic ones since the latter have proven to be more robust and efficient, even though they do not guarantee exact solutions. Some of the successfully designed stochastic algorithms include Simulated Annealing, Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization, Bee Colony Optimization, Artificial Bee Colony Optimization, Firefly Optimization etc. A critical look at these ‘efficient’ stochastic algorithms reveals the need for improvements in the areas of effectiveness, the number of several parameters used, premature convergence, ability to search diverse landscapes and complex implementation strategies. The African Buffalo Optimization (ABO), which is inspired by the herd management, communication and successful grazing cultures of the African buffalos, is designed to attempt solutions to the observed shortcomings of the existing stochastic optimization algorithms. Through several experimental procedures, the ABO was used to successfully solve benchmark optimization problems in mono-modal and multimodal, constrained and unconstrained, separable and non-separable search landscapes with competitive outcomes. Moreover, the ABO algorithm was applied to solve over 100 out of the 118 benchmark symmetric and all the asymmetric travelling salesman’s problems available in TSPLIB95. Based on the successful experimentation with the novel algorithm, it is safe to conclude that the ABO is a worthy contribution to the scientific literature

An investigation into the electrochemical removal of unwanted residual material protrusions from parts

Manuscript I: The removal of residual casting material from gating has traditionally been performed by abrasive grinding techniques. However, high amounts of belt wear can occur when working with high strength alloys, especially those typically seen in the aerospace industry. An alternative machining process called electrochemical machining (ECM) uses electrolysis to precisely remove material at high rates. ECM has many advantages over conventional grinding: no tool wear, no induced mechanical or thermal stresses, and high removal rates independent of material hardness or strength. The industrial application of ECM to residual casting material removal can potentially realize large cost savings and decreased component processing time by eliminating belt wear and increasing material removal rates. The approach taken in this work is the design and fabrication of a laboratory apparatus for the purpose of testing the ECM of casting material. Commercial ECM machines, while more powerful, can be excessively large and cost prohibitive when performing an initial feasibility study. Many times these commercial machines are calibrated to mass produce a specific part, and do not have the level of variability desired for laboratory experimentation. The test apparatus presented provides a robust and relatively low cost method of investigating the applicability of ECM to this purpose. The device is comprised of an electrolyte filtration and delivery system, a stable machining enclosure, and a single axis computer controlled tool. The ECM variables that can be adjusted include electrolyte temperature, mass flow rate, applied voltage, tool feed rate, and electrode gap. Process data from these variables is collected via multiple sensors in the machine and provides real-time feedback to users. A universal tool connection and workpiece fixture allows for different experimental setups to be easily tested. From experimentation with this test apparatus, it will be possible to identify optimum methods for the ECM of these residual casting artifacts. Manuscript II: Rapid tool wear can occur during the removal of residual protrusions from high strength alloy parts. In this work, a new method of using electrochemical machining (ECM) capsules to remove protrusions without any tool wear is presented. An ECM capsule is an electrochemical cell that is placed on a part over a protrusion, and removes material through electrolysis. These capsules are advantageous due to their low cost and simplicity compared to conventional ECM equipment. The use of these capsules is demonstrated in two ways. First, a parameter optimization was performed on the material removal rates of Inconel 718 and Titanium 6-4 bar stock using a 2⁸⁻⁴ fractional factorial design of experiments. Then, using the optimized values, torch-cut protrusions were machined off of manufactured Titanium 6-4 parts. Inherent variability in the geometry of the protrusions rendered it difficult to completely remove the protrusions without cutting into the part. Surface scans of the parts showed that the capsules were able to successfully remove between 63% and 80% of each protrusion. Properly integrated into a protrusion removal operation, these ECM capsules could offer significant cost savings due to their ability to machine protrusions with no incurred tool wear

Teaching Learning based Optimization Applied to Mechanical Constrained Design Problems

Amidst all the evolutionary optimization algorithms Teaching–Learning-Based Optimization (TLBO) seems to be a promising technique with relatively competitive performances. It outperforms some of the well-known metaheuristics regarding constrained benchmark functions, constrained mechanical design, and continuous non-linear numerical optimization problems. This dissertation presents the application of TLBO to various problems of mechanical engineering. Both constrained and unconstrained optimization has been performed on some manufacturing processes and design problems. Parametric optimization of three non-conventional machining processes namely electro-discharge machining, electrochemical machining and electro-chemical discharge machining, have been carried out and the results are compared with other evolutionary algorithms. Improvement in the existing TLBO algorithm has been incorporated in this dissertation using two schemes namely bit string mutation and replacement of worst solutions with fresh ones. Performance evaluation of these modifications have been presented in this dissertation by solving six optimization problems using original TLBO and proposed modifications. It has been found that better results are achieved in reaching the global optimal values by the use of these modifications. However, the results prefer the use of bit string mutation over scheme of replacing the worst solutions with fresh solutions in addition to the original logic of TLBO. The bit wise mutation and replacement of the worst solutions with fresh ones, proved an added advantage to the existing algorithm. Both these modifications resulted in a steeper convergence rate and finally provided global optimal solutions, and in some cases even better solutions than previously published results