A hybrid approach for selection of optimal process parameters in abrasive water jet cutting
01 Sep 2000-Vol. 214, Iss: 9, pp 781-791
TL;DR: In this article, a hybrid approach combining fuzzy logic principles and a genetic approach for selecting optimal parameters in abrasive water jet cutting of any material with predetermined thickness is presented, where a fuzzy model is built with the knowledge base formed by means of experimental data that is generated by varying the process parameters such as water jet pressure, jet traverse rate and abrasive flowrate at five levels each.
Abstract: This paper presents a hybrid approach combining fuzzy logic principles and a genetic approach for selecting optimal parameters in abrasive water jet cutting of any material with predetermined thickness. A fuzzy model is built with the knowledge base formed by means of experimental data that is generated by varying the process parameters such as water jet pressure, jet traverse rate and abrasive flowrate at five levels each. This particular model predicts the depth of cut achievable with any given set of process parameters. A genetic algorithm employed in combination with a fuzzy model automatically determines the best combination of process parameters in abrasive water jet cutting of any material. The effectiveness of the proposed approach is demonstrated by a case study dealing with abrasive water jet cutting of black granite.
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TL;DR: In this article, the researches made on Injection type abrasive water jet (AWJ) machining process as it is widely accepted by researchers and Industries for solving various issues.
144 citations
TL;DR: Five types of analysis on the abilities, limitations and effectual modifications of FL in modeling based on the comments from previous works that conduct experiment using FL in the modeling and review by few authors lead the author to conclude that FL is the most popular AI techniques used in modeling of machining process.
Abstract: The application of artificial intelligence (AI) techniques in modeling of machining process has been investigated by many researchers. Fuzzy logic (FL) as a well-known AI technique is effectively used in modeling of machining processes such as to predict the surface roughness and to control the cutting force in various machining processes. This paper is started with the introduction to definition of FL and machining process, and their relation. This paper then presents five types of analysis conducted on FL techniques used in machining process. FL was considered for prediction, selection, monitoring, control and optimization of machining process. Literature showed that milling contributed the highest number of machining operation that was modeled using FL. In terms of machining performance, surface roughness was mostly studied with FL model. In terms of fuzzy components, center of gravity method was mostly used to perform defuzzification, and triangular was mostly considered to perform membership function. The reviews extend the analysis on the abilities, limitations and effectual modifications of FL in modeling based on the comments from previous works that conduct experiment using FL in the modeling and review by few authors. The analysis leads the author to conclude that FL is the most popular AI techniques used in modeling of machining process.
118 citations
TL;DR: In this study, Artificial Neural Network and Simulated Annealing techniques were integrated labeled as integrated ANN-SA to estimate optimal process parameters in abrasive waterjet (AWJ) machining operation to find the optimal values of the process parameters.
Abstract: In this study, Artificial Neural Network (ANN) and Simulated Annealing (SA) techniques were integrated labeled as integrated ANN-SA to estimate optimal process parameters in abrasive waterjet (AWJ) machining operation. The considered process parameters include traverse speed, waterjet pressure, standoff distance, abrasive grit size and abrasive flow rate. The quality of the cutting of machined-material is assessed by looking to the roughness average value (R"a). The optimal values of the process parameters are targeted for giving a minimum value of R"a. It was evidence that integrated ANN-SA is capable of giving much lower value of R"a at the recommended optimal process parameters compared to the result of experimental and ANN single-based modeling. The number of iterations for the optimal solutions is also decreased compared to the result of SA single-based optimization.
79 citations
01 Dec 2011
TL;DR: The results showed that both of the proposed integration systems managed to estimate the optimal process parameters, leading to the minimum value of machining performance when compared to the result of real experimental data.
Abstract: In this study, Simulated Annealing (SA) and Genetic Algorithm (GA) soft computing techniques are integrated to estimate optimal process parameters that lead to a minimum value of machining performance. Two integration systems are proposed, labeled as integrated SA-GA-type1 and integrated SA-GA-type2. The approaches proposed in this study involve six modules, which are experimental data, regression modeling, SA optimization, GA optimization, integrated SA-GA-type1 optimization, and integrated SA-GA-type2 optimization. The objectives of the proposed integrated SA-GA-type1 and integrated SA-GA-type2 are to estimate the minimum value of the machining performance compared to the machining performance value of the experimental data and regression modeling, to estimate the optimal process parameters values that has to be within the range of the minimum and maximum process parameter values of experimental design, and to estimate the optimal solution of process parameters with a small number of iteration compared to the optimal solution of process parameters with SA and GA optimization. The process parameters and machining performance considered in this work deal with the real experimental data in the abrasive waterjet machining (AWJ) process. The results of this study showed that both of the proposed integration systems managed to estimate the optimal process parameters, leading to the minimum value of machining performance when compared to the result of real experimental data.
78 citations
01 Jan 2008
TL;DR: A neuro-genetic approach is proposed to suggest the process parameters for maintaining the desired depth of cut in abrasive waterjet (AWJ) cutting by considering the change in diameter of focusing nozzle, i.e. for adaptive control of AWJ cutting process.
Abstract: This paper presents a neuro-genetic approach proposed to suggest the process parameters for maintaining the desired depth of cut in abrasive waterjet (AWJ) cutting by considering the change in diameter of focusing nozzle, i.e. for adaptive control of AWJ cutting process. An artificial neural network (ANN) based model is developed for prediction of depth of cut by considering the diameter of focusing nozzle along with the controllable process parameters such as water pressure, abrasive flow rate, jet traverse rate. ANN model combined with genetic algorithm (GA), i.e. neuro-genetic approach, is proposed to suggest the process parameters. Further, the merits of the proposed approach is shown by comparing the results obtained with the proposed approach to the results obtained with fuzzy-genetic approach [P.S. Chakravarthy, N. Ramesh Babu, A hybrid approach for selection of optimal process parameters in abrasive water jet cutting, Proceedings of the Institution of Mechanical Engineers, Part B: J. Eng. Manuf. 214 (2000) 781-791]. Finally, the effectiveness of the proposed approach is assessed by conducting the experiments with the suggested process parameters and comparing them with the desired results.
58 citations
References
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TL;DR: GA's are shown to be effective for both levels of the systems optimization problem and are applied to the second level task of identifying efficient GA's for a set of numerical optimization problems.
Abstract: The task of optimizing a complex system presents at least two levels of problems for the system designer. First, a class of optimization algorithms must be chosen that is suitable for application to the system. Second, various parameters of the optimization algorithm need to be tuned for efficiency. A class of adaptive search procedures called genetic algorithms (GA) has been used to optimize a wide variety of complex systems. GA's are applied to the second level task of identifying efficient GA's for a set of numerical optimization problems. The results are validated on an image registration problem. GA's are shown to be effective for both levels of the systems optimization problem.
2,924 citations
310 citations
TL;DR: In this paper, the authors presented a model for predicting the depth of cut of abrasive-waterjets in different metals based on an improved model of erosion by solid particle impact, which is also presented.
Abstract: Ultrahigh-pressure abrasive-waterjets (AWJs) are being developed as net shape and near-net-shape machining tools for hard-to-machine materials. These tools offer significant advantages over existing techniques, including technical, economical, environmental, and safety concerns. Predicting the cutting results, however, is a difficult task and a major effort in this development process. This paper presents a model for predicting the depth of cut of abrasive-waterjets in different metals. This new model is based on an improved model of erosion by solid particle impact, which is also presented. The erosion model accounts for the physical and geometrical characteristics of the eroding particle and results in a velocity exponent of 2.5, which is in agreement with erosion data in the literature. The erosion model is used with a kinematic jet-solid penetration model to yield expressions for depths of cut according to different modes of erosion along the cutting kerf. This kinematic model was developed previously through visualization of the cutting process. The depth of cut consists of two parts: one due to a cutting wear mode at shallow angles of impact, and the other due to a deformation wear mode at large angles of impact. The predictions of the AWJ cutting model are checked against a large database of cutting results for a wide range of parameters and metal types. Materials are characterized by two properties: the dynamic flow stress, and the threshold particle velocity. The dynamic flow stress used in the erosion model was found to correlate with a typical modulus of elasticity for metals. The threshold particle velocity was determined by best fitting the model to the experimental results. Model predictions agree well with experimental results, with correlation coefficients of over 0.9 for many of the metals considered in this study.
217 citations
15 Aug 1995
199 citations
TL;DR: In this paper, experimental techniques based on statistical experimental design principles and theoretical investigations were conducted to study abrasive water jet (AWJ) cutting of alumina-based ceramics, and semi-empirical cutting depth equations were determined for the prediction and optimization of the AWJ cutting performance.
Abstract: An abrasive water jet (AWJ) can provide a more effective means for precision cutting of ceramic materials as compared with conventional machining methods, but many aspects about this cutting technology are still under flux and development. In this study, experimental techniques based on statistical experimental design principles and theoretical investigations were conducted to study AWJ cutting of alumina-based ceramics. Semi-empirical cutting depth equations are determined for the prediction and optimization of the AWJ cutting performance. Topographical characteristics of uncut-through kerf and the effects of various parameters are discussed. In addition, visualization studies are conducted to develop further understanding of the macromechanics of the AWJ cutting process.
106 citations