Optimization of process parameters in the abrasive waterjet machining using integrated SA-GA

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

Challenges towards Structural Integrity and Performance Improvement of Welded Structures

Welding is a fabrication process that joint materials, is extensively utilized in almost every field of metal constructions. Heterogeneity in mechanical properties, metallurgical and geometrical defects, post-weld residual stresses and distortion due to non-linear welding processes are prime concerns for performance reduction and failures of welded structures. Consequently, structural integrity analysis and performance improvement of weld joints are important issues that must be considered for structural safety and durability under loading. In this study, an extensive experimental program and analysis were undertaken on the challenges towards structural integrity analysis and performance improvement of different welded joints. Two widely used welding techniques including solid-state “friction- stir- welding (FSW)” and fusion arc “gas tungsten arc welding (GTAW)” were employed on two widely utilized materials, namely aluminum alloys and structural steels. Various destructive and non-destructive techniques were utilized for structural integrity analysis of the welded joints. Furthermore, various “post-weld treatment (PWT)” techniques were employed to improve mechanical performances of weld joints. The work herein is divided into six different sections including: (i) Establishment of an empirical correlation for FSW of aluminum alloys. The developed empirical correlation relates the three critical FSW process parameters and was found to successfully distinguish defective and defect-free weld schedules; (ii) Development of an optimized “adaptive neuro-fuzzy inference system (ANFIS)” model utilizing welding process parameters to predict ultimate tensile strength (UTS) of FSW joints; (iii) Determination of an optimum post-weld heat treatment (PWHT) condition for FS-welded aluminum alloys; (iv) Exploration on the influence of non-destructively evaluated weld-defects and obtain an optimum PWHT condition for GTA-welded aluminum alloys; (v) Investigation on the influence of PWHT and electrolytic-plasma-processing (EPP) on the performance of welded structural steel joints; and finally, (vi) Biaxial fatigue behavior evaluation of welded structural steel joints. The experimental research could be utilized to obtain defect free weld joints, establish weld acceptance/rejection criteria, and for the better design of welded aluminum alloy and steel structures. All attempted research steps mentioned above were carried out successfully. The results obtained within this effort will increase overall understanding of the structural integrity of welded aluminum alloys and steel structures

Artificial cognitive architecture with self-learning and self-optimization capabilities. Case studies in micromachining processes

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura : 22-09-201

Experimental Investigations on Machining of CFRP Composites: Study of Parametric Influence and Machining Performance Optimization

Carbon Fiber Reinforced Polymer (CFRP) composites are characterized by their excellent mechanical properties (high specific strength and stiffness, light weight, high damping capacity etc.) as compared to conventional metals, which results in their increased utilization especially for aircraft and aerospace applications, automotive, defense as well as sporting industries. With increasing applications of CFRP composites, determining economical techniques of production is very important. However, as compared to conventional metals, machining behavior of composites is somewhat different. This is mainly because these materials behave extremely abrasive during machining operations. Machining of CFRP appears difficult due to their material discontinuity, inhomogeneity and anisotropic nature. Moreover, the machining behavior of composites largely depends on the fiber form, the fiber content, fiber orientations of composites and the variability of matrix material. Difficulties are faced during machining of composites due to occurrence of various modes of damages like fiber breakage, matrix cracking, fiber–matrix debonding and delamination. Hence, adequate knowledge and in-depth understanding of the process behavior is indeed necessary to identify the most favorable machining environment in view of various requirements of process performance yields. In this context, present work attempts to investigate aspects of machining performance optimization during machining (turning and drilling) of CFRP composites. In case of turning experiments, the following parameters viz. cutting force, Material Removal Rate (MRR), roughness average (Ra) and maximum tool-tip temperature generated during machining have been considered as process output responses. In case of drilling, the following process performance features viz. load (thrust), torque, roughness average (of the drilled hole) and delamination factor (entry and exit both) have been considered. Attempt has been made to determine the optimal machining parameters setting that can simultaneously satisfy aforesaid response features up to the desired extent. Using Fuzzy Inference System (FIS), multiple response features have been aggregated to obtain an equivalent single performance index called Multi-Performance Characteristic Index (MPCI). A nonlinear regression model has been established in which MPCI has been represented as a function of the machining parameters under consideration. The aforesaid regression model has been considered as the fitness function, and finally optimized by evolutionary algorithms like Harmony Search (HS), Teaching-Learning Based Optimization (TLBO), and Imperialist Competitive Algorithm (ICA) etc. However, the limitation of these algorithms is that they assume a continuous search within parametric domain. These algorithms can give global optima; but the predicted optimal setting may not be possible to adjust in the machine/setup. Since, in most of the machines/setups, provision is given only to adjust factors (process input parameters) at some discrete levels. On the contrary, Taguchi method is based on discrete search philosophy in which predicted optimal setting can easily be achieved in reality.However, Taguchi method fails to solve multi-response optimization problems. Another important aspect that comes into picture while dealing with multi-response optimization problems is the existence of response correlation. Existing Taguchi based integrated optimization approaches (grey-Taguchi, utility-Taguchi, desirability function based Taguchi, TOPSIS, MOORA etc.) may provide erroneous outcome unless response correlation is eliminated. To get rid of that, the present work proposes a PCA-FuzzyTaguchi integrated optimization approach for correlated multi-response optimization in the context of machining CFRP composites. Application potential of aforementioned approach has been compared over various evolutionary algorithms