Modeling and Optimization of Gas Metal Arc Welding (GMAW) Process

Weld quality is greatly affected by the operating process parameters in the gas metal arc welding (GMAW) process. The quality of the welded material can be evaluated by many characteristics, such as bead geometric parameters, deposition efficiency, weld strength, weld distortion, et cetera. These characteristics are controlled by a number of welding process parameters, and it is important to set up proper process parameters to attain good quality. Various optimization methods can be applied to define the desired process output parameters through developing mathematical models to specify the relationship between the input parameters and output parameters. The method capable of accurate prediction of welding process output parameters would be valuable for rapid development of welding procedures and for developing control algorithms in automated welding applications. This chapter presents the details of various techniques used for modeling and optimization of GMAW process parameters. The optimization methods covered in this chapter are appropriate for modeling and optimizing the GMAW process. It is found that there is high level of interest in the adaptation of RSM and ANN techniques to predict responses and to optimize the GMAW process. Combining two optimization techniques, such as GA and RSM, would reveal good results for finding out the optimal welding conditions. Furthermore, efforts are required to apply advanced optimization techniques to find out the optimal parameters for GMAW process at which the process could be considered safe and more economical.

Computational Techniques in Statistical Analysis and Exploitation of CNC Machining Experimental Data

Extracting CNC machining data on- or off-line demands thorough and careful planning. Exploitation of this data can be carried out by statistical methods, in order to obtain the most influential parameters along with their respective level of significance. However, significance of machining parameters varies according to the posed Quality Characteristics at each machining phase. In actual experiments, measuring devices and assemblies are used, and data is recorded in computer archives. To shorten the production time and cost, machining processes are planned on CAM software, especially when complex part geometries, such as sculptured surfaces, are involved. Hence, planning machining experiments using CAM software modules is an efficient approach for experimentation on the actual CNC machine tools. Data extraction and statistical analysis methodologies are presented along with respective machining experimental examples.

Joining Sheets to Tubular Profiles by Tube Forming

This chapter presents an innovative forming process for joining sheet panels to tubular profiles at room temperature. Finite element analysis and experimentation are utilized to understand the deformation mechanics of the process, to identify the operational feasibility window, and to discuss the capabilities across the useful range of working conditions. The feasibility of the proposed joining process is demonstrated by presenting conceptual applications and industrial prototypes comprising a seat-back bottom frame and an automotive hand-brake system. Results show that joining sheets to tubular profiles by means of tube forming can successfully replace conventional joining technologies based on mechanical fixing with fasteners, welding, or structural adhesive bonding.

Developments in Finite Element Technology and Optimization Formulations for Sheet Metal Forming

This contribution aims to provide a comprehensive overview of some research developments in the field of computational mechanics and numerical simulations applied to metal forming processes. More specifically, this chapter’s goal is to encompass three main fields of research applied to plastic forming processes: (i) the development of alternative finite element formulations for the simulation of sheet metal forming processes; (ii) the development and discussion of distinct optimization procedures and formulations suitable for the characterization of constitutive parameters to be used in numerical simulations, relying on experimental result data; (iii) the study of non-conventional forming processes, particularly the case of single-point incremental forming operations. For each of these topics, a summary of the formulations and main ideas is provided, as well as a list of references for the interested reader. The main goal of this chapter is, therefore, to provide a comprehensive source of information for researchers from both academia and industrial worlds, about some recent achievements and future trends in the numerical simulation field.

Modeling and Optimization of Abrasive Water Jet Cutting of Kevlar Fiber-Reinforced Polymer Composites

This chapter presents a detailed study of abrasive water jet (AWJ) cutting of thin and thick Kevlar fiber-reinforced polymer (FRP) composites used in transport aircraft and anti-ballistic applications. Kevlar composites are considered to be very challenging to machine using traditional techniques. Most of the research conducted in the area of AWJ cutting has been limited to single response optimization. However, in real life machining, the performance of a process/product demands multi-objective optimization (MOO). No work has been reported till now using different MOO techniques for AWJ cutting of Kevlar FRP composites. Experimental modeling of depth of cut and various design of experiments based single and multi-objective optimization studies are presented here. Statistical analysis of variance has been performed to rank the different process parameters and estimate their effects on various AWJ cut kerf quality characteristics. The studies conducted in this chapter are likely to prove beneficial to the AWJ community in performing modeling and simultaneous optimization of multiple quality characteristics.

Application of Particle Swarm Optimization for Achieving Desired Surface Roughness in Tungsten-Copper Alloy Machining

The tungsten-copper electrodes are used in the manufacture of die steel and tungsten carbide workpieces due to high thermal and electrical conductivity of copper, spark erosion resistance, low thermal expansion coefficient, better arc-resistance, non-welding, and high melting temperature of tungsten. Since a tungsten-copper electrode is more expensive than traditional electrodes; there is a need to study the machinability aspects, especially the surface roughness of turned components, which has a greater influence on product quality. This chapter deals with the application of response surface methodology (RSM) for the development surface roughness model for turning of tungsten-copper alloy. The experiments were planned as per full factorial design (FFD) with cutting speed, feed rate, and depth of cut as the process parameters. The proposed surface roughness model was employed with particle swarm optimization (PSO) to optimize the parameters. PSO program gives the minimum values of surface roughness and the corresponding optimal machining parameters.

Multi-Objective Optimization of Manufacturing Processes Using Evolutionary Algorithms

Recently evolutionary algorithms have created more interest among researchers and manufacturing engineers for solving multiple-objective problems. The objective of this chapter is to give readers a comprehensive understanding and also to give a better insight into the applications of solving multi-objective problems using evolutionary algorithms for manufacturing processes. The most important feature of evolutionary algorithms is that it can successfully find globally optimal solutions without getting restricted to local optima. This chapter introduces the reader with the basic concepts of single-objective optimization, multi-objective optimization, as well as evolutionary algorithms, and also gives an overview of its salient features. Some of the evolutionary algorithms widely used by researchers for solving multiple objectives have been presented and compared. Among the evolutionary algorithms, the Non-dominated Sorting Genetic Algorithm (NSGA) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) have emerged as most efficient algorithms for solving multi-objective problems in manufacturing processes. The NSGA method applied to a complex manufacturing process, namely plateau honing process, considering multiple objectives, has been detailed with a case study. The chapter concludes by suggesting implementation of evolutionary algorithms in different research areas which hold promise for future applications.

Simulation of Grinding by Means of the Finite Element Method and Artificial Neural Networks

Simulation of grinding is a topic of great interest due to the wide application of the process in modern industry. Several modeling methods have been utilized in order to accurately describe the complex phenomena taking place during the process, the most common being the Finite Element Method (FEM) and the Artificial Neural Networks (ANN). In the present work, a FEM model and an ANN model for precision surface grinding, are presented. Furthermore, a new approach, a combination of the aforementioned methods, is proposed, and a hybrid model is presented. This model comprises the advantages of both FEM and ANN models. The three kinds of models described in this work are able to accurately predict several grinding features that define the outcome of the process and the quality of the final product.

Models and Optimization Techniques of Machining Parameters in Turning Operations

Machining parameters optimization is one of the most essential and interesting problems in manufacturing world. Efficient optimization of machining parameters can produce high-quality products with low cost and high productivity. Thus, many process optimization models of the turning operations with one or two tools are established in order to realize various machining aims. Due to the complexity of optimization models, many new optimization techniques are proposed to solve them. Major optimization techniques include genetic algorithms, simulated annealing, ant colony optimization, particle swarm optimization, et cetera. In this chapter, a comprehensive discussion on various soft computing techniques are presented, especially meta-heuristic algorithms concerning optimization of machining parameters in both single-tool and multi-tools turning operations. In addition, some future challenges and research trends are also discussed in this chapter.

Application of Soft-Computing Methods in Cellular Manufacturing

The essential problem in Cellular Manufacturing System (CMS) is to identify the machine cells and subsequent part families with an aim to curtail the intercell and intracell traffic, known as Cell Formation Problem (CFP). This chapter portrays the need of soft-computing methods to model the CFP to attain enhanced solutions. The novelty of this chapter is in developing a hybrid state-of-the-art metaheuristic approach, namely SAHCF (Simulated Annealing Heuristic to Cell Formation), to solve the binary CFP, and further, a Fuzzy-ART based hybrid technique is framed to solve the generalized CFP using operational time. The proposed techniques are tested on the test datasets published in the past literature. Both the techniques are shown to outperform the published methods available in literature and attained enhanced results by exceeding the solution quality on the test problems. The originality of this study lies in designing simple and efficient methodologies to produce near optimal solutions for the shop-floor managers with minimum computing abilities and time.