Application of Standard Deviation Method Integrated PSO Approach in Optimization of Manufacturing Process Parameters

Multi-objective optimization is one of the most popular research areas in the world of manufacturing. It concerns the manufacturing optimization problems involving more than one optimization simultaneously, but in this present scenario, it is becoming very tough to solve a manufacturing-related multi-objective problem as no logical method has been developed in assignment of response individual weight. Therefore, to tackle this problem, this chapter proposes a new integrated approach by combining Standard Deviation Method with Particle Swarm Optimization. Two examples of optimizing the advanced manufacturing process parameters are performed to test the proposed approach. The examples considered for this approach are also attempted using other established optimization techniques such as Desirability-based RSM and SDM-GA. The results verify the effectiveness of the proposed approach during multi-objective manufacturing process parameter optimization.

From Optimization to Clustering

In the past two decades, Swarm Intelligence (SI)-based optimization techniques have drawn the attention of many researchers for finding an efficient solution to optimization problems. Swarm intelligence techniques are characterized by their decentralized way of working that mimics the behavior of colony of ants, swarm of bees, flock of birds, or school of fishes. Algorithmic simplicity and effectiveness of swarm intelligence techniques have made it a powerful tool for solving global optimization problems. Simulation studies of the graceful, but unpredictable, choreography of bird flocks led to the design of the particle swarm optimization algorithm. Studies of the foraging behavior of ants resulted in the development of ant colony optimization algorithm. This chapter provides insight into swarm intelligence techniques, specifically particle swarm optimization and its variants. The objective of this chapter is twofold: First, it describes how swarm intelligence techniques are employed to solve various optimization problems. Second, it describes how swarm intelligence techniques are efficiently applied for clustering, by imposing clustering as an optimization problem.

Application of Fuzzy Logic for Mapping the Agro-Ecological Zones

The mapping of agro-ecological zone, which is integrated with the suitability of land evaluation, will determine the ideal farming system. The ideal farming system including sustainable land management will support the food security scenario of a region. In this chapter, the implementation of fuzzy logic for mapping the agro-ecological zone is discussed. The agro-ecological zone in Boyolali is used as case study in which the mapping considers its physiographic characteristics and climate. Two physiographic characteristics are involved: slope of the land and elevation. Rainfall is used as representative of climate. The experiment results reveal that simple membership function with the Mamdani inferences system could help decision makers to classify the agricultural land in Boyolali.

An Intelligent Process Development Using Fusion of Genetic Algorithm with Fuzzy Logic

Intelligent System (IS) can be defined as the system that incorporates intelligence into applications being handled by machines. The chapter extensively discusses the role of Genetic Algorithm (GA) in the search and optimization process along with discussing applications developed so far. A very detailed discussion on the Fuzzy Rule-Based System is presented along with major applications developed in different domains. The chapter presents algorithm of implementing intelligent procedure to decide whether a patient is prone to heart disease or not. The procedure evolves solutions using genetic operators and provides its decision automatically. The chapter presents discussion on the results achieved as a result of prototypical implementation of the evolutionary fuzzy hybrid model. The significant advantage of the presented research work is that applications that do not have any mathematical formulation and still demand optimization can be easily solved using the designed approach.

Fuzzy Logic in Healthcare

The medical decision-making process is fuzzy in its nature. The physician handles linguistic concepts in deciding the diagnosis and prognosis. The conversion from this fuzzy nature into crisp real world outcome causes the loss of precision. Fuzzy logic is a suitable way to provide the physician with the support he needs in handling linguistic concepts and get rid of the loss of precision. Fuzzy logic technologies are applied to each area of medicine, and they have been proven to be successful. The literature shows that the medical area has a great compatibility with fuzzy logic technology. Fuzzy cognitive maps, fuzzy expert systems, fuzzy medical image processing, fuzzy applications in information retrieval from medical databases, fuzzy medical data mining, and hybrid fuzzy applications are the common and most known fuzzy logic usage areas in the medical field. This chapter is a descriptive study that examines and explains the common fuzzy logic applications in the medical field after an introduction to fuzzy logic.

Comparison of Uncertainties in Membership Function of Adaptive Lyapunov NeuroFuzzy-2 for Damping Power Oscillations

The direct focus of this chapter is to explore the potential of online Adaptive NeuroFuzzy Type-2 (ANFT2) control system for damping inter-area oscillations using Static Synchronous Compensator (STATCOM). The nonlinear ANFT2-based direct control scheme is proposed to damp inter-area oscillations by utilizing its model free and universal approximation capabilities. The Gaussian and triangular membership functions with different variations of uncertain mean and standard deviation are considered for ANFT2. The adaptation mechanism utilizes gradient descent-based back-propagation algorithm using Lyapunov stability criteria to update the rule parameters. The performance evaluation of proposed control strategy has been validated using two and three machine power systems. The nonlinear time domain simulations reveal that ANFT2 has excellent damping capabilities as compared to conventional PI control. Simulation results for different performance indices further emphasize the optimal performance of ANFT2 with uncertain mean and variance of triangular membership function in transient and steady state region.

A Stochastic Approach to Product-Driven Supply Chain Design

Supply chain is an alliance of independent business processes, such as supplier, manufacturing, and distribution processes that perform the critical functions in the order fulfillment process. However, the discussions in marketing and logistic literature universally conclude that it would be desirable to determine the life cycle of products in the firm, as they have a great impact on appropriate supply chain design. Designing a supply chain effectively is a complex and challenging task, due to the increasing outsourcing, globalization of businesses, continuous advances in information technology, and product life cycle uncertainty. Indeed, uncertainty is one of the characteristics of the product life cycle. In particular, the strategic design of the supply chain has to take uncertain information into account. This chapter presents a two-phase mathematical programming approach for effective supply chain design with product life cycle uncertainty considerations.

Robust Control and Synchronization of Chaotic Systems with Actuator Constraints

This chapter proposes a robust control approach for the class of chaotic systems subject to magnitude and rate actuator constraints. The approach consists of decomposing the chaotic system into a linear part plus a nonlinear part to form an augmented system comprising the system itself and the integral of the output error. The resulting system is posteriorly seen as a linear system plus a bounded disturbance, and two robust controllers are applied: first, a controller based on a generalization of the Lyapunov function, then a Linear-Quadratic Regulator (LQR) with a prescribed degree of stability. Numerical simulations are performed to validate the approach applying it to the Lorenz chaotic system and to a chaotic aeroelastic system, and parameter uncertainties are also considered to prove its robustness. The results confirm the effectiveness of the approach, and the constraints are guaranteed as opposed to other control techniques which do not consider any kind of constraints.

Cancer Biomarker Assessment Using Evolutionary Rough Multi-Objective Optimization Algorithm

A hybrid unsupervised learning algorithm, which is termed as Evolutionary Rough Multi-Objective Optimization (ERMOO) algorithm, is proposed in this chapter. It comprises a judicious integration of the principles of the rough sets theory with the archived multi-objective simulated annealing approach. While the concept of boundary approximations of rough sets in this implementation deals with the incompleteness in the dynamic classification method with the quality of classification coefficient as the classificatory competence measurement, it enables faster convergence of the Pareto-archived evolution strategy. It incorporates both the rough set-based dynamic archive classification method in this algorithm. A measure of the amount of domination between two solutions is incorporated in this chapter to determine the acceptance probability of a new solution with an improvement in the spread of the non-dominated solutions in the Pareto-front by adopting rough sets theory. The performance is demonstrated on real-life breast cancer dataset for identification of Cancer Associated Fibroblasts (CAFs) within the tumor stroma, and the identified biomarkers are reported. Moreover, biological significance tests are carried out for the obtained markers.

GPR and RVM-Based Predictions of Surface and Hole Quality in Drilling of AISI D2 Cold Work Tool Steel

This chapter examines the capability of Gaussian Process Regression (GPR) and Relevance Vector Machine (RVM) for prediction of surface and hole quality in drilling of AISI D2 cold work tool steel. This chapter uses GPR and RVM as regression techniques. The database contains information about cutting tool, feed rate, cutting speed, surface roughness, and roundness error. Cutting tool, feed rate, and cutting speed are considered inputs of GPR and RVM. The outputs of GPR and RVM are surface roughness and roundness error. In RVM, radial basis function is adopted as kernel function. GPR uses radial basis function as covariance function. The obtained variance can be used to determine uncertainty. A sensitivity analysis is also carried out. This chapter gives robust models based on RVM and GPR for prediction of surface and hole quality in drilling of AISI D2 cold work tool steel.