Designing Comminution Circuits with a Multi-Objective Evolutionary Algorithm

Mining is an important industry in Australia, contributing billions of dollars to the economy. The performance of a processing plant has a large impact on the profitability of a mining operation, yet plant design decisions are typically guided more by intuition and experience than by analysis. In this paper, we motivate the use of an evolutionary algorithm to aid in the design of such plants. We formalise plant design in terms suitable for application in a multi-objective evolutionary algorithm and create a simulation to assess the performance of candidate solutions. Results show the effectiveness of this approach with our algorithm producing designs superior to those used in practice today, promising significant financial benefits

Green Technologies for Production Processes

This book focuses on original research works about Green Technologies for Production Processes, including discrete production processes and process production processes, from various aspects that tackle product, process, and system issues in production. The aim is to report the state-of-the-art on relevant research topics and highlight the barriers, challenges, and opportunities we are facing. This book includes 22 research papers and involves energy-saving and waste reduction in production processes, design and manufacturing of green products, low carbon manufacturing and remanufacturing, management and policy for sustainable production, technologies of mitigating CO2 emissions, and other green technologies

Investigation and optimization of machining parameters in electrochemical machining of aluminium metal matrix composites

Metal matrix composites (MMCs) have been increasingly used in industries, nuclear plant, and automobiles due to their superior properties compared to other alloys, and that is owing to the tough and abrasive hard reinforced particles. It’s complicated to machine hard materials by traditional processes methods, therefore The present study focused on the investigation of parameters in electrochemical machining (ECM) like electrolyte concentration(EC), voltage(V), and Inter-electrode gap (IEG) on the radial over cut (ROC) and material removal rate (MRR) in the ECM of Al-7.5%B4C. Stir casting method was used to fabricate metal matrix composites. Based on Taguchi design, the process parameters were optimized. A Multiple Regression Model (MRM) was employed as model for radial over cut and material removal rate. The mathematical model was examined using analysis of variance (ANOVA). The EC 10 g/L, V 10 v, and IEG 0.3 mm are the optimal parametric combination for ROC. Also, the EC 30 g/L, V 18 V, and IEG 0.2 mm are the optimal parametric combination for MRR

Handbook of Computational Intelligence in Manufacturing and Production Management

Artificial intelligence (AI) is simply a way of providing a computer or a machine to think intelligently like human beings. Since human intelligence is a complex abstraction, scientists have only recently began to understand and make certain assumptions on how people think and to apply these assumptions in order to design AI programs. It is a vast knowledge base discipline that covers reasoning, machine learning, planning, intelligent search, and perception building. Traditional AI had the limitations to meet the increasing demand of search, optimization, and machine learning in the areas of large, biological, and commercial database information systems and management of factory automation for different industries such as power, automobile, aerospace, and chemical plants. The drawbacks of classical AI became more pronounced due to successive failures of the decade long Japanese project on fifth generation computing machines. The limitation of traditional AI gave rise to development of new computational methods in various applications of engineering and management problems. As a result, these computational techniques emerged as a new discipline called computational intelligence (CI)

Multi-Objective Optimization of Input Machining Parameters to Machined AISI D2 Tool Steel Material

Poor surface finish on die and mould transfers the bad quality to processed parts. High surface roughness is an example of bad surface finish that is normally reduced by manual polishing after conventional milling machining process. Therefore, in order to avoid disadvantages by manual polishing and disadvantage by the machining, a sequence of two machining operations is proposed. The main operation is run by the machining and followed by Rotary Ultrasonic Machining Assisted Milling (RUMAM). However, this sequence operation requires optimum input parameters to generate the lowest surface roughness. Hence, this paper aims to optimize the input parameters for both machining operations by three soft-computing approaches – Genetic Algorithm, Tabu Search, and Particle Swarm Optimization. The method adopted in this paper begins with a fitness function development, optimization approach usage and ends up with result evaluation and validation. The soft-computing approaches result outperforms the experiment result in having minimum surface roughness. Based on the findings, the conclusion suggests that the lower surface roughness can be obtained by applying the input parameters at maximum for the cutting speed and vibration frequency, and at minimum for machining feed rate. This finding assists manufacturers to apply proper input values to obtain parts with minimum surface roughness

Advances in Robotics, Automation and Control

The book presents an excellent overview of the recent developments in the different areas of Robotics, Automation and Control. Through its 24 chapters, this book presents topics related to control and robot design; it also introduces new mathematical tools and techniques devoted to improve the system modeling and control. An important point is the use of rational agents and heuristic techniques to cope with the computational complexity required for controlling complex systems. Through this book, we also find navigation and vision algorithms, automatic handwritten comprehension and speech recognition systems that will be included in the next generation of productive systems developed by man

Energy efficient cutting parameter optimization

Mechanical manufacturing industry consumes substantial energy with low energy efficiency. Increasing pressures from energy price and environmental directive force mechanical manufacturing industries to implement energy efficient technologies for reducing energy consumption and improving energy efficiency of their machining processes. In a practical machining process, cutting parameters are vital variables set by manufacturers in accordance with machining requirements of workpiece and machining condition. Proper selection of cutting parameters with energy consideration can effectively reduce energy consumption and improve energy efficiency of the machining process. Over the past 10 years, many researchers have been engaged in energy efficient cutting parameter optimization, and a large amount of literature have been published. This paper conducts a comprehensive literature review of current studies on energy efficient cutting parameter optimization to fully understand the recent advances in this research area. The energy consumption characteristics of machining process are analyzed by decomposing total energy consumption into electrical energy consumption of machine tool and embodied energy of cutting tool and cutting fluid. Current studies on energy efficient cutting parameter optimization by using experimental design method and energy models are reviewed in a comprehensive manner. Combined with the current status, future research directions of energy efficient cutting parameter optimization are presented