Optimization of injection molding parameters for WC-TaC-6Co

The main purpose of this paper is to optimize the injection moulding parameters of WC-Co with TaC as Grain Growth Inhibitor (GGI), through Design of Experiment – Taguchi Method. The selected responses that need to be controlled are shrinkage and warpage. The parameters that were taken into considerations were GGI percentage, injection temperature, injection pressure and injection speed. In this study, L9 (34) orthogonal array from Taguchi Method was chosen as experimental setup and the responses were analyzed using Minitab version 16. Best parameters combinations chosen are, GGI (0.8 wt. % and 1.2 wt. %), injection temperature (145°C), injection pressure (45% and 55%) and injection speed (40%) for minimal shrinkage and warpage. Based on these findings, it is concluded that by controlling the optimum parameter setting, best quality of desired product can be easily achieved and maintained throughout the process

Parameter optimization of metal injection moulding: a review

Metal injection moulding (MIM) is a newly developed technology to form metals and alloys into desired shape. MIM is a combination of conventional plastic injection moulding and powder metallurgy. Detailed analysis of powders, binders, injection moulding, binder removal process and parameter optimization process is discussed. Taguchi method can also be applied for optimal design configurations when significant interactions exist between and among the controlled variables. This paper provides an overview of MIM process, optimization of parameters using Taguchi method

Major Advances in Particle Swarm Optimization: Theory, Analysis, and Application

Over the ages, nature has constantly been a rich source of inspiration for science, with much still to discover about and learn from. Swarm Intelligence (SI), a major branch of artificial intelligence, was rendered to model the collective behavior of social swarms in nature. Ultimately, Particle Swarm Optimization algorithm (PSO) is arguably one of the most popular SI paradigms. Over the past two decades, PSO has been applied successfully, with good return as well, in a wide variety of fields of science and technology with a wider range of complex optimization problems, thereby occupying a prominent position in the optimization field. However, through in-depth studies, a number of problems with the algorithm have been detected and identified; e.g., issues regarding convergence, diversity, and stability. Consequently, since its birth in the mid-1990s, PSO has witnessed a myriad of enhancements, extensions, and variants in various aspects of the algorithm, specifically after the twentieth century, and the related research has therefore now reached an impressive state. In this paper, a rigorous yet systematic review is presented to organize and summarize the information on the PSO algorithm and the developments and trends of its most basic as well as of some of the very notable implementations that have been introduced recently, bearing in mind the coverage of paradigm, theory, hybridization, parallelization, complex optimization, and the diverse applications of the algorithm, making it more accessible. Ease for researchers to determine which PSO variant is currently best suited or to be invented for a given optimization problem or application. This up-to-date review also highlights the current pressing issues and intriguing open challenges haunting PSO, prompting scholars and researchers to conduct further research both on the theory and application of the algorithm in the forthcoming years