Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

The Optimization of Packaging System Process Parameters Using Taguchi Method

Packaging systems constitute substantially to product cost, its safety, and optimization. Unfortunately, no previous optimization studies have examined the packaging system in a bottling process plant for the unique, developing country environment. Consequently, the Taguchi method is applied to optimize a process plant's packaging system in a Nigerian plant's real-life situation. Optimal combinations of packaging system parameters that minimize product waste are created. An L4 (23) Taguchi orthogonal array was selected to analyze the data, and signal-to-noise ratios were computed for each experiment's run. Since the aim was to minimize beer waste, the ‘the-smaller-the-better’ signal-to-noise ratio was chosen in the analysis. S/N ratio plots revealed the optimum settings to obtain minimal product waste, namely, A2, B1, and C2 from the main effects plot for signal-to-noise ratios. A two-way ANOVA was performed on the significant factors to determine their percentage contributions to the response (product waste). Through Taguchi's innovative approach, the feasibility of optimizing the packaging process parameters was demonstrated and validated

Optimization of Packaging Process Parameters Using Combined Taguchi Method-present Worth Method/Inflationary Factor Validated

In a previous article, the packaging optimization literature illustrated how to optimize the packaging parameters using the classical Taguchi approach. Notwithstanding, it is compelling to bridge the gap created in the article for further advancement. Thus, this paper contributes to expanding scholarship regarding the area. The paper targets the Taguchi methodical optimization literature in which several scores of research across engineering disciplines and beyond have been undertaken. At present, evaluation using the Taguchi method often stops at the determination of the optimal settings from the response table through a chain of steps from factor-level selection, orthogonal array choice, signal-to-noise determination, and response table evaluation to emerge the optimal parameters. There is no information on the economic aspects of the parameters, yet processes are expected to be sustainable while economic factors play a central role. A novel idea of the present work is introduced where the interest rate and inflationary factor with levels are used to determine the economic strength of each parameter through ranking. Data was collected from a brewery process, and literature data concerning cold arc welding parametric evaluation was used. The outcome demonstrates the workability of the method in the packaging plant and cold arc welding process. The work is useful for packaging managers and welding engineers for planning purposes

Industry/University Collaboration at the University of Michigan-Dearborn: A Focus on Relevant Technology

https://deepblue.lib.umich.edu/bitstream/2027.42/154105/1/kampfner1997.pd

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