Mohd Fadzil Faisae Ab Rashid

A Review of Multi-holes Drilling Path Optimization Using Soft Computing Approaches

In today’s competitive environment, optimization is considered as an important element for maintaining and improving both aspect of manufacturing such as quality and productivity. In multi-holes drilling process, 70% of the machining time involved the tool movement and tool switching. Various researches had been conducted to reduce the tool movement and switching time. This paper reviews the research publications on the drilling path optimization using soft computing approaches. In particular, this review focuses on four main aspects; drilling application areas, problem modeling, optimization algorithms and objective functions of drilling path optimization. Based on the review, the researchers’ interest in this area is still growing. However, the existing researches were limited to implement, modify and hybridized the well-established optimization algorithms. Furthermore, there is a lack of awareness to consider the environmental and sustainable issues in the existing research. In future, the researcher is suggested to give focus on energy consumption that related with sustainable manufacturing and also to explore the potential of new meta-heuristics algorithms that can lead to significant in reduction machining time.

Optimization of Multi-holes Drilling Path Using Particle Swarm Optimization

In multi-holes drilling process, the tool movement and tool switching consumed on average 70% of the total machining time. Tool path optimization is able to reduce the time taken in machining process. This paper is focus on the modeling and optimization of multi-holes drilling path. The problem is modeled as traveling salesman problem (TSP) and optimized using Particle Swarm Optimization (PSO). To test the PSO performance, 15 test problems were created with different range of holes numbers. The optimization results from PSO were compared with other top algorithms such Genetic Algorithm and Ant Colony Optimization algorithm. PSO is also compared with another algorithm like Whale Optimization Algorithm, Ant Lion Optimizer, Dragonfly Algorithm, Grasshopper Optimization Algorithm, Moth-flame Optimization and Sine Cosine Algorithm. The result indicates that PSO algorithm is performed better than comparison algorithms. PSO algorithm gives the minimum value of fitness path and their CPU time compared to other algorithms. Hence, the smaller their value, the algorithm is better and more efficient. In future, researchers should more focus on environmental issues and energy consumption for sustainable manufacturing. Besides, need to explore other potential of new meta-heuristics algorithms to increase the hole drilling operation efficiencies.In multi-holes drilling process, the tool movement and tool switching consumed on average 70% of the total machining time. Tool path optimization is able to reduce the time taken in machining process. This paper is focus on the modeling and optimization of multi-holes drilling path. The problem is modeled as traveling salesman problem (TSP) and optimized using Particle Swarm Optimization (PSO). To test the PSO performance, 15 test problems were created with different range of holes numbers. The optimization results from PSO were compared with other top algorithms such Genetic Algorithm and Ant Colony Optimization algorithm. PSO is also compared with another algorithm like Whale Optimization Algorithm, Ant Lion Optimizer, Dragonfly Algorithm, Grasshopper Optimization Algorithm, Moth-flame Optimization and Sine Cosine Algorithm. The result indicates that PSO algorithm is performed better than comparison algorithms. PSO algorithm gives the minimum value of fitness path and their CPU time compared to other algorithms. Hence, the smaller their value, the algorithm is better and more efficient. In future, researchers should more focus on environmental issues and energy consumption for sustainable manufacturing. Besides, need to explore other potential of new meta-heuristics algorithms to increase the hole drilling operation efficiencies.

Optimisation of Assembly Line Balancing Type-E with Resource Constraints Using NSGA-II

Assembly line balancing of Type-E problem (ALB-E) is an attempt to assign the tasks to the various workstations along the line so that the precedence relations are satisfied and some performance measures are optimised. A majority of the recent studies in ALB-E assume that any assembly task can be assigned to any workstation. This assumption lead to higher usage of resource required in assembly line. This research studies assembly line balancing of Type-E problem with resource constraint (ALBE-RC) for a single-model. In this work, three objective functions are considered, i.e. minimise number of workstation, cycle time and number of resources. In this paper, an Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) has been proposed to optimise the problem. Six benchmark problems have been used to test the optimisation algorithm and the results are compared to multi-objective genetic algorithm (MOGA) and hybrid genetic algorithm (HGA). From the computational test, it was found NSGA-II has the ability to explore search space, has better accuracy of solution and also has a uniformly spaced solution. In future, a research to improve the solution accuracy is proposed to enhance the performance of the algorithm.

Mathematical Modelling of Mixed-Model Assembly Line Balancing Problem with Resources Constraints

Modern manufacturing industries nowadays encounter with the challenges to provide a product variety in their production at a cheaper cost. This situation requires for a system that flexible with cost competent such as Mixed-Model Assembly Line. This paper developed a mathematical model for Mixed-Model Assembly Line Balancing Problem (MMALBP). In addition to the existing works that consider minimize cycle time, workstation and product rate variation, this paper also consider the resources constraint in the problem modelling. Based on the finding, the modelling results achieved by using computational method were in line with the manual calculation for the evaluated objective functions. Hence, it provided an evidence to verify the developed mathematical model for MMALBP. Implications of the results and future research directions were also presented in this paper.

Implementation of Ant Colony Optimization Algorithm to Minimize Cost of Turning Process

In machining process, turning is one of process that were significantly change by introduction of computer numerical control (CNC). However, the process improvement is not stopping there, but the focused has change to reduce the machining cost. Improper parameter selection will caused vibration in cutting, unsecure workpiece, unappealing finishing and cost consuming. Therefore, the optimum parameter setting is required because it related to certain quality characteristics such as the unit production cost. This paper presents the study to minimize production cost for CNC turning process by using Ant Colony Optimization (ACO). The result shows that, the ACO was capable to search for optimum production cost in shorter time compare to other methods, including Genetic Algorithm.