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Reducing machining time in a milling process is one of the important criteria to improve the overall efficiency of the machining process. This paper presents a study on the reduction of machining time, focusing on contour parallel machining to increase the efficiency and performance during the machining process. One method to enhance the performance of contour parallel machining is by defining a tool path interval that is larger than the radius of the cutting tool in a roughing operation because of its capability of reducing the tool path length and machining time. However, this causes the occurrence of an uncut region at the corner and at the centre of a contour parallel. This uncut region can be removed through an additional tool path known as the clear tool path. Therefore, in this paper, a new method based on an optimisation technique is introduced to generate a clear tool path that removes the entire uncut region in contour parallel machining at minimum cutting time. Ant colony algorithm (ACO) is used to optimise the clear tool path length in contour parallel machining time by minimising the movement of cutting tool in removing the entire uncut regions. A new transition rule has been established from the conventional ACO, which adapted the uncut region occurring at the corner of the contour parallel. Then, to validate the optimisation result, a cutting experiment was carried out using computer numerical control (CNC) milling machine. It can be ascertained from this study that the optimisation of the clear tool path gives optimal tool path length whilst reducing the cutting time in the roughing process. [ABSTRACT FROM AUTHOR]

In this paper, a tri-objective optimization problem related to sequencing in multi-model reconfigurable assembly systems (RAS) is addressed. RASs are also designed around product families similar to the more generic reconfigurable manufacturing systems (RMSs). A multi-model assembly line needs reconfiguration after assembling each batch of the products from the family. Further, the changeover of assembly line from one product type to another is also of varying degree of complexity and reconfiguration cost. Two novel indices for commonality and reconfiguration complexity are proposed here to incorporate this feature of RAS. Optimizing the reconfiguration time/cost is taken as the first objective while fulfilling the product due dates and the work load balance among the assembly stations as the second and third objective functions, respectively. This optimization problem is solved using a Multi-objective Self-Organizing Migrating Algorithm (MOSOMA) metaheuristic to find the non-dominated Pareto optimal solutions representing optimum sequencing of products in the family. An approach for identifying the best solution from the multiple Pareto optimal solutions using aggregate fitness is also proposed. A numerical illustration along with computational procedure is presented on a hypothetical RAS model. [ABSTRACT FROM AUTHOR]

In machining large complex parts with critical stock allowance, even small deviations in the blank parts or slight inadequacy in the fixturing may result in local shortage of material (i.e., insufficient stock allowance). This paper presents an optimal localization algorithm that aligns the measured points from a blank part with the nominal model to assure the weakest allowance area with as much material to be cut as possible. Instead of exploiting extra constraints to force the allowance at each point larger than a specific value, which is a popular strategy for allowance assurance in the previous localization algorithms, we formulate the blank localization problem as an unconstrained max-min problem. To deal with the unsmoothness exhibiting in the max-min objective function, a method based on the entropy optimization principle is adopted to convert the non-differential objective function to an unconstrained differential one, which can be efficiently solved using the conventional Quasi-Newton algorithms. The unconstrained optimization result finally gives rise to localization with the maximum allowance margin. For the blank parts that the material shortage is inevitable, the method can still efficiently achieve reasonable localization results, which confine the material shortage to a least extent. The proposed method is easy to be implemented and works well for both sparse sample points and dense-scanned points. Case studies included justify the superiority of the proposed scheme over the existing non-linear constrained optimization solutions. [ABSTRACT FROM AUTHOR]

In Mellal and Williams (2015) 'Cuckoo optimization algorithm for unit production cost in multi-pass turning operations' published in the International Journal of Advanced Manufacturing Technology, the authors state the proposed Cuckoo optimization algorithm, denoted as COA, outperforms the hybrid particle swarm optimization (HPSO) for a class of multi-pass turning problems. The HPSO was presented by Costa et al. (Int J Adv Manuf Technol 53:421-433, 2011). Notably, though HPSO achieves a lower unit production cost than COA, the authors point out HPSO violates a tool life-related constraint, thus resulting ineffective. In this paper, we demonstrate that the HPSO does not violate any constraint pertaining to the multi-pass turning problem at hand and, as a result, the outperformance of COA over HPSO is incorrect. [ABSTRACT FROM AUTHOR]