Optimal Micro-Motion Unit Decomposition-Based Reliability Allocation for Computer Numerical Control Machine Using the Swarm Bat Algorithm

Abstract

In the reliability literature, reliability allocation is an important and widely studied topic. The existing reliability allocation methods, however, have limitations, including imprecise system decomposition, single-factor consideration, and poor practicability. To overcome those limitations, we propose an integrated fuzzy reliability allocation method based on micro-motion decomposition, cost function, and multi-factor analysis. The problems in the existing methods caused by equally weighted factors and influences of failures were overcome by correcting the traditional risk priority number method and evaluating the uncertainties and subjective factors during allocation using fuzzy language and triangular fuzzy number. A cost model was established based on the state of the art, subsystem intricacy, and environmental conditions, with which the issues of difficulty applying cost statistics and computational complexity in the current allocation methods were solved. The contribution of this paper is as follows. A fast approach of Pareto-optimal solution recommendation using the Pareto reliability index has been utilized to recommend a list of optimal ranking for decision-makers. Besides, the moving mean of the average precision and the moving mean standard deviation are utilized to demonstrate the trend of the evolutionary process. A multiobjective swarm bat algorithm has been developed to handle the multi-objective problems and its feasibility has been verified in a case study comparing the performance of the proposed method with that of the existing methods.

Experimental Studies of Tail Shapes for Hummingbird-Like Flapping Wing Micro Air Vehicles

“…Our future research includes the following aspects: (1) MAV applications to agriculture, such as autonomous pollination [40]; (2) high efficiency and reliability in design for the MAV's key components [41]- [47] using new AI algorithms, with the support of the parallel CIAD framework [48].…”

Recent advances of bat-inspired algorithm, its versions and applications

“…In the classification, clustering, and forecasting domain, the BA is successfully applicable in several problems such as prediction and classification [ 18 , 20 , 74 , 123 , 125 , 183 , 185 , 195 , 205 , 212 , 245 , 285 , 294 , 307 ], data clustering [ 17 , 27 , 46 , 98 , 223 , 234 , 238 , 295 , 301 , 345 , 346 ], data forecasting [ 39 , 168 , 243 , 335 ], and train the feed forward neural network [ 75 , 171 , 213 ]. These applications are shown in Fig 15 .…”

How Can Artificial Intelligence Help With Space Missions - A Case Study: Computational Intelligence-Assisted Design of Space Tether for Payload Orbital Transfer Under Uncertainties

“…To perform the simplification for the Equations (21) and (22) by fulfilling a binomial expansion, let's involve an additional assumption of L r c , and cause to the result of L r c 1, which can be employed to obtain the reformations as (23) and (24).…”

“…a) Fast approach for Pareto-optimal solution recommendation Thus, based on the three design objectives given in Equations (27), (28) and (29), in this section, the three pairs of trend indices are used to establish a fast approach for Pareto-optimal solution recommendation (FPR) [21], [23], to define a fitness function, to investigate the multi-objective problem, and to build the evolutionary pathway for the optimization process. As shown in Figure 8, the FPR using a Pareto risk index (β 2 ) can be outlined in five sub-steps, which include (1) normalization, (2) calculating the Pareto risk index β 2 , (3) ranking using β 2 , (4) calculating the three pairs of trend indices, and (5) generating the evolutionary pathway, as discussed in Section III.…”