Using Sequence-Approximation Optimization and Radial-Basis-Function Network for Brake-Pedal Multi-Target Warping and Cooling

Devi²,

et al. 2023

Biomimetics

The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area.

Section: Neural Network and Multi-objective Optimizationmentioning

confidence: 99%

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

Devi²,

et al. 2023

Biomimetics

“…This paper presents a modification of the WSM presented by [ 28 ] by considering the restriction of the variables in order to obtain an optimal point and reduce the shrinkage and warpage in the plastic injection process. Regarding the plastic injection molding process, different works use Pareto optimization to optimize the parameters of the injection molding process to optimize the quality of the parts in order to reduce manufacturing costs [ 29 , 30 , 31 ].…”

Section: Theoretical Considerations On Injection Molding Process Opti...mentioning

confidence: 99%

Optimization of the Reduction of Shrinkage and Warpage for Plastic Parts in the Injection Molding Process by Extended Adaptive Weighted Summation Method

et al. 2022

The consumer market has changed drastically in recent times. Consumers are becoming more demanding, and many companies are competing to be market leaders. Therefore, companies must reduce rejects and minimize their operating costs. One problem that arises in producing plastic parts is controlling deformation, mainly in the form of shrinkage due to the material and warpage associated with the geometry of the parts. This work presents a novel extended adaptive weighted sum method (EAAWSM: Extended Adaptive Weighted Summation Method) integrated into a Pareto front model. The performance of this model is evaluated against three other conventional optimization methods—Taguchi–Gray (TG), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Model Optimization by Genetic Algorithm (MOGA)—and compared with EAAWSM. Two response variables and three input factors are considered to be analyzed: material melting temperature, mold temperature, and filling time. Subsequently, the performance is compared and its behavior observed using Moldflow® simulation. The results show that with the EAAWSM method, the shrinkage is 15.75% and the warpage is 3.847 mm, regarding the manufacturing process parameters of a plastic part. This proposed deterministic model is easy to use to optimize two or more output variables, and its results are straightforward and reliable.

“…In 2022, Han-Jui Chang et al [ 16 ] used a multiobjective optimization method to optimize automotive pedal injection-molding defects. The optimized injection-molding parameters are cycle time and warpage, and the Pareto boundary of multiobjective optimization results is used to identify the nonlinear relationship between cycle time and warpage to find the Pareto optimal solution of warpage values under different cooling times.…”

Section: Literature Reviewsmentioning

confidence: 99%

A Node Detection Method Based on Johnson–Cook and Thin-Film IMD Characteristic Model Armor Damage Detection Repair and Subsequent Optimization

et al. 2022

Self Cite

In this paper, a node detection method is proposed for the detection of battle damage to armor. This experiment uses the special nature of the film to virtualize the surface of the armor IMD film coverage. The die index is a large area and is easy to damage, but with the use of a unique IMD film stamping die, the possibility of damage decreases, which provides a damage prediction function for the armor. In addition, for the damaged armor, the same method can be used to detect because the damaged part more easily causes the surface film to rupture after being impacted, so it is possible to optimize the design of the armor and the molding through the die index. The die index can also detect the degree of damage to the damaged part of the damaged armor. Therefore, the IMD die index is introduced to quantify the data, and the degree of damage is judged by the IMD die index. The novelty of this work is that each node can efficiently detect the vulnerable damage position of the armor using the die index and then pass through the COMSOL. The Johnson–Cook stress model simulates the battle loss, obtains the stress deformation that occurs after the battle loss, and verifies the experiment by comparing the results obtained. Finally, the repair method is used to repair all the predicted battle damage parts based on additive manufacturing to ensure that they can be used again after repair.