•A multi-objective robust parameter optimization for additive manufacturing process.•Modified k-means simultaneously optimize the mean and variance of multiple responses.•Modified k-means-based ...optimization with higher performances than conventional method.
Metal additive manufacturing (AM) technology, especially laser powder bed fusion (LPBF), has received abundant interest from industries and the research community. Process optimization methods have thus multiplied to improve the overall quality of the final parts. However, little attention has been given to the quality repeatability issue. This paper proposes a novel multi-objective robust parameter optimization framework to explore optimal process parameters with respect to relative density and dimensional accuracy of LPBF-fabricated parts. Specifically, a modified k-means clustering, named the Extended and Weighted K-means (EWK-means), was constructed to simultaneously optimize the mean and the variance of the multiple responses. Experiments were conducted to verify the effectiveness of the proposed optimization framework. In addition, the effects of the process parameters, environment-related parameters, and physical properties on the hardness of the parts were analyzed using several machine learning models. The results showed that the proposed method achieved a set of optimal process parameters with better quality and satisfactory variability in the printed parts compared with other robust parameter optimization methods.
This paper proposes a robust and computationally efficient control method for damping ultra-low frequency oscillations (ULFOs) in hydropower-dominated systems. Unlike the existing robust optimization ...based control formulation that can only deal with a limited number of operating conditions, the proposed method reformulates the control problem into a bi-level robust parameter optimization model. This allows us to consider a wide range of system operating conditions. To speed up the bi-level optimization process, the deep deterministic policy gradient (DDPG) based deep reinforcement learning algorithm is developed to train an intelligent agent. This agent can provide very fast lower-level decision variables for the upper-level model, significantly enhancing its computational efficiency. Simulation results demonstrate that the proposed method can achieve much better damping control performance than other alternatives with slightly degraded dynamic response performance of the governor under various types of operating conditions.
The objective of this work is to determine an optimal setup for the 12L14 free-machining steel-turning process that will be able to neutralize the influence of tool wear in the workpiece's mean ...roughness R.sub.a. Aiming this, equations for the mean and variance of the roughness were modelled using the response surface methodology. A crossed array with three input variables of the turning process (cutting speed, feed and depth of cut) and a noise variable (use of new and wear tools) is applied to the methodology. Subsequently, these same responses were optimized using the mean square error, which allows the response mean value to approach a predetermined target value by cancelling variations thereof through a weighted objective. Confirmation experiments were conducted to prove the suitability of the method and excellent results were obtained. Keywords: robust parameter optimization, mean square error, 12L14 free-machining steel turning, response surface methodology
Selecting the optimum process parameter level setting for multi-quality processes is cumbersome. Previous methods were plagued by complex computational search, unrealistic assumptions, ignoring the ...interrelationship between responses and failure to select optimum process parameter level settings. The methods of variable return to scale (VRS) back-propagation neural network (BPNN) previously adopted were limited by the use of weak models, poor discriminatory tendency and an inability to select the optimum parameter level setting. This study applied a modified VRS-adequate BPNN topology model in the robust parameter procedure to solve this problem. Here, standard VRS models are allowed to self-assess, leading to partitioning. The upper bound of the free variable of the VRS model is restricted and the VRS penalization coefficient is adopted to determine the optimum process parameter level setting. The effectiveness of the proposed model measured by the total anticipated improvement yielded the highest total improvement over the existing methods.
Robust design optimization is used to examine the effect of variations in the design variables, for example production tolerances, on the response variables. This article presents a new approach for ...uncertainty propagation, supplemented by a comparative study of various methods. These are design of experiments in combination with quantile and kernel density estimation to approximate the cumulative density function of the response. The accuracy and efficiency of the novel methods in comparison to the standard method of determining, which is the empirical distribution function is investigated by means of mathematical analyses and a numerical study proving the suitability of the proposed proceeding. The article also discusses different robustness measures and their suitability in engineering. A new one based on quantiles is introduced.
A process is proposed and outlined in which optimal structural design, design post-processing, manufacturing process planning, and subsequent manufacturing may be done algorithmically in a digital ...design and manufacturing platform. Customized algorithms are developed to define the structural design problem, mesh the design domain and solve the finite element equilibrium equations, perform robust structural topology optimization, optimal build orientation determination for additive manufacturing, support structure location requirements, adaptive slicing of the finite element mesh, and control of a custom made Digital Light Processing (DLP), stereolithography-based 3D Printer.
Random Early Detection (RED) is the most widely used Adaptive Queue Management (AQM) mechanism in the internet. Although RED shows better performance than its predecessor, DropTail, its performance ...is highly sensitive to parameter settings. Under non-optimum parameter settings, the performance degrades and quickly approaches that of DropTail gateways. As the network conditions change dynamically and since the optimum parameter settings depend on these, the RED parameters also need to be optimized and updated dynamically. Since the interaction between RED and TCP is not well understood as analytical solutions cannot be obtained, stochastic approximation based parameter optimization is proposed as an alternative. However, simulation based approaches may yield a sub-optimal solution since for these to work, the network needs to be accurately simulated which is, however, infeasible with today's internet. In this paper, we present an optimization technique for optimizing RED parameters that makes use of direct measurements in the network. We develop a robust two-timescale simultaneous perturbation stochastic approximation algorithm with deterministic perturbation sequences for optimization of RED parameters. A proof of convergence of this algorithm is provided. Network simulations, using direct implementation of the algorithm over RED routers, are carried out to validate the proposed approach. The algorithm presented here is found to show better performance as compared to a recently proposed algorithm that adaptively tunes a RED parameter. PUBLICATION ABSTRACT
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