A Prediction Modeling Framework For Noisy Welding Quality Data

Numerous and various research projects have been conducted to utilize historical manufacturing process data in product design. These manufacturing process data often contain data inconsistencies, and it causes challenges in extracting useful information from the data. In resistance spot welding (RSW), data inconsistency is a well-known issue. In general, such inconsistent data are treated as noise data and removed from the original dataset before conducting analyses or constructing prediction models. This may not be desirable for every design and manufacturing applications since every data can contain important information to further explain the process. In this research, we propose a prediction modeling framework, which employs bootstrap aggregating (bagging) with support vector regression (SVR) as the base learning algorithm to improve the prediction accuracy on such noisy data. Optimal hyper-parameters for SVR are selected by particle swarm optimization (PSO) with meta-modeling. Constructing bagging models require 114 more computational costs than a single model. Also, evolutionary computation algorithms, such as PSO, generally require a large number of candidate solution evaluations to achieve quality solutions. These two requirements greatly increase the overall computational cost in constructing effective bagging SVR models. Meta-modeling can be employed to reduce the computational cost when the fitness or constraints functions are associated with computationally expensive tasks or analyses. In our case, the objective function is associated with constructing bagging SVR models with candidate sets of hyper-parameters. Therefore, in regards to PSO, a large number of bagging SVR models have to be constructed and evaluated, which is computationally expensive. The meta-modeling approach, called MUGPSO, developed in this research assists PSO in evaluating these candidate solutions (i.e., sets of hyper-parameters). MUGPSO approximates the fitness function of candidate solutions. Through this method, the numbers of real fitness function evaluations (i.e., constructing bagging SVR models) are reduced, which also reduces the overall computational costs. Using the Meta2 framework, one can expect an improvement in the prediction accuracy with reduced computational time. Experiments are conducted on three artificially generated noisy datasets and a real RSW quality dataset. The results indicate that Meta2 is capable of providing promising solutions with noticeably reduced computational costs

Shopping centre performance under the management of Link REIT after privatization

Includes bibliographical references (p. 85-96).Thesis (B.Sc)--University of Hong Kong, 2010.published_or_final_versio

Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing; FAIM 2014

Paper presented at the Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing, held May 20-23, 2014 in San Antonio, Texas, and organized by the Center for Advanced Manufacturing and Lean Systems, University of Texas at San Antonio; Includes bibliographical references; A significant inconsistency problem exists in the quality of resistance spot welding, and yet it offers various advantages in production. These inconsistent welding data can be eliminated using anomaly detection or instance selection methods. However, in the weldability prediction problem, this inconsistency we refer to as proper-inconsistency, may not be eliminated since it can be used to extract additional information. In this research, we examine the effects of this inconsistency on prediction performance using two machine learning methods, k-Nearest Neighbors (kNN) regression and Generalized Regression Neural Network, in order to identify an approach towards tackling the proper-inconsistency problem in weldability prediction. We also propose a new prediction performance measure, Mean Acceptable Error (MACE), for prediction models in the presence of proper-inconsistency. The proposed method is tested with actual weldability test dat

Towards proper-inconsistency in weldability prediction using k-nearest neighbor regression and generalized regression neural network with mean acceptable error

A significant inconsistency problem exists in the quality of resistance spot welding, and yet it offers various advantages in production. These inconsistent welding data can be eliminated using anomaly detection or instance selection methods. However, in the weldability prediction problem, this inconsistency we refer to as proper-inconsistency, may not be eliminated since it can be used to extract additional information. In this research, we examine the effects of this inconsistency on prediction performance using two machine learning methods, k-Nearest Neighbors (kNN) regression and Generalized Regression Neural Network, in order to identify an approach towards tackling the proper-inconsistency problem in weldability prediction. We also propose a new prediction performance measure, Mean Acceptable Error (MACE), for prediction models in the presence of proper-inconsistency. The proposed method is tested with actual weldability test dataThis proceeding is from Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing (2014): 395–401, doi:10.14809/faim.2014.0395. Posted with permission.</p