Computational modelling
Alper Kiraz; Asiye Yücedag Gürel
Abstract
Continuously adding value to a company's products and services is inevitable in adapting to this evolving and challenging global market. That is why lean philosophy is becoming increasingly important and popular among companies, and they are relying more and more on it. It not only assists in increasing ...
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Continuously adding value to a company's products and services is inevitable in adapting to this evolving and challenging global market. That is why lean philosophy is becoming increasingly important and popular among companies, and they are relying more and more on it. It not only assists in increasing profitability and quality by eliminating all processes that provide no value to the customer but also enables increased flexibility in production and productivity. In this study, the criteria affecting the Lean Maturity Level (LML) were determined, and a lean maturity measurement model, which helps companies define and understand the level of lean maturity and lean effectiveness, was developed. A recently completed case study included data from an online survey with 116 questions, which were conducted on 187 middle to senior-level professionals in Türkiye from different industries. In this model, 9 main and 14 sub-lean criteria were generated to determine LML , and each criterion was weighted based on the assessments of experts. In this paper, the interval-valued spherical fuzzy AHP method is applied for the very first time to the weighting of the criteria of a lean maturity assessment model. After collecting data through an online survey study, Confirmatory Factor Analysis (CFA) in the IBM SPSS AMOS V26 program was applied to test the model fit, validity, and reliability. To determine the LMLs, the leveling scale (understanding, implementation, improvement, and sustainability) was used from the model for LMLs in manufacturing cells. As a result of the analysis of the survey results obtained from the participating companies, the overall LML was calculated as 2.55 out of 4. This result corresponds to the level 3 - improvement range on the leveling scale. The lean maturity success rate of surveyed companies was set at 64%.
Computational Intelligence
Alper Kiraz; Enes Furkan Erkan; Onur Canpolat; Onur Kökümer
Abstract
In welded constructions, there should be no defects in the welding seams, or defects should have in an acceptable range for obtaining more reliable welding operations. An undercut is one of the most important welding defects occurring on the workpieces produced by butt welding. Determining the correct ...
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In welded constructions, there should be no defects in the welding seams, or defects should have in an acceptable range for obtaining more reliable welding operations. An undercut is one of the most important welding defects occurring on the workpieces produced by butt welding. Determining the correct value of the stress concentration factor (SCF) allows deciding whether it accepts welding defects, in which case. Many characteristics and ranges influence SCF, making it challenging to calculate a more precise SCF. In this study, six different artificial neural networks (ANN) models are developed for predicting SCF. These models differ in terms of the training dataset used (70%-90%) and the number of neurons (5-10-20) in the hidden layer. Developed ANN models consist of three input variables the ratio of Undercut depth (h) and Undercut deep Radius (r), Reinforcement angle (Q1), deep angle of welding seam (Q2), and an output variable as SCF. The prediction performance of 6 developed ANN models in different specifications is compared. The model with a 90% training set and five neurons in the hidden layer performed the best with an accuracy of 0.9834. According to the ANN model with these features, MAE, MAPE, and RMSE values are calculated as 0.0094, 2.50%, and 0.0129, respectively.
