Mathematical Modeling of Weld Bead Geometry, Quality, and Productivity for Stainless Steel Claddings Deposited by FCAW

In recent years, industrial settings are seeing a rise in the use of stainless steel claddings. The anti-corrosive surfaces are made from low cost materials such as carbon steel or low alloy steels. To ensure the final quality of claddings, however, it is important to know how the welding parameters affect the process??s outcome. Beads should be defect free and deposited with the desired geometry, with efficiency, and with a minimal waste of material. The objective of this study then is to analyze how the flux-cored arc welding (FCAW) parameters influence geometry, productivity, and the surface quality of the stainless steel claddings. It examines AISI 1020 carbon steel cladded with 316L stainless steel. Geometry was analyzed in terms of bead width, penetration, reinforcement, and dilution. Productivity was analyzed according to deposition rate and process yield, and surface quality according to surface appearance and slag formation. The FCAW parameters chosen included the wire feed rate, voltage, welding speed, and contact-tip-workpiece distance. To analyze the parameters?? influences, mathematical models were developed based on response surface methodology. The results show that all parameters were significant. The degrees of importance among them varied according to the responses of interest. What also proved to be significant was the interaction between parameters. It was found that the combined effect of two parameters significantly affected a response; even when taken individually, the two might produce little effect. Finally, the development of Pareto frontiers confirmed the existence of conflicts of interest in this process, suggesting the application of multi-objective optimization techniques to the sequence of this study.     

Development of a special geometry carbide tool for the optimization of vertical turning of martensitic gray cast iron piston rings

The machining process for vertical turning martensitic gray cast iron is of great importance to the automotive industry, mainly in the manufacturing process of piston rings. The aim of this paper is to demonstrate the process of development of coated carbide tools to maximize the productivity of the process, considering the maximum life of the cutting tool and the minimum machining cost per part. Using full-factorial design of experiments, we tested two different geometries: a square tool with special geometry—formed by two edges and two ends simultaneously cutting—and a hexagonal tool. Considering that the special square geometry provided maximum life, full quadratic models for responses of interest were constructed using a central composite design for feed (f) and rotation (n). Applying the generalized reduced gradient algorithm, the proposed optimization goals were achieved with feed f?=?0.37?mm/v and rotation of 264?rpm for the use of the special square tool. Confirmation experiments prove the effectiveness of this solution.     

Artificial neural networks for machining processes surface roughness modeling

In recent years, several papers on machining processes have focused on the use of artificial neural networks for modeling surface roughness. Even in such a specific niche of engineering literature, the papers differ considerably in terms of how they define network architectures and validate results, as well as in their training algorithms, error measures, and the like. Furthermore, a perusal of the individual papers leaves a researcher without a clear, sweeping view of what the field’s cutting edge is. Hence, this work reviews a number of these papers, providing a summary and analysis of the findings. Based on recommendations made by scholars of neurocomputing and statistics, the review includes a set of comparison criteria as well as assesses how the research findings were validated. This work also identifies trends in the literature and highlights their main differences. Ultimately, this work points to underexplored issues for future research and shows ways to improve how the results are validated.     

A multivariate surface roughness modeling and optimization under conditions of uncertainty

Correlated responses can be written in terms of principal component scores, but the uncertainty in the original responses will be transferred and will influence the behavior of the regression function. This paper presents a model building strategy that consider the multivariate uncertainty as weighting matrix for the principal components. The main objective is to increase the value of R² predicted to improve model’s explanation and optimization results. A case study of AISI 52100 hardened steel turning with Wiper tools was performed in a Central Composite Design with three-factors (cutting speed, feed rate and depth of cut) for a set of five correlated metrics (R_a, R_y, R_z, R_q and R_t). Results indicate that different modeling methods conduct approximately to the same predicted responses, nevertheless the response surface to Weighted Principal Component – case b – (WPC1^b) presented the highest predictability.     

A multivariate normal boundary intersection PCA-based approach to reduce dimensionality in optimization problems for LBM process

Engineering with Computers - Laser beam machining (LBM) is a promising manufacturing process that exhibits several desirable quality characteristics. Given a large number of objective functions,...     

Toward a robust optimal point selection: a multiple-criteria decision-making process applied to multi-objective optimization using response surface methodology

During the multi-objective optimization process, numerous efficient solutions may be generated to form the Pareto frontier. Due to the complexity of formulating and solving mathematical problems, choosing the best point to be implemented becomes a non-trivial task. Thus, this paper introduces a weighting strategy named robust optimal point selection, based on ratio diversification/error, to choose the most preferred Pareto optimal point in multi-objective optimization problems using response surface methodology. Furthermore, this paper proposes to explore a theoretical gap—the prediction variance behavior related to the weighting. The ratios Shannon’s entropy/error and diversity/error and the unscaled prediction variance are experimentally modeled using mixture design and the optimal weights for the multi-objective optimization process are defined by the maximization of the proposed measures. The study could demonstrate that the weights used in the multi-objective optimization process influence the prediction variance. Furthermore, the use of diversification measures, such as entropy and diversity, associated with measures of error, such as mean absolute percent error, was determined to be useful in mapping regions of minimum variance within the Pareto optimal responses obtained in the optimization process.

     

Optimization of Radial Basis Function neural network employed for prediction of surface roughness in hard turning process using Taguchi’s orthogonal arrays

This work presents a study on the applicability of radial base function (RBF) neural networks for prediction of Roughness Average (R_a) in the turning process of SAE 52100 hardened steel, with the use of Taguchi’s orthogonal arrays as a tool to design parameters of the network. Experiments were conducted with training sets of different sizes to make possible to compare the performance of the best network obtained from each experiment. The following design factors were considered: (i) number of radial units, (ii) algorithm for selection of radial centers and (iii) algorithm for selection of the spread factor of the radial function. Artificial neural networks (ANN) models obtained proved capable to predict surface roughness in accurate, precise and affordable way. Results pointed significant factors for network design have significant influence on network performance for the task proposed. The work concludes that the design of experiments (DOE) methodology constitutes a better approach to the design of RBF networks for roughness prediction than the most common trial and error approach.     

Crack avoidance in steel piston rings through the optimization of process and gas nitriding parameters

This paper describes research into adequately estimating the main variables of a thermochemical gas nitriding process of stainless steel parts for engine components. The paper lays out an experimental strategy for the nitriding process that optimizes a set of variables that have a bearing on the occurrence of nitriding cracks. The results demonstrate that several factors and interactions are relevant in the occurrence of nitriding cracks. The proposed strategy was found to be effective at achieving continuous improvement and stricter control.     

Multi-objective optimization of pulsed gas metal arc welding process based on weighted principal component scores

Most welding processes present large sets of correlated quality characteristics. With this particularity in mind, we present a multi-objective optimization technique based on Principal Component Analysis (PCA) and response surface methodology (RSM). This two-fold technique utilizes PCA to factorize the original welding responses. The original responses—obtained through a Central Composite Design—are then replaced by the resulting principal component scores. The technique’s advantage is that it reduces the data set and still considers the correlation among the responses. Quite often, however, the first principal component alone cannot explain the amount of variance–covariance structure of the welding responses. In this paper, we remedy this shortfall by proposing an objective function established in terms of the most significative principal component scores (weighted by their respective eigenvalues). Experimental results were obtained with a multiresponse pulsed gas metal arc welding process. These results, when compared with other strategies of multiresponse combination, verify the adequacy of our proposed approach.