Application of Response Surface Methodology to Predict Ovality of AA6082 Flow Formed Tubes

Srinivasulu, M.; Komaraiah, M.; Rao, Chalamalasetti Srinivasa

doi:10.4018/ijmmme.2013010104

Cited by 7 publications

(8 citation statements)

References 13 publications

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“…It uses a rotating spindle and three mechanically guided rollers to form gear components with up to 400 mm in diameter. This process is called a flow-forming process [ 3 ] and is controlled by a CNC (Computerized Numerical Control) program, which contains the commands to operate the machine. The same CNC program is executed in all the experiments.…”

Section: Methodsmentioning

confidence: 99%

“…Furthermore, there are several works that deal with predicting the geometry of the finished workpiece based on the process input parameters such as the machine settings. For instance, in [ 3 ], linear and quadratic models are used to predict ovality based on roller feed, spindle speed, and radius of the roller. In [ 25 , 26 ], the researchers train artificial neural networks to predict various geometry variables based on process inputs and compare them with linear models, with the neural networks producing significantly better results in both cases.…”

Section: Related Workmentioning

confidence: 99%

“…Flow forming is a metal forming process for the production of rotationally symmetrical parts such as flanged components and pressure cylinders [ 1 , 2 ]. In a flow-forming process, a blank, i.e., a short and thick metal disk, is mounted on a rotating spindle and stretched out by mechanically guided rollers [ 3 ]. In the process, the workpiece becomes longer and its wall thickness thinner, while the inside diameter remains constant [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

“…In a flow-forming process, a blank, i.e., a short and thick metal disk, is mounted on a rotating spindle and stretched out by mechanically guided rollers [ 3 ]. In the process, the workpiece becomes longer and its wall thickness thinner, while the inside diameter remains constant [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Monitoring Flow-Forming Processes Using Design of Experiments and a Machine Learning Approach Based on Randomized-Supervised Time Series Forest and Recursive Feature Elimination

Anozie,

Fink,

Friedrich

et al. 2024

Sensors

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The machines of WF Maschinenbau process metal blanks into various workpieces using so-called flow-forming processes. The quality of these workpieces depends largely on the quality of the blanks and the condition of the machine. This creates an urgent need for automated monitoring of the forming processes and the condition of the machine. Since the complexity of the flow-forming processes makes physical modeling impossible, the present work deals with data-driven modeling using machine learning algorithms. The main contributions of this work lie in showcasing the feasibility of utilizing machine learning and sensor data to monitor flow-forming processes, along with developing a practical approach for this purpose. The approach includes an experimental design capable of providing the necessary data, as well as a procedure for preprocessing the data and extracting features that capture the information needed by the machine learning models to detect defects in the blank and the machine. To make efficient use of the small number of experiments available, the experimental design is generated using Design of Experiments methods. They consist of two parts. In the first part, a pre-selection of influencing variables relevant to the forming process is performed. In the second part of the design, the selected variables are investigated in more detail. The preprocessing procedure consists of feature engineering, feature extraction and feature selection. In the feature engineering step, the data set is augmented with time series variables that are meaningful in the domain. For feature extraction, an algorithm was developed based on the mechanisms of the r-STSF, a state-of-the-art algorithm for time series classification, extending them for multivariate time series and metric target variables. This feature extraction algorithm itself can be seen as an additional contribution of this work, because it is not tied to the application domain of monitoring flow-forming processes, but can be used as a feature extraction algorithm for multivariate time series classification in general. For feature selection, a Recursive Feature Elimination is employed. With the resulting features, random forests are trained to detect several quality features of the blank and defects of the machine. The trained models achieve good prediction accuracy for most of the target variables. This shows that the application of machine learning is a promising approach for the monitoring of flow-forming processes, which requires further investigation for confirmation.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Monitoring Flow-Forming Processes Using Design of Experiments and a Machine Learning Approach Based on Randomized-Supervised Time Series Forest and Recursive Feature Elimination

Anozie,

Fink,

Friedrich

et al. 2024

Sensors

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show abstract

“…Table 3 summarizes defects types and effects of process issues on their insurgence. Ovality is influenced by feed rate and roller radius, but the defect can be minimized through correct selection of these two parameters, as deployed in [42]. Decreasing feed rate produces deformation in radial direction, which causes an increasing in ovality.…”

Section: Mechanics Of Flow Formingmentioning

confidence: 99%

A Review of Flow Forming Processes and Mechanisms

Marini

Cunningham²,

Corney

2015

KEM

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Abstract. After years of purely academic interest and niche applications, today the flow forming process is increasing demand in aerospace, automotive and defense industries. This review surveys academic paper of last fifty years, in order to evaluate the current state-of-the-art for academic and practitioner. Theoretical and experimental approaches are collected and compared by evaluating their prediction models. As a result, several knowledge gaps are identified, for example stress and strain tensors evolutions are not determined for workpiece, due to high computational cost and uncertainty about the correct finite elements approach to adopt. Similarly although, the final microstructure is often evaluated for specific cases, study of its evolution during plastic deformation has not been reported. Residual stress and final material proprieties, such as corrosion behavior, have been not studied numerically or experimentally. Tool path impact and alternative geometries are not deeply explored. Particular attention is given to process experimental optimization and characterization through Design of Experiment, which is still limited to a few papers and sometimes not well developed. The results of this review will help define a research agenda for future developments.

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A Review on Identification and Elimination of Ovality in Circular Holes During Manufacturing of Hollow Cylindrical Components

Mane¹,

Dabir²

2020

IOP Conf. Ser.: Mater. Sci. Eng.

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This review paper aims toward the investigation of manufacturing process of hollow cylindrical component to identify the defects causing ovality in circular holes. The manufacturing refers to the processes required to convert raw material into the final product which may include casting, machining, welding, finishing, heat treatment processes and ends with final inspection to check for defects. Mostly the circular holes of hollow cylindrical components show ovality after manufacturing, exceeding tolerance values and such defect leads to poor fittings. After careful study of the variety of research papers related to ovality, it was found that the ovality may occur due to component characteristics or due to defects in manufacturing process of the component. The results of these research papers concluded that machining parameters, unbalanced workpiece, clamping pressure, clamping geometry and clamping technique influence the dimensional deviations and it can be prevented by improving the production with corrective techniques.

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Application of Response Surface Methodology to Predict Ovality of AA6082 Flow Formed Tubes

Cited by 7 publications

References 13 publications

Monitoring Flow-Forming Processes Using Design of Experiments and a Machine Learning Approach Based on Randomized-Supervised Time Series Forest and Recursive Feature Elimination

Monitoring Flow-Forming Processes Using Design of Experiments and a Machine Learning Approach Based on Randomized-Supervised Time Series Forest and Recursive Feature Elimination

A Review of Flow Forming Processes and Mechanisms

A Review on Identification and Elimination of Ovality in Circular Holes During Manufacturing of Hollow Cylindrical Components

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