This paper presents soft computing-based modeling and multi-objective optimization of process parameters in single-point incremental forming (SPIF) of aluminum alloy sheet in order to obtain desired deformed shape with optimal formability satisfying multiple objectives. Response surface methodology and adaptive neuro-fuzzy inference system (ANFIS)-based models were developed to predict the responses based on the experimental data collected according to central composite design of experiments considering tool diameter, feed rate and step height as inputs, and outputs, namely forming wall angle, deformed sheet thickness and surface roughness. Inverse analyses were also performed to determine the set of input parameters to achieve desired outputs. Two different algorithms, namely back-propagation and hybrid, were employed to train the ANFIS in batch mode with the help of experimental data. The performances of the developed models were tested through real experimental data and also cross-validation methods. ANFIS trained by hybrid algorithm was found to be slightly better than that trained by the back-propagation algorithm in terms of prediction accuracy. Desirability function and a non-dominated sorting genetic algorithm were utilized for performing multi-objective optimization in SPIF, and the obtained optimal results were found satisfactory compared to the experimental data. The proposed approach could provide a reliable guidance for selection of suitable parameters in SPIF to achieve desired formed parts.

Inverse analysis and multi-objective optimization of single-point incremental forming of AA5083 aluminum alloy sheet

Evaluation and optimization on the formability of an AZ31B Mg alloy during warm incremental sheet forming assisted with oil bath heating

In this study, the response surface method (RSM), back propagation neural network (BPNN), and genetic algorithm (GA) were used for modeling and multi-objective optimization of the forming parameters of AA5052 in incremental sheet forming (ISF). The optimization objectives were maximum forming angle and minimum thickness reduction whose values vary in response to changes in production process parameters, such as the tool diameter, step depth, tool feed rate, and tool spindle speed. A Box–Behnken experimental design was used to develop an RSM and BPNN model for modeling the variations in the forming angle and thickness reduction in response to variations in process parameters. Subsequently, the RSM model was used as the fitness function for multi-objective optimization of the ISF process using the GA. The results showed that RSM effectively modeled the forming angle and thickness reduction. Furthermore, the correlation coefficients of the experimental responses and BPNN predictions of the experiment results were good with the minimum value being 0.97936. The Pareto optimal solutions for maximum forming angle and minimum thickness reduction were obtained and reported. The optimized Pareto front produced by the GA can be a rational design guide for practical applications of AA5052 in the ISF process.

RSM and BPNN Modeling in Incremental Sheet Forming Process for AA5052 Sheet: Multi-Objective Optimization Using Genetic Algorithm

Investigation of nanostructured surface layer of severe shot peened AISI 1045 steel via response surface methodology

When using a unique tool with different controlled path strategies in the absence of a punch and die, the local plastic deformation of a sheet is called Single Point Incremental Forming (SPIF). The lack of available knowledge regarding SPIF parameters and their effects on components has made the industry reluctant to embrace this technology. To make SPIF a significant industrial application and to convince the industry to use this technology, it is important to study mechanical properties and effective parameters prior to and after the forming process. Moreover, in order to produce a SPIF component with sufficient quality without defects, optimal process parameters should be selected. In this context, this paper offers insight into the effects of the forming tool diameter, coolant type, tool speed, and feed rates on the hardness of AA1100 aluminium alloy sheet material. Based on the research parameters, different regression equations were generated to calculate hardness. As opposed to the experimental approach, regression equations enable researchers to estimate hardness values relatively quickly and in a practicable way. The Relative Importance (RI) of SPIF parameters for expected hardness, determined with the partitioning weight method of an Artificial Neural Network (ANN), is also presented in the study. The analysis of the test results showed that hardness noticeably increased when tool speed increased. An increase in feed rate also led to an increase in hardness. In addition, the effects of various greases and coolant oil were studied using the same feed rates; when coolant oil was used, hardness increased, and when grease was applied, hardness decreased.

Hani Mostafanezhad

Papers

Optimization of two-point incremental forming process of AA1050 through response surface methodology