Joining of carbon fibre reinforced polymer (CFRP) composites and aluminium alloys – A review Part A Applied science and manufacturing

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

Mechanical clinching technology, with the ability to join dissimilar, coated, and hard-to-weld materials, has been widely adopted in the automotive field. The finite element method has been proved to be an efficient and effective approach for investigating the clinching process. This paper comprehensively overviewed the recent advances in the application of finite element methods on the clinching process. The modeling methodologies of clinching process, such as material modeling, meshing operation, and contact definition, were summarized and discussed. The advances regarding finite element method used in various types of clinching processes were also reviewed respectively. Subsequently, the finite element methods used to conduct parameter study and strength prediction in the clinching process were introduced and reviewed. Finally, some outlooks about FEM used in the clinching process were proposed and discussed.

Finite element methods used in clinching process

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

A study on the electro-hydraulic clinching of aluminum and carbon fiber reinforced plastic sheets

In recent years, hydroforming process has been applied in different industries such as aerospace, automotive industries due to its advantages, such as capability of forming complicated parts and a better drawing ratio in comparison with a conventional deep drawing process. Hydro-Mechanical Deep Drawing process (HMDD) is one type of hydroforming processes. In this paper, forming of square cups in HMDD process has been investigated numerically. For validation, FEM results have been compared with experimental results. In this study, the effects of two parameters including punch corner radius and the clearance between punch and die on thickness distribution have been investigated. For this end, four different cases have been considered. The results show, that the fabrication of a cup with uniform thickness distribution by using an optimum pressure profile and with a suitable clearance between die-punch is achievable.

Hossein Ghorbani Menghari

Papers

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

A study on the electro-hydraulic clinching of aluminum and carbon fiber reinforced plastic sheets

Investigation of Punch Nose Radius and Punch-Die Clearance on Thinning and Puckering in Hydro-Mechanical Deep Drawing Process