Chan-Joo Lee

The increasing use of vinyl-coated metal (VCM) sheets in home appliances requires robust forming processes to prevent defects such as delamination and wrinkling, especially under elevated temperatures and humidity. This study presents a deep neural network (DNN)-based multi-objective optimization framework to determine optimal stamping parameters for VCM sheets. A delamination limit diagram (DLD) is experimentally established by combining limit dome height tests with immersion tests, defining the critical strain boundary under environmentally conditions. A finite element (FE) based dataset of four process variables was then used to train a DNN surrogate model with high predictive accuracy. Using the trained DNN model, Pareto-based optimization identifies nondominated solutions balancing delamination and wrinkling. The optimal condition was validated by FE simulation, confirming simultaneous suppression of both defects within the DLD. The proposed DNN–Pareto framework provides and efficient and reliable tool for defect prediction and optimization in VCM stamping, ensuring high surface quality and environmental durability.

Carbon-Fiber-Reinforced Plastic (CFRP) is a typical lightweight material used in the aerospace industry. However, the automotive industry has focused on the application of composite materials in vehicle components for weight reduction. In particular, hybrid parts consisting of CFRP reinforcement and a steel outer have been investigated in many studies as a solution to satisfy weight reduction and high strength. In this paper, a steel/CFRP hybrid part was evaluated by impact analysis using several material models, such as the Johnson–Cook model, progressive damage analysis (PDA), and cohesive zone model (CZM). First, the mechanical properties of the steel were determined under different strain rates to assess collision effects. Subsequently, the material properties of the CFRP were evaluated to predict the failure of composite material in the tensile and compressive directions. In addition, the cohesive properties of adhesive film were evaluated under normal and shear modes. Finally, impact analysis using the obtained material properties was conducted to predict the behavior and strength of the steel/CFRP hybrid part under collisions, and the results were compared with the experimental results for verification.

This paper proposes a multi-objective optimization method that integrates deep neural networks (DNN) with gray relational analysis (GRA) to optimize lay-up configurations for carbon fiber-reinforced plastic (CFRP) automotive components. Specifically, a lab-scale CFRP B-pillar structure was investigated to simultaneously maximize structural strength and failure safety. A DNN surrogate model was trained using finite element simulations of 2000 random stacking sequences to achieve high predictive accuracy. The trained model was then used to evaluate all possible lay-up combinations to derive Pareto optimal solutions. Gray relational analysis was subsequently employed to select the final optimal configurations based on designer preferences. The selected lay-up designs demonstrated improvements in both strength and failure safety. To validate the proposed framework, laboratory-scale CFRP B-pillar was fabricated using a prepreg compression molding process and subjected to bending tests. The experimental results confirmed an error below 5% and failure trends consistent with the simulation results, thereby validating the reliability of the proposed method. The proposed DNN-GRA approach enables efficient multi-objective optimization with reduced computational effort and flexibility in reflecting different engineering priorities.

This paper proposes a multi-objective optimization method that integrates deep neural networks (DNN) with grey relational analysis (GRA) to optimize lay-up configurations for carbon fiber-reinforced plastic (CFRP) automotive components. Specifically, a lab-scale CFRP B-pillar structure was investigated to simultaneously maximize structural strength and failure safety. A DNN surrogate model was trained using finite element simulations of 2,000 random stacking sequences to achieve high predictive accuracy. The trained model was then used to evaluate all possible lay-up combinations to derive Pareto optimal solutions. Grey relational analysis was subsequently employed to select the final optimal configurations based on designer preferences. The selected lay-up designs demonstrated improvements in both strength and failure safety. To validate the proposed framework, laboratory-scale CFRP B-pillar was fabricated using a prepreg compression molding process and subjected to bending tests. The experimental results confirmed an error below 5% and failure trends consistent with the simulation results, thereby validating the reliability of the proposed method. The proposed DNN-GRA approach enables efficient multi-objective optimization with reduced computational effort and flexibility in reflecting different engineering priorities.

Carbon fiber-reinforced plastic (CFRP) is a lightweight material. The automotive industry has focused on producing a steel/CFRP hybrid part to reduce overall weight. After manufacturing, delamination can occur at the interface between the CFRP and steel owing to the hybrid part constituting dissimilar materials. However, most studies have focused only on designing the manufacturing processes for the hybrid part or evaluating the adhesive used at the interface. Therefore, it is necessary to predict the behavior of the interface after demolding the hybrid part. This study aimed to predict the interface behavior of a steel/CFRP hybrid part by considering its forming and cohesive properties. First, double cantilever beam (DCB) and end-notched flexure (ENF) tests were performed to obtain cohesive parameters, such as energy release rate of modes I and II (G_I, G_II). The experimentally obtained properties were applied to the bonding area of the hybrid part. Subsequently, a forming simulation was performed to obtain the stress of the steel blank in the hybrid part. The stress distribution after forming was utilized as the initial condition for spring-back simulation. Finally, the interface behavior of the hybrid part was predicted by a spring-back simulation. The simulation was conducted using the residual stress of steel outer and the cohesive properties on the interface, without the application of any external forces. The cases of spring-back simulation were divided as delamination occurrence and attached state. The simulation results for prediction of delamination occurrence and bonding showed good agreement in both cases with experimental ones. The proposed method would contribute to expanding the manufacturing of the hybrid part by stamping and reducing the manufacturing cost by prediction of delamination occurrence.

Carbon fiber reinforced plastic (CFRP) is one of the representative lightweight material. The automotive industry has focused on producing the steel/CFRP hybrid part to reduce the weight. After manufacturing, delamination can occur at the interface between the CFRP and steel owing to the hybrid part constituting dissimilar materials. However, most studies have focused only on designing the manufacturing processes for hybrid part or evaluating the adhesive used at the interface. Therefore, it is necessary to predict the behavior of the interface after demolding the hybrid part. This study aimed to predict the interface behavior of a steel/CFRP hybrid part by considering its forming and cohesive properties. First, double cantilever beam (DCB) and end-notched flexure (ENF) tests were performed to obtain cohesive parameters, such as energy release rate of modes Ⅰ and Ⅱ (GⅠ, GⅡ). The experimentally obtained properties were applied to the bonding area of the hybrid part. Subsequently, a forming simulation was performed to obtain the stress of the steel blank in the hybrid part. The stress distribution after forming was utilized as the initial condition for spring-back simulation. Finally, the interface behavior of the hybrid part was predicted by a spring-back simulation, and the results were compared with the experimental results for verification.

This study investigates the secondary bonding of aircraft skin/stiffener assemblies using press conduction welding with carbon fiber/polyetherketoneketone thermoplastic composites and polyetherimide adhesive. Recognizing the challenges posed by conventional welding methods in maintaining material integrity and uniformity, this research explores an alternative methodology that mitigates these issues while ensuring high-strength bonds. The press conduction welding parameters were selected based on single-lap shear tests and applied in the bonding of skin and omega stiffener components. The temperature range was determined using differential scanning calorimetry. The pressure was held at 1 MPa for 180 s. The welding temperature that produced a high-bonding strength was identified experimentally; these key variables were then used in the welding process of the skin and omega stiffener. By analyzing how the fibers tear and the effectiveness of interdiffusion between the plies, we were able to gain insights into the bonding strength and fractured surface. The findings suggest that press conduction welding provides a viable route for secondary bonding in thermoplastic composite structures, highlighting its advantages in terms of processing efficiency and integrity. This study contributes to the understanding of the mechanical behaviors of bonded joints and underscores the significance of temperature control in the welding process.

The spring-in phenomenon of the composite parts can affect the assembly process. This study aims to predict the spring-in phenomenon of a carbon fiber reinforced plastic (CFRP) part. Here, we predict the spring-in of the CFRP part using a coupled analysis of the forming and cooling processes during the stamping process. First, a simulation of the entire forming process, such as the transfer of the composite laminate, gravity analysis, and forming was performed to obtain the temperature distribution of the CFRP part. Subsequently, a finite-element (FE) simulation of the cooling process was conducted to predict the spring-in phenomenon of the shaped CFRP part using the temperature data obtained in the forming simulation. Finally, a CFRP part was manufactured and compared with the results of the FE simulation.

The spring-in phenomenon of the composite parts can affect the assembly process. This study aims to predict the spring-in phenomenon of carbon fiber reinforced plastic (CFRP) part. Here, we predict the spring-in of the CFRP part using a coupled analysis of the forming and cooling processes during the stamping process. First, a simulation of the entire forming process, such as the transfer of the composite laminate, gravity analysis, and forming was performed to obtain the temperature distribution of the CFRP part. Subsequently, a finite-element (FE) simulation of the cooling process was conducted to predict the spring-in phenomenon of the shaped CFRP part using the temperature data obtained in the forming simulation. Finally, a CFRP part was manufactured and compared with the results of the FE simulation.

Multi-materials of metal-polymer and metal-composite hybrid structures (MMHSs) are highly demanded in several fields including land, air and sea transportation, infrastructure construction, and healthcare. The adoption of MMHSs in transportation industries represents a pivotal opportunity to reduce the product’s weight without compromising structural performance. This enables a dramatic reduction in fuel consumption for vehicles driven by internal combustion engines as well as an increase in fuel efficiency for electric vehicles. The main challenge for manufacturing MMHSs lies in the lack of robust joining solutions. Conventional joining processes, e.g., mechanical fastening and adhesive bonding involve several issues. Several emerging technologies have been developed for MMHSs’ manufacturing. Different from recently published review articles where the focus is only on specific categories of joining processes, this review is aimed at providing a broader and systematic view of the emerging opportunities for hybrid thin-walled structure manufacturing. The present review paper discusses the main limitations of conventional joining processes and describes the joining mechanisms, the main differences, advantages, and limitations of new joining processes. Three reference clusters were identified: fast mechanical joining processes, thermomechanical interlocking processes, and thermomechanical joining processes. This new classification is aimed at providing a compass to better orient within the broad horizon of new joining processes for MMHSs with an outlook for future trends.