Design Optimization of Truck Chassis using Finite Element Analysis

Abstract

The structural integrity and performance of a truck largely depend on the design and material selection of its chassis frame. This study focuses on the performance evaluation of truck chassis for heavy-duty commercial trucks using three distinct materials Structural Steel (AISI 1020), Aluminum Alloy (6061-T6), and Carbon Fiber Composite through Finite Element Analysis (FEA) conducted in ANSYS Workbench. A ladder frame chassis model was developed using CATIA and analyzed under a uniformly applied vertical load of 2500 N with fixed supports at suspension and rear axle mounting points. The primary parameters assessed included total deformation, equivalent (von Mises) stress, and strain distribution. Simulation results revealed that Structural Steel offered superior stiffness and the least deformation, making it suitable for high-strength applications, albeit at the cost of added weight. Aluminum Alloy exhibited the lowest stress and highest deformation, highlighting its effectiveness in lightweight truck design. Carbon Fiber Composite demonstrated a balanced performance, with moderate deformation and high strength, indicating its potential in high-performance automotive applications. The findings emphasize that material selection significantly influences chassis behavior and must be tailored according to performance, weight, and cost requirements. This research provides a comparative insight into material behavior, aiding engineers in selecting optimal materials for automotive chassis design. This research supports material selection and structural refinement in the design of truck chassis subjected to high-load and durability requirements typical of commercial applications.

Introduction

The truck chassis is a critical structural component that supports major systems such as the engine, suspension, and body. Its design and material selection directly impact truck performance, safety, efficiency, and compliance with environmental regulations. With increasing demands for fuel economy and sustainability, there's a growing shift from traditional mild steel to lightweight, high-performance materials like aluminum alloys and carbon fiber composites.

2. Material Trends in Chassis Design

• Steel (AISI 1020): Traditional choice; strong, durable, affordable, but heavy.

• Aluminum Alloy (6061-T6): Lighter, corrosion-resistant, good strength-to-weight ratio.

• Carbon Fiber Composite: Very strong and lightweight, but expensive and complex to manufacture.

Switching to lightweight materials helps reduce fuel consumption, lower emissions, and improve handling and performance without compromising safety.

3. Design & Simulation Tools

Modern engineering practices use tools like:

• CATIA for 3D chassis modeling

• Finite Element Analysis (FEA) using ANSYS Workbench to simulate stress, strain, and deformation under real-world loading.
  These tools allow for virtual prototyping, saving time and cost during development.

4. Research Focus

The study aims to compare the performance of three materials (Steel, Aluminum, Carbon Fiber) under identical loading (2500 N) and boundary conditions. Metrics evaluated:

• Total deformation

• Von Mises stress (equivalent stress)

• Strain distribution

Goal: Determine the most suitable material for truck chassis applications by balancing weight, strength, durability, and cost-effectiveness.

5. Review of Literature: Integrated Truck Dynamics

Modern trucks integrate various dynamic control systems for better safety, handling, and comfort:

• Active Front Steering (AFS): Adjusts front wheel angles independently.

• Direct Yaw Control (DYC): Controls vehicle rotation using braking or torque vectoring.

• Active Suspension: Adapts suspension in real-time for road and load conditions.

• Torque Vectoring: Distributes power across wheels for better cornering and traction.

When combined, these systems enhance both stability and agility, especially in high-speed maneuvers or adverse conditions.

6. Control Technologies and Trends

• Model Predictive Control (MPC): Predicts and optimizes future vehicle states for real-time control.

• Vehicle Stability Control (VSC): Continuously adjusts torque and braking to prevent skidding or rollover.

• Driver Assistance Systems (DAS): ACC, LKA, AEB contribute to a safer driving experience.

• Electric Trucks (EVs): Allow more flexible integration of control systems and regenerative braking.

AI and ML techniques are also used to predict road conditions and optimize vehicle response by analyzing tire-road interaction and sensor data.

7. Importance of Chassis in Modern Trucks

The chassis is no longer passive—it's central to:

• Crash energy absorption

• Ride quality

• Handling

• Integration of control systems

It must accommodate sensors, actuators, and dynamic subsystems while staying lightweight and structurally sound.

8. Research Methodology

• Design: Ladder-type chassis designed in CATIA.

• Materials: Same geometry used for all three materials.

• Simulation: ANSYS Workbench used to simulate load responses.

• Goal: Identify the material that offers the best mechanical performance while maintaining manufacturing and economic feasibility.

Conclusion

This study investigated the structural performance of a truck chassis frame using three different materials Structural Steel (AISI 1020), Aluminum Alloy (6061-T6), and Carbon Fiber Composite by employing Finite Element Analysis (FEA) through ANSYS Workbench. The primary objective was to analyze and compare the chassis behavior under identical loading and boundary conditions, evaluating each material based on total deformation, equivalent stress, and strain distribution. The simulation results clearly demonstrated that Structural Steel exhibited the least amount of deformation and strain, affirming its superior stiffness and ability to maintain structural integrity under load. Its high strength and rigidity make it a reliable choice for robust and durable applications. However, its significant weight contribution remains a disadvantage, particularly when fuel efficiency and truck dynamics are critical. Aluminum Alloy (6061-T6), in contrast, showed the highest deformation, reflecting its lower stiffness but significantly lighter mass. It recorded the lowest equivalent stress among the three materials, indicating a good capacity to distribute mechanical loads within its elastic limit. This makes it a highly favorable option where weight reduction and efficiency are prioritized, especially in the manufacturing of passenger trucks aimed at reducing fuel consumption and emissions. Carbon Fiber Composite offered a balanced profile, with moderate deformation and the highest stress values, yet well within its safe operating range due to its superior strength-to-weight ratio. While it outperforms both steel and aluminum in specific high-performance criteria, the cost and complexity involved in its production make it more suitable for specialized applications such as motorsports or premium truck segments. Overall, the study concludes that material selection for chassis design should be based on a careful evaluation of performance requirements, weight considerations, cost implications, and intended application. Each material demonstrated distinct advantages under controlled simulation conditions, highlighting the trade-offs between strength, stiffness, deformation, and manufacturability. Future research can build upon this work by exploring hybrid or composite material combinations, real-world crash testing, and dynamic simulations to further enhance the structural optimization of automotive chassis systems. The findings of this study can guide manufacturers in optimizing commercial truck chassis by selecting materials that balance strength, weight, and cost under realistic load-bearing conditions.

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Copyright

Copyright © 2025 Abhishek ., Prof. Girish Kumar Khare. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.