Analysis of Dynamic Loads on Goods Transported by Road Vehicles

Abstract

In the packaging industry, the last steps are represented by palletizing operations and stretch-wrapping and banding processes. Pallet stretch wrapping and banding, crucial to protect and stabilize the goods, are obtained by applying a certain number of cellophane layers around the palletized load. The number of cellophane layers is chosen by the operator according to his common sense. To reduce waste, the number of layers to be applied may be optimized by running a FEM analysis simulation. The input of the FEM analysis should be the forces (and hence the accelerations) acting on the pallet in a local-coordinate frame, during a generic journey of the truck from a starting point to a delivery location. The aim of the project is to present a numerical methodology that, given the path in Cartesian coordinates, constructs the time history of the motion law with whom the truck travels. The motion law is then arranged together with the path geometric characteristics (e.g. horizontal and vertical grade, curvature radius) to achieve the temporal trend of the accelerations along the tangential, normal, and vertical directions of the truck. Once the whole time history is obtained, the most demanding cases are identified and the corresponding acceleration trends are used as inputs for the FEM analysis.

Project Goals

The project aims at:

• analysing the state of the art regarding operational speed along road sections
• implementing an algorithm capable of:
  • processing the raw data of the geographical path
  • dividing the path in road sections (e.g., tangents, curves, roundabouts, and intersections with stops)
  • according to the attribute of each road section, computing the operational speed
  • constructing the whole motion law of the vehicle (e.g. finding the evolving in time of the velocity, tangential and centripetal accelerations)
  • identifying the worst cases along the vehicle journey
• validating the model through a comparison with experimental data, recorded on a real truck vehicle

In collaboration with: Robopac

---

Do you want to know more about this project? Send us a message!