Metalsa designs and manufactures structural solutions for the mobility industry focused on those vehicles with a body-on-frame (BOF) architecture. BOF architectures are designed to perform with better off-road, towing and loading capabilities than unibody vehicles because of its flexibility-driven designed frame.
As materials and regulations evolve, frames’ complexity has also increased. To guarantee load-work optimization and avoid over-engineering, frames are designed to have over 150 different components, made by different materials (steel grades, coated materials, and aluminum components). Assembly processes represent the core of the frame manufacturing processes. Although different joining methods are used, gas metal arc welding (GMAW) is the most popular because of its high-speed and low-cost characteristics.
Because welding joints vary in material, length, thickness and shape, welding parameters must be optimized to guarantee the welding quality in every operation. To enable this, Metalsa is looking for collaboration partners with experience in developing machine learning and/or data analytics solutions for GMAW welding environments.
This is an electronic Request-for-Partners (eRFP) Challenge; the Solver will only need to submit a written proposal to be evaluated by the Seeker with the goal of establishing a collaborative partnership.
Gas metal arc welding (GMAW) processes are widely used throughout many industries. One if its main advantages is related to its speed, cost and that it is able to join thick materials. Although other types of joining are being used in the automotive industry, for the high-volume manufacturing of chassis frames, the GMAW is still the most used joining process.
GMAW assembly lines are technologically complex; they include a high number of robots, welding machines, vision systems, sensors and control elements to make sure that the joining processes comply with the expected quality.
Metalsa is seeking a numerical computational model and graphic user interface that is able to process the welding parameters in order to predict and graph the weld bead quality and other key weld attributes easy to be installed in a conventional computer with higher correlation at existing welding machines and processes.
To expedite development and application, Metalsa is seeking for partners who have experience in similar applications and whose benefit may be proven. Although all partners with industrial similar developments may be considered, those that may understand the complexity of a welding process will have a higher probability of being selected.
This is an electronic Request-for-Partners (eRFP) Challenge. The Solver will write a preliminary proposal (about 2-4 pages, including supporting non-confidential information and contact details) to be evaluated by the Seeker with a goal of establishing a collaborative partnership. Upon completion of the evaluation, the Seeker may contact selected Solvers directly to work out terms for a collaboration contract. The monetary value of the contract will vary depending on the amount of work to be delivered and the agreed upon time frame.
Awards:- Collaboration with Metalsa
Deadline:- 01-10-2020
