Uploaded June 2026 | Updated September 2026, 2 weeks ago
Tarek Belgasam from Honda R&D explains how digital engineering supports welding performance prediction, physical test reduction, and faster product development. In this interview from the ESTECO Users’ Meeting North America 2025, he discusses how Honda uses modeFRONTIER, advanced DOE, machine learning, and automated workflows to improve resistance spot welding assessment. The outcome is a shorter development cycle, reduced material use, better virtual welding quality optimization, and broader access to simulation through VOLTA and the RunBox application.
*Engineering domain:*
Automotive engineering, CAE, material research, welding assessment, digital development, and resistance spot welding performance prediction.
*Type of problem:*
Honda needed to reduce physical testing for resistance spot welding assessment while improving welding quality prediction. The team also needed digital tools that support advanced DOE, machine learning, workflow automation, and simulation access for engineers who may not have deep simulation or DOE experience.
*Tools and solvers involved:*
- modeFRONTIER
- VOLTA
- RunBox application
- Advanced DOE methods
- Machine learning capabilities
- Digital engineering tools
- CAE workflows
*Key concepts covered:*
- Welding performance prediction
- Resistance spot welding
- Honda R&D
- modeFRONTIER
- VOLTA
- RunBox application
- Advanced DOE
- Machine learning
- CAE
- Material research
- Digital development
- Physical test reduction
- Workflow automation
- Complex engineering workflows
- Welding quality optimization
- Virtual testing
- Product development cycle reduction
- Material usage reduction
- Simulation democratization
- ESTECO partnership
*Chapters:*
0:00 – Introduction to Honda’s welding performance prediction use case
0:12 – What is Tarek Belgasam’s engineering background?
0:20 – His role in CAE material research at Honda R&D
0:31 – Why did Honda start reducing physical welding tests?
0:49 – How advanced DOE and machine learning supported the project
1:04 – How did Honda build a welding performance prediction workflow?
1:13 – ESTECO technical support during the project
1:21 – How Honda reduced the development cycle from six months
1:38 – How virtual testing helps reduce physical tests and material use
1:46 – How modeFRONTIER automates complex digital workflows
2:00 – Why simulation democratization matters for engineering teams
2:18 – How VOLTA and RunBox support broader simulation access
*Related ESTECO resources:*
- modeFRONTIER product page: engineering.esteco.com/modefrontier
- VOLTA product page: engineering.esteco.com/volta
- Design of Experiments technology: engineering.esteco.com/technology/design-of-experiments
- AI data-driven modeling: engineering.esteco.com/technology/ai-data-driven-modeling
- Simulation process integration and automation: engineering.esteco.com/technology/simulation-process-integration-automation
- Automotive and ground transportation: engineering.esteco.com/industries/automotive-and-ground-transportation
#WeldingPrediction #modeFRONTIER #HondaRD #DigitalEngineering
Tarek Belgasam from Honda R&D explains how digital engineering supports welding performance prediction, physical test reduction, and faster product development. In this interview from the ESTECO Users’ Meeting North America 2025, he discusses how Honda uses modeFRONTIER, advanced DOE, machine learning, and automated workflows to improve resistance spot welding assessment. The outcome is a shorter development cycle, reduced material use, better virtual welding quality optimization, and broader access to simulation through VOLTA and the RunBox application.
*Engineering domain:*
Automotive engineering, CAE, material research, welding assessment, digital development, and resistance spot welding performance prediction.
*Type of problem:*
Honda needed to reduce physical testing for resistance spot welding assessment while improving welding quality prediction. The team also needed digital tools that support advanced DOE, machine learning, workflow automation, and simulation access for engineers who may not have deep simulation or DOE experience.
*Tools and solvers involved:*
- modeFRONTIER
- VOLTA
- RunBox application
- Advanced DOE methods
- Machine learning capabilities
- Digital engineering tools
- CAE workflows
*Key concepts covered:*
- Welding performance prediction
- Resistance spot welding
- Honda R&D
- modeFRONTIER
- VOLTA
- RunBox application
- Advanced DOE
- Machine learning
- CAE
- Material research
- Digital development
- Physical test reduction
- Workflow automation
- Complex engineering workflows
- Welding quality optimization
- Virtual testing
- Product development cycle reduction
- Material usage reduction
- Simulation democratization
- ESTECO partnership
*Chapters:*
0:00 – Introduction to Honda’s welding performance prediction use case
0:12 – What is Tarek Belgasam’s engineering background?
0:20 – His role in CAE material research at Honda R&D
0:31 – Why did Honda start reducing physical welding tests?
0:49 – How advanced DOE and machine learning supported the project
1:04 – How did Honda build a welding performance prediction workflow?
1:13 – ESTECO technical support during the project
1:21 – How Honda reduced the development cycle from six months
1:38 – How virtual testing helps reduce physical tests and material use
1:46 – How modeFRONTIER automates complex digital workflows
2:00 – Why simulation democratization matters for engineering teams
2:18 – How VOLTA and RunBox support broader simulation access
*Related ESTECO resources:*
- modeFRONTIER product page: engineering.esteco.com/modefrontier
- VOLTA product page: engineering.esteco.com/volta
- Design of Experiments technology: engineering.esteco.com/technology/design-of-experiments
- AI data-driven modeling: engineering.esteco.com/technology/ai-data-driven-modeling
- Simulation process integration and automation: engineering.esteco.com/technology/simulation-process-integration-automation
- Automotive and ground transportation: engineering.esteco.com/industries/automotive-and-ground-transportation
#WeldingPrediction #modeFRONTIER #HondaRD #DigitalEngineering
