Teardown Analysis**: Start by dissecting several automobile models to understand their construction, materials used, manufacturing processes, and associated costs. This step helps in creating a comprehensive database of components and their properties.

**Database Creation**: Compile the findings into a structured database containing information on materials, thicknesses, manufacturing techniques, and associated costs for each component.

Customized Recommendations**: When a new automobile is torn down, input the data into the AI system. The algorithm then intelligently suggests cost-reduction strategies based on similarities with existing components in the database. It considers factors like material substitutions, thickness adjustments, and optimized manufacturing processes.

Interdisciplinary Collaborationby assembling a team of environmental experts, material scientists, and automotive engineers. They collaborate to analyze existing materials, technologies, and manufacturing processes used in automobiles and identify opportunities for sustainability improvements.

Iterations

*Data Collection and Analysis**: Conduct comprehensive research and gather data on sustainable materials, technologies, and processes relevant to automotive manufacturing including teardown comparison with previous to choose cheapest material

*NLP Integration**: Utilize Natural Language Processing (NLP) techniques to facilitate communication and collaboration among the interdisciplinary team. NLP algorithms assist in extracting relevant information from technical documents, research papers, and expert discussions.

*AI Development**: Develop AI algorithms that analyze the database of sustainable options and generate recommendations based on specific criteria such as environmental impact, performance, cost, and feasibility. These algorithms continuously learn and improve through feedback and new data inputs.

**Customized Suggestions**: When a new automotive project or component is being designed, input the requirements into the AI system. It then intelligently suggests sustainable materials, technologies, and processes that meet the specified criteria, considering factors like recyclability, energy efficiency, and carbon footprint.

**Expansion to ADAS and Autonomous Vehicles**: As the AI model matures, expand its scope to include Advanced Driver Assistance Systems (ADAS) and autonomous vehicles. This involves identifying sustainable solutions for components such as sensors, batteries, and autonomous driving systems.

*Feedback Loop**: Encourage a feedback loop where engineers and designers provide input on the feasibility and effectiveness of the AI-generated recommendations. This ensures continuous improvement and refinement of the AI model.

**Implementation and Impact**: Implement the recommended sustainable solutions in automotive manufacturing processes. Monitor and evaluate the environmental and economic impact of these changes, highlighting the benefits in terms of reduced carbon emissions, resource conservation, and cost savings for development of new car models at much lower cost.

By leveraging NLP, AI, and interdisciplinary collaboration, automotive manufacturers can accelerate the transition towards sustainable practices, making significant contributions to environmental conservation and resource efficiency in the automotive industry.

The concept is to develop an intelligent AI based model for innovative automotive designs at much lower costs in sustainable material .