Carbon footprint prediction method for linkage mechanism design

Area of Science:

• Environmental Science
• Mechanical Engineering
• Sustainable Design

Background:

• Greenhouse gas emissions, particularly carbon dioxide, are accelerating global warming, posing significant environmental and societal risks.
• Product carbon emissions are largely determined during the design phase, but data uncertainty complicates direct carbon footprint calculation.
• Accurate carbon footprint assessment at the design stage is crucial for developing sustainable products and mitigating climate change.

Purpose of the Study:

• To propose a novel Carbon Footprint Prediction model for the Linkage mechanism Scheme Design Stage (CFPL-SDS).
• To enable designers to quantify the carbon performance of linkage mechanisms early in the design process.
• To provide a foundation for low-carbon optimization of linkage mechanisms.

Main Methods:

• Development of the CFPL-SDS model to quantify the carbon performance of linkage mechanisms.
• Design of a four-finger training mechanism based on the structural characteristics of a closed-loop cascade rehabilitation robot.
• Application and verification of the CFPL-SDS model using the designed four-finger training mechanism.

Main Results:

• The CFPL-SDS model successfully quantifies the carbon footprint of linkage mechanisms during the design stage.
• Feasibility of the CFPL-SDS model was demonstrated through its application to a specific four-finger training mechanism.
• The study validates the model's capability to handle fuzzy and uncertain data inherent in the design phase.

Conclusions:

• The CFPL-SDS model is an effective tool for predicting carbon footprints in the early design stages of linkage mechanisms.
• This predictive capability supports informed decision-making for designers aiming to reduce product carbon emissions.
• The research establishes a crucial mathematical foundation for addressing low-carbon optimization challenges in linkage mechanism design.