AIDEAS
Repair Reuse Recycle Suite

As products reach the end of their lifecycle, they often become more homogeneous compared to their initial manufacturing state. This poses challenges in terms of automated disassembly, sorting, and separation processes. Manual inspection is typically required to assess their condition and determine the necessary treatment based on the extent of damage or wear. AI presents numerous opportunities to optimize the infrastructure involved in material circulation within the economy. A key focus is leveraging AI algorithms that utilize cameras and other sensors to recognize and identify objects. These algorithms aim to model the disassembly and recycling processes, predicting the outcome of each action by analyzing probabilistic relationships among various disassembly, sorting, separation, and recycling aspects. The goal is to develop a data-driven model that can identify the most suitable end-of-life solution for old machines, taking into account factors such as energy consumption, safety, maintainability, productivity, and technological advancements. This will involve advancements in AI technologies, specifically in the fields of visual recognition and machine learning.

At the end of their lifecycle, products tend to exhibit greater uniformity compared to their initial manufacturing stage, making the processes of disassembly, sorting, and separation more challenging to automate. Assessing their condition typically requires manual inspection and subsequent treatment based on the extent of damage or wear they have endured. AI presents numerous opportunities to optimize the material circulation infrastructure in the economy, particularly by leveraging algorithms capable of object recognition and identification using cameras and sensors.

The next phase involves conducting an impact analysis on various end-of-life scenarios for different machinery. This analysis takes into account cost (Life Cycle Cost), environmental (Life Cycle Assessment), and social (Social Life Cycle Assessment) perspectives. AI algorithms are employed to model the disassembly and recycling processes, predicting the outcomes of each action by examining the probabilistic relationships among disassembly, sorting, separation, and recycling aspects. Based on the results obtained from Life Cycle Cost, Life Cycle Assessment, and Social Life Cycle Assessment, the algorithms formulate a multi-objective optimization strategy that balances economic, social, and environmental benefits.

The adoption of smart retrofitting solutions for old machines offers various benefits, including improving working conditions, enhancing the quality process, enabling better communication and collaboration, increasing productivity, efficiency, flexibility, and agility, and reducing costs.

Overall, smart retrofitting enables organizations to maximize the value and performance of existing machinery while also contributing to sustainability goals by reducing waste and improving energy efficiency. The combination of hardware implementation, data acquisition and control platforms, AI algorithms, user-friendly interfaces, and innovative business models forms the foundation for successful smart retrofitting initiatives.

In the AIDEAS Prescriptive Maintenance solutions, the identification of variables related to the remaining component life is crucial for predicting maintenance requirements and extending the useful life of the machine. The solution leverages AI regression algorithms to analyze and process these variables and generate features that are characteristic of the remaining life of the components.

To predict the remaining life of the components, the AI regression algorithms use these features as inputs and estimate the remaining life based on historical data, maintenance reports, physics-based models, or a combination of these sources. It’s important to ensure the availability of good regressands, which are the actual remaining life values of the components, for training the algorithms. These regressands can be obtained through experiments, operational data, or other reliable sources.

The AI regression algorithms learn from the available data and build predictive models that can estimate the remaining life of components in real-time. By analyzing the predicted remaining life and comparing it with the desired extension of the machine’s useful life, maintenance requirements can be identified. This proactive approach allows for timely maintenance interventions, optimizing machine performance and minimizing downtime.

The development of the Machine Passport for storing and sharing manufacturing data throughout the product life phases is a crucial aspect of ensuring efficient data integration, sharing, and exchange in the industry. The MP aims to establish data exchange protocols, standards, and interfaces that facilitate seamless communication between various computer-aided systems and manufacturing stages.