AIDEAS is a European artificial intelligence innovation platform designed to optimize the entire industrial equipment lifecycle: from design and manufacturing to use, repair, reuse, and recycling. Its goal is to strengthen the sustainability, agility, and resilience of machinery manufacturers, allowing them to incorporate advanced AI solutions without having to completely redefine their current systems.
The heart of AIDEAS is structured around four Suites integrated throughout the equipment lifecycle: the Industrial Equipment Design Suite to support the design of components, mechanisms, and control elements using AI integrated with CAD/CAM/CAE tools; the Manufacturing Suite to optimize component selection, manufacturing planning, operation sequencing, and quality control; the Use Suite to improve commissioning, production, quality, and predictive maintenance in real-world conditions; and the Repair–Reuse–Recycle Suite to extend the useful life of machines, facilitate their intelligent reconditioning, and support more sustainable end-of-life decisions.
Across the board, AIDEAS incorporates Machine Passport, an intelligent platform for capturing, managing, and sharing large volumes of data from all stages of the life cycle (design, manufacturing, use, and R-R-R). This digital passport acts as the data “backbone” of the AIDEAS ecosystem, connecting the solutions of the different suites and enabling the secure exchange of information between phases and actors in the value chain.
By converting the data generated at each stage of the life cycle into actionable knowledge, AIDEAS helps manufacturers design more efficient machines, manufacture with greater agility, operate equipment under optimal conditions, and plan for repair, reuse, or recycling in an informed manner. Together, the AIDEAS Solutions Hub drives a more competitive, digital, and sustainable European manufacturing sector, ready for present and future industrial challenges.
The primary objective of this tool is to facilitate the integration of the AI-assisted optimization modules developed in AIMDO and AIMDG with standard CAD/CAM/CAE systems, thus enabling their effective implementation. This entails the creation of APIs and user interfaces (UIs) that allow seamless integration of the AI-assisted optimization modules with CAx systems such as SolidWorks, ensuring their readiness for operational environments. The APIs and UIs will not only incorporate the specific functionalities of the optimization modules but also account for the requirements of other standard CAx solutions, such as SolidWorks. Additionally, comprehensive testing and performance optimization will be conducted as part of this task.
The primary objective of this tool is to synthesize data for training the optimization modules in AIMDO. Consequently, AI solutions will be made accessible for shorter time series and lower volume productions, with a reduced need for resources to train the relevant AI algorithms. The data will be primarily generated through artificial means, utilizing digital twins and simulations. The first step involves determining suitable samples from the parameter spaces. Subsequently, the automation of the simulation processes must be established, ensuring that the necessary computer resources are available for executing the automated simulations. Additionally, a monitoring system will be developed to oversee the simulation data space, tailored to the specific optimization modules. Real-world and historical data from pilot customers will also be incorporated to enhance the training data tensor. This inclusion is particularly important to guarantee unbiased training data, enabling the optimization modules to draw from a balanced data pool.
The AI-based tool for the design phase of mechanisms and dynamic machines encompasses various modules to address the complexities of optimizing dynamic systems. These modules aim to support the design process by creating models that establish relationships between machine performance indicators and design parameters. These models can be either data-driven or based on machine physics, allowing for flexibility in the design process.
The AI-assistant within the tool enables users to modify design parameters based on objective functions and criteria. Manufacturing and operation constraints, as well as boundary conditions, are defined to ensure the parameters fall within applicable ranges. Target criteria are established and weighted according to their importance, enabling the optimization of CAD parameters.
The AIDEAS Industrial Equipment Manufacturing Suite is a set of artificial intelligence technologies designed to help machinery manufacturers optimise the industrial equipment manufacturing phase: from the selection and procurement of purchased components to the optimisation of parts manufacturing processes, operations planning and sequencing, quality control, and end-product customisation.
The primary focus of this solution is to enhance manufacturing techniques and methods to efficiently produce the necessary components and products for industrial equipment. These optimizations encompass various aspects such as sequencing and balancing production processes, conducting quality examinations pre and post-production, resource allocation, and calibration of shop floor equipment. ITI aims to develop an AI-driven scheduling optimization tool specifically designed to generate and update production schedules within manufacturing plants. This tool will consider factors like the availability of human resources, setup times, dependencies between operations, and more, to swiftly generate production schedules within a short timeframe (less than 30 minutes). Additionally, it will enable rapid rescheduling to provide near real-time responses to environmental changes, including machine breakdowns, last-minute customer orders, delays in raw material delivery, and other unforeseen events.
The primary objective of this tool is to synthesize data for training the optimization modules in AIMDO. Consequently, AI solutions will be made accessible for shorter time series and lower volume productions, with a reduced need for resources to train the relevant AI algorithms. The data will be primarily generated through artificial means, utilizing digital twins and simulations. The first step involves determining suitable samples from the parameter spaces. Subsequently, the automation of the simulation processes must be established, ensuring that the necessary computer resources are available for executing the automated simulations. Additionally, a monitoring system will be developed to oversee the simulation data space, tailored to the specific optimization modules. Real-world and historical data from pilot customers will also be incorporated to enhance the training data tensor. This inclusion is particularly important to guarantee unbiased training data, enabling the optimization modules to draw from a balanced data pool.
The primary objective of this tool is to enhance the efficiency of product packaging, storage, and delivery processes. To achieve this, AI applications will assist users in developing optimized plans and strategies for logistics, ensuring timely delivery to customers. Additionally, the optimization efforts will focus on space utilization within the manufacturer’s storage facilities, accounting for potential disruptive events. Ultimately, the AI applications aim to maximize the responsiveness and agility of the supply chain between the manufacturer and customers.
The AIDEAS Use Suite is a set of artificial intelligence-based solutions designed to optimise the use and operation phase of industrial equipment, improving service performance, product quality, maintenance (including predictive maintenance) and machinery availability based on actual operating data.
The utilization of AIMC methodology will be employed by this tool for comprehensive machine anomaly detection, with a focus on evaluating component conditions. Relevant variables associated with key component features will be identified, followed by the extraction of these features from measurement data using knowledge-based processing and AI regression techniques for component condition evaluation. The collective component features will then undergo AI clustering techniques during training and AI classification techniques during implementation to identify machine anomalies. This will enable the AIDEAS Condition Evaluator to accurately assess the condition of individual components and the overall impact on machine performance in terms of anomalies within the context of AIAD.
This tool determines the capabilities of components by analyzing their features using AI regression algorithms from AIAD. It then combines these capabilities to derive the overall capabilities of the entire machine. The machine’s capabilities are subsequently aligned, to the best possible extent, through AI-based adaptive control, with the primary process parameters within the AIDEAS Adaptive Controller. Achieving this necessitates the implementation of Machine Learning Control techniques, enabling the adaptation of machine control to the assessed conditions using Reinforcement Learning Control. The controller will be trained against a machine model constructed from measurement data to identify a control solution that adheres to the limitations imposed by the process parameters.
The purpose of this tool is to assist the end user of the machine during the initial calibration process at the customer’s factory, tailored to meet the specific requirements of each customer and factory using AI techniques. Calibration is a crucial step for every machine upon its arrival at the customer’s premises, posing challenges for both the machine provider and the end user. To address this, the proposed solution leverages AI to facilitate the initial calibration process. The primary goal is to employ a supervised learning approach that learns from experienced users and identifies the most suitable calibration parameters based on the specific processing needs. By doing so, this tool will offer calibration capabilities to all AIDEAS pilots, ensuring accurate and efficient machine calibration.
The AI-based approach employed in this tool, as described in AIMC, enables comprehensive detection of machine anomalies by evaluating component conditions. Relevant variables associated with key component features are identified and extracted from measurement data using a combination of knowledge-based processing and AI regression techniques. Through the utilization of AI clustering during training and AI classification during operation, the aggregated component features are analyzed to detect machine anomalies effectively. As a result, the AIDEAS Condition Evaluator provides precise assessments of component-level conditions, while the implications of these anomalies on the overall machine performance are evaluated within the AIAD framework.
The AI-enabled quality monitoring tool for built machinery aims to provide valuable features for monitoring and ensuring the quality of manufactured products. It supports various standardized data modalities used in industrial product inspection across different manufacturing domains.
One of the key features of the tool is the AI-supported 3D analysis and comparison tool, known as ZG3D, developed by ITI. This tool enables the detection of differences between an expected or ideal 3D product model and the actual 3D model captured from the produced object. These differences can include geometric tolerances, shape deformations, and texture defects such as voids, cracks, or other surface anomalies. By comparing the captured model with the expected model, the tool can identify and highlight any discrepancies, allowing for effective quality monitoring and analysis.
In addition to 3D analysis, the tool also supports visual surface anomaly detection in 2D. This is achieved through unsupervised approaches developed by XLAB, which eliminate the need for laborious data collection and labeling of anomalous samples. Instead, the algorithms can be trained using defect-free samples, enabling straightforward adaptability to new product lines right from the start. This approach streamlines the process of detecting surface anomalies and ensures flexibility in monitoring product quality across different manufacturing scenarios.
The AIDEAS Repair–Reuse–Recycle Suite is a set of artificial intelligence solutions designed to manage the repair, reconditioning and end-of-life phases of industrial equipment, facilitating decisions on advanced maintenance, component reuse and recycling to extend the useful life of machines and improve their sustainability.
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.
