Approach

The project work plan comprises four phases in consistence with project methodology: 1) project set-up and requirements specification, 2) edge and platform AI development, 3) solution integration and human-machine interaction, and 4) project validation and ethical considerations; aligned with eight Work Packages (WP).

AIPROFICIENT is 36 months long and the objectives will be reached by: one WP for use case characterization and project requirements specification; two WPs on development of edge and platform AI technology, one WP on human-machine interaction and interfaces, one WP on platform integration and interoperability, one WP for use case validation and ethical considerations, one WP for dissemination, exploitation and standardization, and project management WP.

An overview of the project activities is presented as follows:

WP1 – Pilot site characterization, requirements and system architecture

This Work Package delivers the project foundation in the form of ethics by design platform requirements and specification, technical characterization of the pilot sites, operation scenarios and KPIs, an analysis of the operation conditions, legal and ethical issues, and identification of demo scenarios. It will prepare the consortium, and set the conditions for the rest of the WPs.

WP2 – Smart components and local AI at system edge

This Work Package is responsible for the introduction of local AI at the system edge by embedding it at the component level. It will deliver the raw data pre-processing, self-diagnostics algorithms, and component-oriented proactive maintenance, machine vision analysis, but also the control algorithms for closing the control loop from the system edge.

WP3 – Platform AI analytics and decision-making support

This Work Package delivers system-level AI services to the cloud-based platform, and includes development of digital twins, predictive AI analytics, and holistic generative optimisation to meet predefined goals. It will leverage physical modelling, rule-based stream processing, and data-driven AI techniques to develop a holistic multi-objective optimisation approach for improved production planning and execution.

WP4 Human-machine interfaces, explainable AI and shop-floor feedback

This Work Package is aimed to improve the human-machine interaction by developing the interfaces for the connected worker. This will include the development of a plant management dashboard for plant operators and smart mobile app for the workers on the shop floor. WP4 will be also aimed to improve the explainability and transparency of AI decision making.

WP5 – AI-PROFICIENT system integration and deployment

This Work Package deploys the system middleware with gateway transformation services, and integrates the AI services and enabling components (WP2 and WP3) to support the devised methodology. It will perform the integration with the plant automation systems and plant management solutions, as well as the integration with AI enablers of AI4EU platform.

WP6 – Use case evaluation and ethical considerations

This Work Package carries out the demonstration and validation activities to evaluate, through short- and long-term tests, the effectiveness of the delivered solution across all project pilots. It will deal with ethical aspects of the project, perform the instantiation of HLEG guidelines, and provide the recommendations for similar activities, and report on the lessons learnt.

WP7 – Dissemination, exploitation and standardization

This Work Package covers the communication and dissemination of the project results, aiming to increase the visibility of the project, and raise interest in the AI-PROFICIENT solution with the potential stakeholders. It will also develop the preliminary exploitation plan and IPR management strategies, as well as address the standardization related activities.

WP8 – Project management

This Work Package covers project management related activities, including financial, technical, communication, and risk management; as well as quality assurance of the project deliverables (e.g., reports, software, experimental results, guidelines, etc.). It will also set the data and knowledge management plan of the project.

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      How does Machine Learning relate to AI?

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      Machine Learning is a subset of AI
      Machine learning is a subset of AI. The idea is that machines will “learn” and improve at tasks over time rather than having humans continually input parameters. Machine learning is a practical application of AI.
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      What is Neural Networks?

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      Is a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain​
      Neural networks are a class of machine learning algorithms. The neuron part of the neural is the computational component, and the network part is how the neurons are connected. Neural networks pass data among themselves, gathering more and more meaning as the data moves along. Because the networks are interconnected, more complex data can be processed more efficiently.​
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      What is Artificial intelligence?​

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      Making a Machine intelligent​
      Artificial intelligence (AI) is intelligence—perceiving, synthesizing, and inferring information—demonstrated by machines, as opposed to intelligence displayed by non-human animals and humans​
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      The characteristics of the computer system capable of thinking, reasoning and learning is known as

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      artificial intelligence​
      Artificial intelligence (AI) is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience.​
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      The first AI programming language was called:​

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      IPL
      Information Processing Language (IPL) is a programming language created by Allen Newell, Cliff Shaw, and Herbert A. Simon at RAND Corporation and the Carnegie Institute of Technology about 1956. This is the first AI programming Language known.​
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      An AI technique that allows computers to understand associations and relationships between objects and events is called:​

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      pattern matching​
      Pattern matching is a technique used in AI which allows a computer program to identify a specific pattern that is present in a given data set and take appropriate actions based on the results of the analysis.​
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      Artificial Intelligence has its expansion in the following application.

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      All of the above​
      From manufacturing to marketing, businesses across multiple industries are leveraging artificial intelligence to optimize their processes and services.
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      Who is the father of artificial intelligence?​

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      John McCarthy​
      John McCarthy (September 4, 1927 – October 24, 2011) was an American computer scientist and cognitive scientist. He was one of the founders of the discipline of artificial intelligence.​
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      When did the term "artificial intelligence" first appear?​

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      In 1956​
      In 1956, at a conference at Dartmouth College, in Hanover, New Hampshire, the term "artificial intelligence" was coined​
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      Which is not the commonly used programming language for AI?​

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      Perl​
      Perl is not the commonly used programming language for AI. ​
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      Great, now that you understand the basics of Artificial Intelligence, we suggest that you glance at our project documents highlighting the improvements that AI services introduced in the manufacturing industry.
      Within the AI-PROFICIENT project, we piloted seven different use cases run across different manufacturing sites of INEOS and Continental. Our solutions aim at improving manufacturing efficiency while keeping humans in control.
      The following questions are based on the outcomes of AI-PROFICIENT use cases and should give you an idea of the practical advantages of AI for manufacturing. Click next to get started.
      Answer
      Great, now that you understand the basics of Artificial Intelligence, we suggest that you glance at our project documents highlighting the improvements that AI services introduced in the manufacturing industry.
      Within the AI-PROFICIENT project, we piloted seven different use cases run across different manufacturing sites of INEOS and Continental. Our solutions aim at improving manufacturing efficiency while keeping humans in control.
      The following questions are based on the outcomes of AI-PROFICIENT use cases and should give you an idea of the practical advantages of AI for manufacturing. Click next to get started.
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      By how much percent AI implementation decreases raw material waste in tyres production?​

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      8%
      Combining Explainable AI algorithms with the global optimisation algorithms in the rubber extrusion allows continuous suggestion of the operator's optimal machinery parameters. This reduces the time when the rubber extrusion process is set and thus reduces the time when the suboptimal rubber outcome that cannot be used in further tire production.
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      Which of the following is a polymer extrusion process:​

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      A chemico-mechanical process​
      Chemically, the process involves the melting and flow of a polymer material under heat, which can result in chemical reactions such as degradation or cross-linking of the polymer chains. The chemical properties of the polymer material and the extrusion conditions, such as temperature, pressure, and residence time, can affect the final properties of the extruded product.​

      Mechanically, the process involves the deformation of the molten polymer material under pressure as it is forced through a die orifice to form a continuous shape. The mechanical properties of the polymer material, such as its elasticity, viscosity, and melt strength, determine its ability to withstand the stresses and strains imposed during the extrusion process.​
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      Which are the key elements of an optimization problem:​

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      The objective function, the decision variables and the constraints​
      The objective function defines the goal of the optimization problem, whether it is to maximize or minimize a certain value. The decision variables are the variables that can be adjusted or controlled to achieve the optimal solution to the objective function. The constraints are the limitations or restrictions that must be satisfied during the optimization process.​

      The other two options mentioned in the question are not related to the key elements of an optimization problem. Hyperparameters, validation schemes, and accuracy metrics are more related to machine learning or data science problems. The differential hypothesis is not a common term used in optimization problems. ​
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      When the blade of the cutting system starts to wear out but it can keep cutting:​

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      It might work well but chances are eventually it will compromise the quality of the final product.​
      As the blade of the cutting system starts to wear out, it will gradually lose its sharpness and efficiency, which can affect the quality of the cut and increase the cutting forces. If the blade is allowed to continue cutting even after it has worn out, it can lead to several negative consequences.
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      According to the literature, which are the three main sources of variation when trying to model a cutting process in an industrial setup:​

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      Workpiece factor, feed factors and saw blade factors​
      In a well-controlled manufacturing environment, the cutting process should be designed to be robust and less sensitive to operator variation. Regarding to the environmental factors, they are less significant compared to other sources of variation.
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      Developing the AI service which will improve tread alignment in part of the Combiline depends upon marking pictures of the tread pieces, which will then be used to train the AI. How should the developers and industrial partner approach this picture marking task for AI training?​

      Answer
      The developers should clearly specify how many pictures need to be taken, how long the process will take, and discuss with the industrial partner who will do it.​
      An ethical approach to this task involves clearly specifying how many pictures, and how much added work time it involves. On that basis the developer can discuss with the industrial partner who should do the task, e.g. whether the developers themselves are better placed to do it, or whether the industrial partner’s operators should do it. If the latter, it should be made clear to the operators whether they can refuse to do it, or it is an obligation which makes up part of their work. ​
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      What is explainable AI and how it can help in manufacturing?​

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      XAI stands for "explainable artificial intelligence" and it can help in manufacturing by improving process control, enabling predictive maintenance, enhancing quality control, and improving safety.​
      XAI stands for explainable artificial intelligence and provides insights into decision-making processes, identifies patterns and anomalies in equipment data, and detects defects in real-time. This allows manufacturers to make better decisions, reduce downtime and maintenance costs, and address problems quickly.​
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      How should the developers engage the operators when developing the AI service and related apps and HMIs, with the goal of simplifying the job of confirming correct chemical product bag placement before production? ​

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      The developers should work closely with the operators to design the services, choose the HMIs and adapt the apps, according to the actual work context. ​
      To implement an ethical by design user centered approach, the developers should work closely with the operators who will actually use the service. They should take care to understand the specific work conditions (dust, noise, time for tasks, etc.), discuss the planned solutions with the operator users, design initial mockups and discuss these with the operator users regularly in an iterative process as they develop the service. ​