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08/12/2023

AI-PROFICIENT: Pioneering Advancements in AI Integration for Manufacturing Excellence 

AI-PROFICIENT, a groundbreaking project launched in 2020, has successfully demonstrated the transformative potential of advanced Artificial Intelligence (AI) technologies in manufacturing processes. Aimed at enhancing production planning, execution, efficiency and human-machine collaboration, AI-PROFICIENT has made significant strides in addressing challenges related to adaptability and resilience in Cyber Physical Production Systems. 

Key Achievements 

  • AI-PROFICIENT places a human-centered (anthropocentric) approach to AI at its core, adopting an Ethics-by-design philosophy. This emphasis on ethical considerations underscores the project’s commitment to responsible AI implementation. 
  • The project has developed a suite of cutting-edge tools and applications designed to address the specific challenges of manufacturing. These tools, ranging from surrogate data-driven models to natural human-computer interaction systems, are implemented into two remarkable Human-Machine Interface (HMI) applications. 
  • AI-PROFICIENT showcased its capabilities in three diverse production facilities, partnering with manufacturing enterprises Continental and Ineos. The project meticulously examined potential ethical and legal challenges associated with these environments, providing comprehensive recommendations for addressing them. 
  • The project’s tools include a variety of innovative solutions such as connected worker applications, quality data-analysis tools, process/machine-level anomaly detection, data-driven predictive AI analytics, and more. These tools are implemented into two remarkable HMI applications: the INEOS Augmented Reality (AR) Application and the Continental AI Dashboard.  

Driving Innovation Together: Meet the Collaborators Behind AI-PROFICIENT 

AI-PROFICIENT was made possible through the collaboration of ten partners from six European countries. Universite de Lorraine (France): Coordinator of the project, Continental France SNC (France), Tekniker (Spain), Ineos Services (Belgium), Tenforce (Belgium), VTT (Finland), Inos Hellas (Greece), Ibermatica (Spain), Institut Mihajlo Pupin (Serbia), Athens Technology Center (Greece) 

Download the full Press Release – EN version

07/12/2023

AI-PROFICIENT's Impactful Journey: Validation Analysis of Demonstration Scenarios and Insights from Manufacturing Innovation
13/12/2023

User Perspectives Unveiled: AI-PROFICIENT's Final Assessment in Use Case Evaluation

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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?​

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      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. ​