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Improving non-deterministic uncertainty modelling in Industry 4.0 scheduling

arXiv.org Artificial Intelligence

The latest Industrial revolution has helped industries in achieving very high rates of productivity and efficiency. It has introduced data aggregation and cyber-physical systems to optimize planning and scheduling. Although, uncertainty in the environment and the imprecise nature of human operators are not accurately considered for into the decision making process. This leads to delays in consignments and imprecise budget estimations. This widespread practice in the industrial models is flawed and requires rectification. Various other articles have approached to solve this problem through stochastic or fuzzy set model methods. This paper presents a comprehensive method to logically and realistically quantify the non-deterministic uncertainty through probabilistic uncertainty modelling. This method is applicable on virtually all Industrial data sets, as the model is self adjusting and uses epsilon-contamination to cater to limited or incomplete data sets. The results are numerically validated through an Industrial data set in Flanders, Belgium. The data driven results achieved through this robust scheduling method illustrate the improvement in performance.


Smart Factories Integrate PLCs, Automation and Robotics

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The beginning of this, the third decade of the new millennium, may have started out rather bleak, but it does not mean that things like innovation or advances in technology have come to a grinding halt. In fact, quite the opposite is true. As we find ourselves in the midst of the Fourth Industrial Revolution (more commonly referred to as Industry 4.0) the adoption of automated machinery in the manufacturing sector shows no indication of slowing down. But how is it that manufacturers can integrate PLCs – a key component of manufacturing developed almost 60 years ago – with contemporary automation and robotics solutions to create the smart factories of tomorrow? Let's take a deeper dive into that relationship.


Industry 4.0: Impacts of Machine Learning on the Manufacturing Industry

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The manufacturing industry is deeply impacted by the rise of machine learning projects. Originally coined by the German government in 2011, Industry 4.0 refers to the idea that the world has undergone the process of moving into a Fourth Industrial Revolution. Industry 4.0 is now widely accepted as the next paradigm for production, and if you're not already embracing the new revolution, then you are behind. Many outside of the tech industry are familiar with the First and Second from history textbooks but may not know where modern society now stands. The First revolution was a move from hand-made products to machine-made, the Second was marked by continuous processes and assembly lines, and the Third represented the widespread use of computers and robotics for industrial automation.


Turn the Light Back On!

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My childhood friend Marco was born and raised like me, in the Italian maritime city of Monfalcone. Fifty miles away from Venice, at the very North tip of the Mediterranean, he works in the city shipyard. Marco is a descendant of a long history of artisans whose lineage can be traced back to Venetian shipbuilders in the Middle Ages. Unlike his ancestors, much of Marco's work relies on his manual skills, augmented by today's digital aids. Paraphrasing an old Industry 4.0 joke, I once told Marco how the super-automated shipyard of the future "…will only need two employees: a guard dog, and you, hired to feed the dog."


Industry 5.0: what is it and what will it do for manufacturing?

#artificialintelligence

Industry 4.0 is the bringing together of robots, interconnected devices and fast networks of data within a factory environment, basically to make the factory more productive and to execute the routine tasks that are best done by robots and not best done by humans. Industry 4.0 starts to move towards Industry 5.0 when you begin to allow customers to customise what they want There are reports flying around at the moment saying the UK can get massive benefits from Industry 4.0 and it can, but we're not at the beginning; we're right in the middle of it. To me, it's not just about generating increased productivity, but long-term, high-value careers because jobs change as you're introducing Industry 4.0 technologies into a factory. As soon as you enter an Industry 4.0 factory, you also get a massive increase in safety, quality and reduced waste. Industry 4.0 moves towards Industry 5.0 when you allow customers to customise what they want.


Semantic CPPS in Industry 4.0

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Cyber-Physical Systems (CPS) play a crucial role in the era of the 4thIndustrial Revolution. Recently, the application of the CPS to industrial manufacturing leads to a specialization of them referred as Cyber-Physical Production Systems (CPPS). Among other challenges, CPS and CPPS should be able to address interoperability issues, since one of their intrinsic requirement is the capability to interface and cooperate with other systems. On the other hand, to fully realize theIndustry 4.0 vision, it is required to address horizontal, vertical, and end-to-end integration enabling a complete awareness through the entire supply chain. In this context, Semantic Web standards and technologies may have a promising role to represent manufacturing knowledge in a machine-interpretable way for enabling communications among heterogeneous Industrial assets.


Semantic CPPS in Industry 4.0

arXiv.org Artificial Intelligence

Cyber-Physical Systems (CPS) play a crucial role in the era of the 4thIndustrial Revolution. Recently, the application of the CPS to industrial manufacturing leads to a specialization of them referred as Cyber-Physical Production Systems (CPPS). Among other challenges, CPS and CPPS should be able to address interoperability issues, since one of their intrinsic requirement is the capability to interface and cooperate with other systems. On the other hand, to fully realize theIndustry 4.0 vision, it is required to address horizontal, vertical, and end-to-end integration enabling a complete awareness through the entire supply chain. In this context, Semantic Web standards and technologies may have a promising role to represent manufacturing knowledge in a machine-interpretable way for enabling communications among heterogeneous Industrial assets. This paper proposes an integration of Semantic Web models available at state of the art for implementing a5C architecture mainly targeted to collect and process semantic data stream in a way that would unlock the potentiality of data yield in a smart manufacturing environment. The analysis of key industrial ontologies and semantic technologies allows us to instantiate an example scenario for monitoring Overall Equipment Effectiveness(OEE). The solution uses the SOSA ontology for representing the semantic datastream. Then, C-SPARQL queries are defined for periodically carrying out useful KPIs to address the proposed aim.


SublimeCode

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How it all started My journey with artificial intelligence started with my dissertation when my tutor suggested a project that would change my perception of the future of technology. My initial idea was a simple PWA(progressive web app) that would facilitate entertainment service providers to connect with potential clients. For some reason, my tutor considered this too basic for my potential(I still don't understand why) and suggested instead a project that would predict the availability of those service providers. This would've only been possible with an artificial intelligence approach, a topic unfamiliar to me at the time. Extensive research, online courses, and lack of sleep were my only options.


The Role of Collaborative Robots in Industry 5.0

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For many years, the goal of the manufacturing industry has been simple -- create smart, automated production flows that emphasize digital communication and the collection of data to continuously optimize production. This model of production is known as Industry 4.0, which is really an umbrella term that lumps together a variety of things like cyber-physical systems, the internet of things, cloud computing, cognitive computing, and artificial intelligence. But there may be an even better system – one that emphasizes collaborative robotics and values human input. In contrast to Industry 4.0, Industry 5.0 aims to put the human touch back into development and production. Industry 5.0 is all about granting human operators the benefits of robots such as technical precision and heavy-lifting capabilities.


Accelerating smart manufacturing

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On March 11, the novel coronavirus disease, COVID-19, was officially declared a pandemic by the World Health Organization.1 As regions in North America and Europe faced critical shortages of personal protective equipment (PPE) to protect frontline workers and ventilators to treat patients, global supply chain bottlenecks and scarcity of supply put pressure on overwhelmed health care systems in the hardest-hit regions. Leaders in many countries were desperate for supplies, and many turned to the manufacturing industry for assistance. The examples below illustrate the possible value of manufacturing ecosystems and digital tools in delivering faster turnaround and enabling better collaboration among partners. They also demonstrate how quickly coinnovation and collaboration can occur when organizations have a common goal and can leverage digital technologies.