The emergence of artificial intelligence (AI) in industry is not merely a question of evolution from traditional automation. We at Aritex have already embraced this profound paradigm shift, which is redefining the way we conceive, design and operate production systems.
Because AI integrated into manufacturing automation means that engineers no longer work solely with predictable, explicitly programmed systems, but with environments capable of learning, adapting and anticipating. And the work teams we’ve adapted to this new way of working are better equipped to harness the full potential of AI.
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Key AI developments for industrial automation
AI is causing three key global transformations in industrial automation:
- A move from deterministic to adaptive systems
- Changes from explicit programming to implicit learning
- Reaction is being left behind and anticipation is taking its place
This has direct implications for highly automated sectors such as the automotive industry, where Aritex has extensive experience: more flexible plants, a greater emphasis on data, and the rise of hybrid roles that combine industrial expertise, software and analytics.
An engineer’s vision drives AI
The emergence of AI opens up a world of possibilities for industrial engineers. To begin with, AI will radically transform their role. Over the next 10–15 years, professionals who wish to add value will do so by drawing on in-depth structural knowledge based on:
- A solid grounding in industrial processes, which is essential for putting any model into context
- Control and automation know-how, to understand how physical systems interact
- Analytical skills, including statistics and the basics of machine learning
- A systemic mindset: a key element for integrating multiple disciplines into complex environments
Aritex’s experience shows us that AI does not eliminate the need for engineering; on the contrary, it enhances its capabilities. But it will also sideline those who are unable to incorporate it into their work.
That is why we are exploring the integration of AI-based features into our digitalisation processes, while also identifying the most suitable applications for each specific case and configuring the tools to make the most of artificial intelligence.
Current applications of AI in manufacturing
To begin with, the integration of AI agents into any in-house system is already bringing about a clear improvement in process optimisation, as they can capture data from the MES, the CMMS (maintenance), the WMS (warehouse management), etc., and provide immediate responses based on a large volume of data.
The automotive industry is one specific sector where AI is already making a tangible impact. It is integrated at many levels in the production process
Quality control with machine vision
One of the most established applications is the use of computer vision systems based on convolutional neural networks (CNNs). These systems enable to:
- Detect defects in car bodywork (scratches, porosity, paint defects)
- Check weld quality
- Check assemblies
- Identify dimensional deviations
Unlike traditional systems based on fixed rules and strict lighting conditions, AI utilises models trained on thousands of images that can recognise complex patterns.
The results are plain to see:
- Reduced false positives
- Detection of defects that are invisible to the human eye
- Greater adaptability to new models without having to redesign the system
This enables 100% in-line inspection without affecting production rates, a key milestone in high-throughput environments.
AI-powered predictive maintenance
Another key feature today is data-driven maintenance. By applying machine learning models to variables such as vibration, temperature, electricity consumption and fault logs, it is possible to predict:
- Faults in electric motors
- Bearing wear
- Tool wear
- Issues with welding robots
The approach to industrial asset maintenance has now shifted from corrective-preventive maintenance to predictive maintenance. How? By using a CMMS (Computerised Maintenance Management System) that monitors the ‘vital signs’ of the asset involved and schedules the necessary intervention when indicators reveal a pattern suggesting that a failure is likely.
The impact is significant: reduced unplanned downtime, an optimised spare parts inventory and improved OEE (Overall Equipment Effectiveness).
Process optimisation with AI
AI enables real-time adjustment of parameters to maximise efficiency in welding and assembly. Unlike traditional control systems that react to deviations, data-driven models:
- Analyse large volumes of historical data
- Identify ideal operating conditions
- Replicate patterns of maximum efficiency
For example, a system can automatically adjust the power and route during laser welding processes to offset variations in the sheet metal, resulting in higher quality and less waste.
The key here is not just to monitor, but to learn continuously from the process.
A prime example of the challenges we face at Aritex is quality control on painting and machining lines for parts in the aerospace industry. We have been implementing Augmented Reality solutions for years, as we explained in this article . These systems are now enhanced by AI capabilities.
Smart and collaborative robotics
Industrial robotics is also evolving towards more flexible systems. Nowadays robots can:
- Adapt to geometric variations
- Identify parts using machine vision
- Work alongside operators in shared environments (cobots)
This marks a departure from traditional robotics, which is characterised by highly structured environments and rigid programming. Robots now adapt their behaviour in line with sensor data and context, enabling greater flexibility in production.
Planning and internal logistics
AI also plays a key role in optimising production flows:
- Dynamic production sequencing
- Intelligent buffer management
- Real-time resource allocation
- Optimisation of AGV (Automated Guided Vehicle) and AMR (Autonomous Mobile Robot) fleets
In ‘just-in-sequence’ systems, where timing is critical, even minor errors can lead to significant inefficiencies. Machine learning algorithms enable these systems to be stabilised and improve their resilience.
Future prospects for AI in industry: towards the fully integrated smart factory
AI is not only optimising what already exists, it also has the potential to completely redefine the industrial life cycle.
AI-assisted industrial design
Current design processes rely on CAD/CAE tools and simulations that use approaches such as the finite element method (FEM). However, AI is introducing new capabilities:
Advanced generative design
The engineer defines constraints (loads, materials, costs) and the AI generates multiple optimised solutions, many of which are counterintuitive.
Design-manufacturing integration
Systems will be able to design parts whilst taking actual production constraints directly into account: tolerances, welding processes or line variability.
This will enable a truly automated approach to design for manufacturing (DFM).
Industrial automation
One of the most disruptive changes will take place in programming:
- Automatic generation of PLC code from functional specifications
- Automated logical verification
- Programming robots using demonstration-based learning
Instead of programming step by step, the operator will be able to demonstrate a task, and the system will learn how to carry it out.
Advanced Autonomous Robotics
The next step will be robotics capable of understanding context:
- Flexible handling of unstructured parts
- Adaptation to model changes without reprogramming
- Greater autonomy in dynamic environments
This will bring about truly flexible production, where changeover times are reduced and adaptability is increased.
Autonomous maintenance
Maintenance will evolve from prediction – where it currently stands – towards autonomy:
- Automatic root-cause diagnosis
- Intervention planning without human intervention
- Automatic spare-parts management
- Dynamic production reconfiguration
The system will not only detect problems, but also make operational decisions.
Digital twins and AI
The combined concept of a digital twin and AI represents one of the greatest opportunities:
- Real-time simulation of thousands of scenarios
- Continuous planning optimisation
- Assessment of investments prior to implementation
- Redesign plant layouts
This will enable us to anticipate bottlenecks and validate changes before actually implementing them, thereby reducing risks and costs.
Introduction of new product lines
AI will also transform the engineering phase:
- Automatic layout design
- Optimised equipment selection
- Accurate ramp-up estimation
- Early identification of risks
Experience will continue to be a key factor, but it will be complemented by models trained with data taken from many previous projects.
Engineers in the Age of AI
The integration of AI into manufacturing automation is not a passing trend, but a structural transformation of the industry. It is changing the way we design, programme and implement production systems.
This presents opportunities and challenges for engineers. The opportunity to work with more powerful tools, capable of solving complex problems and significantly improving efficiency. It also creates the challenge of adapting to an environment where data, software and artificial intelligence are just as important as mechanics or electronics.
In this new landscape, the real value will not lie in knowing how to program a machine, but in understanding how to integrate intelligent systems into a complete industrial process. At Aritex, we can advise you on how to integrate these AI-based systems.
Because the factory of the future won’t just be automated: it will be smart, adaptable and predictive. And the engineer who leads that change will be the one who understands that AI is not just another tool, but the heart of the new wave of automation.
