AI IS already making waves in other industries by helping businesses automate complex tasks, make smarter decisions, and improve efficiency. In 3D printing, AI is poised to solve some of the major technical challenges holding the technology back, like quality and scalability. By optimising designs, monitoring print quality, and making production more scalable, AI could be the key to pushing 3D printing into the mainstream.

The current challenges and how AI is stepping in to solve them can be outlined as follows:

3D Printing: More than just trial and error?

Achieving high precision in 3D printing can be tricky. The quality of the print can be affected by a range of factors—material choice, process errors like over- or under-extrusion, and design mistakes, to name a few. These issues can lead to defects such as increased porosity, poor mechanical properties, cracks, distortions, or incomplete parts. Not only does this waste resources, but it also extends production times.

Typically, getting a quality print involves a lot of trial and error. Even if you get it right once, there's no guarantee the next print will turn out the same, if you're using a different printer. This brings us to the next big hurdle: scalability.

The scalability struggle

While 3D printing is great for prototyping and small-batch production for now, scaling up for mass production is another story. Maintaining consistent quality across large print jobs is difficult, limiting its use in high-volume industries. Automation is a critical gap that still needs to be addressed. Plus, the costs are often too high for mass production – unless, at least in some cases, the designs are optimised for additive manufacturing (AM).

Traditional design vs. design for AM

Design for traditional manufacturing is not the same as designing for additive manufacturing. However, traditional CAD tools can be pretty limiting when it comes to 3D printing. They aren’t always optimised for 3D printing's unique requirements, leading to inefficient designs and longer development cycles.

To overcome these limitations, integrating AI into the design process could offer substantial benefits.

Generative design: The new ChatGPT for designers?

With AI, designs can be optimised for better printing, and even create lighter and stronger parts. AI can highlight areas for improvement, suggest modifications, and explore new design possibilities that human designers might miss. These AI-generated designs often look more like natural structures, such as bones, rather than traditional parts.

Several AI-based software packages, like Autodesk's Netfabb, are already evaluating and optimising design files for 3D printing using generative design. By inputting the desired parameters, manufacturers can let AI find the most efficient way to design the part, minimising the need for endless simulations and iterations. This makes 3D printing more efficient and cost-effective.

There are even tools that allow users to upload 2D images or describe what the part should look like, and AI takes over the rest, just like ChatGPT for texts or Midjourney for images.

Broader adoption through design automation

AI-driven design automation makes 3D printing more accessible by simplifying complex processes and lowering the entry barrier. By automating design iterations and optimisations, AI enables engineers and designers of different skill levels to efficiently create sophisticated parts and assemblies. This streamlines prototype development, reducing both time and resource expenditure, and promises to expand 3D printing applications across diverse sectors.

Monitoring and predicting defects

AI can also monitor the printing process in real-time, predicting potential defects and adjusting parameters to prevent errors. By analysing sensor data continuously and leveraging machine learning models trained on extensive datasets, AI enhances print precision. This mitigates issues like warping and layer misalignment, ensuring higher-quality outputs and minimising material waste. In turn, this improves overall print reliability and efficiency.

Enhancing consistency and scalability

AI helps scale 3D printing for mass production by optimising workflows and ensuring consistency across multiple machines. Advanced algorithms can coordinate large-scale print jobs and ensure that each printer operates under optimal conditions. By boosting efficiency and reducing failed prints, AI lowers the overall cost of 3D printing, transforming it into a scalable and reliable production method suitable for various industrial applications.

AI and 3D printing are already transforming industries like healthcare, aerospace, and automotive. From creating custom prosthetics to optimising lightweight structures for aircraft, the impact is significant. While AI might seem like a buzzword in some contexts, in 3D printing, it is a game-changer.

Henrike Wonneberger is COO of Replique

replique.io

As competition among manufacturers continues to grow, more digitalised and automated approaches are being adopted through the addition of robotics and AI to their manufacturing processes. These industry 4.0 technologies are transforming manufacturers’ value chains by offering greater flexibility, improved efficiency, and cost savings. These benefits span industries, with key industrial sectors such as automotive, aerospace, energy, and marine, adopting them for applications such as custom production moulds and tools, lightweighting finished parts, minimising waste, and reducing lead times.

One rapidly emerging technology driving this shift is 3D printing, more specifically Large-Format Additive Manufacturing (LFAM) combined with robotics. Yet, many companies hesitate to integrate these innovations, uncertain of where to begin, and which applications best benefit their business.

Practical Uses of LFAM

It is through industrial applications that the main benefits of robotic LFAM are best demonstrated, including design flexibility, minimised material waste, shortened production times, enhanced sustainability, and reliability.

For example, in aerospace and railways, composite 3D printing is used to produce large-scale parts, from lightweight and strong cabin components for passenger rolling stock, to extremely precise functional mock-ups and tooling. This can include jigs and fixtures for trimming, milling, and assembly; tools for positioning and vacuum-assisted drilling of airplane fuselage panels; master moluds for composite glass or carbon fiber parts; inspection fixtures for checking part precision; and cold lamination tools for aircraft fuselage maintenance.

In the automotive industry, LFAM is revolutionising the production of large, monolithic, lightweight components such as body panels and tooling. These parts can be produced faster, more cost-effectively, and with a significantly smaller environmental footprint.

LFAM is also widely used in architecture and construction, for creating forms, structural components, façade elements, and partition panels. It enables on-demand production without moulds and reduces transportation needs through on-site manufacturing. The Heron AM system is often applied to produce indoor, modular setups and unique displays for office and commercial spaces, customised, large-scale walls and façade elements with complex shapes, promoting eco-friendly and efficient construction.

Advantages of LFAM robotic platforms

When integrated into industrial supply chains, LFAM robotic platforms offer manufacturers an unparalleled opportunity to streamline and optimise production processes. The modular configuration of these platforms provides exceptional versatility, allowing manufacturers to customise setups according to their specific needs. For example, with our Heron AM or Vipra AM platforms, different extruding or deposition heads can be paired with robotic arms of varying sizes to achieve specific manufacturing needs and achieve efficiencies specific to their production purposes.

3D printing with robotic systems enables the production of highly complex, custom-designed parts with drastic reduction of waste and inefficiencies. These systems can deliver remarkable speed, precision, and flexibility. The use of smart manufacturing solutions such as sensors and IoT technologies that monitor and optimise the production data and 3D printing process as a whole enhances the LFAM robotic platforms’ performances. Core Manufacturing Benefits of Robotic LFAM

One benefit of LFAM is that it reduces production costs by eliminating the need for costly moulds, tooling, and labor-intensive assembly processes. Pairing LFAM with robotic systems automates workflows, cutting labor costs and speeding up time-to-market of finished parts. The ability to produce short-run or custom parts, which would be prohibitively expensive using traditional methods, becomes highly feasible with LFAM.

Manufacturers can also scale production seamlessly without the need for retooling or reconfiguring machinery when using LFAM. This is particularly advantageous in industries like aerospace and automotive, where custom designs and large components are common. The flexibility of LFAM robotic platforms enables companies to switch between different product lines or custom orders with zero to minimal downtime.

Additionally, LFAM enables the creation of complex, lightweight designs that are also strong and durable, particularly valuable in aerospace and automotive sectors. This level of customisation provides a significant competitive advantage in markets that demand unique, tailor-made products.

LFAM also minimises waste significantly compared to traditional manufacturing processes, which align with many manufacturers' sustainability goals. If this is a priority, it is worth looking into pellet-form recycled and high-performance materials, including thermoplastics and composites. This reduces material costs, and, by printing parts net shape, waste is cut drastically, lowering the overall use of raw materials and environmental impact of production.

The future of robotic LFAM

As awareness of its core benefits grows, an increasing number of OEMs are adopting robotic additive manufacturing to streamline production and expand the portfolio of applications and end parts they can achieve. The industry continues to introduce advanced solutions capable of producing composite and advanced material parts more sustainably, cost-effectively, and with greater design flexibility. For early adopters, these unique capabilities have become powerful business assets, driving cost savings, operational efficiency, and the creation of new revenue streams.

Violetta Nespolo is chief marketing and strategy officer at Caracol AM

www.caracol-am.com

Led by Professor Deborah Greaves at the University of Plymouth, the Supergen ORE Hub includes co-directors from a consortium of ten universities. From The University of Manchester, Professor Tim Stallard serves as a Co-Director and Dr Hannah Mullings is an Early Career Researcher (ECR) Co-Lead.

The report, aimed at researchers, industry, policymakers, and the public, summarises the current impacts of climate change and the UK’s progress in reducing carbon emissions. It outlines offshore renewable energy deployment pathways needed for a just, sustainable and secure energy transition, with 2040 identified as a key milestone towards the UK 2050 Net Zero goals.

Key findings from the report include:

• Achieving 100 GW of offshore wind energy by 2040 is critical, requiring a nearly seven times increase in capacity. Radical innovation is essential to optimise and scale up growth.
• Tidal stream energy has the potential to grow alongside offshore wind and could reach over 11 GW of capacity in UK waters. Rapid progress is required, to deliver the EU SET Plan target of 6 GW deployment of tidal stream by 2050.
• Wave energy has significant potential, with an estimated exploitable resource of 25 GW in the UK. Deployment of 12 GW of wave and tidal stream by 2050 could add £40 billion GVA to the UK economy and reduce energy balancing costs by £1 billion annually. Investment in innovation over the next decade is crucial to achieving this potential.

Professor Tim Stallard said: “The ORE Outlook 2040 report highlights the high potential for Offshore Renewable Energy sources to contribute to the UK meeting its Net Zero goals. The growth required cannot be realised by upscaling current approaches alone and urgent action is needed to accelerate innovation and deployment.”

The report also explores ORE development through lenses of planning and consenting, people, supply chain, and infrastructure and grid. Investment in research and innovation is highlighted as crucial to de-risking new technologies, reducing costs, improving performance and ensuring the UK retains its technological leadership on the global stage.

“This report highlights the critical role of research and innovation in driving the growth of offshore renewable energy. Supporting early career researchers and fostering collaboration across disciplines is key to achieving sustainable and equitable energy solutions for the future."
Dr Hannah Mullings, Research Fellow, EPSRC SuperGen ORE Impact Hub ECR Co-Lead

The Supergen ORE Hub, established by the Engineering and Physical Sciences Research Council (EPSRC), aims to deliver strategic and coordinated research on sustainable power generation and supply.

Read ORE Outlook 2040 here

AI technologies are already playing a role in improving services across the UK. In healthcare, for instance, AI is helping hospitals diagnose conditions such as breast cancer more quickly, assess pain levels for non-verbal patients, and streamline patient discharge processes. These applications align with the government’s aim to modernise the NHS and deliver better outcomes for patients.

The Prime Minister emphasised AI’s potential to benefit working people, including improving the efficiency of public services. Examples include using AI to simplify planning consultations, reduce administrative burdens for teachers, and identify road maintenance needs such as potholes. The plan also aims to boost productivity, with IMF estimates suggesting that fully embracing AI could raise UK productivity by up to 1.5 percentage points annually, potentially adding an average of £47 billion to the economy each year over the next decade.

Key measures in the AI Opportunities Action Plan include:

• Establishing dedicated AI Growth Zones to accelerate planning permissions and infrastructure development, starting with Culham, Oxfordshire
• Increasing public compute capacity twentyfold to support AI applications, beginning with the construction of a new supercomputer
• Creating a National Data Library to enable secure access to public data for AI development
• Forming an AI Energy Council to address energy requirements for AI advancements and support clean energy initiatives
• Launching a new team to enhance the UK’s sovereign AI capabilities.

The plan also outlines measures to attract global investment. Recent commitments from companies such as Vantage Data Centres, Nscale, and Kyndryl total £14 billion and are expected to create over 13,000 jobs nationwide. These investments include new data centres, a tech hub in Liverpool, and a sovereign AI data centre in Essex.

The UK seeks to position itself as a global leader in AI, building on its status as the world’s third-largest AI market. The strategy draws lessons from approaches in the US and EU to balance innovation with long-term stability, ensuring that businesses have the necessary support to thrive.

2025 IS the year humanity is expected to start transitioning into a new era called the ‘Autonomous Society.’ This evolution, rooted in the predictions of the SINIC Theory, marks the next phase in how society, science and technology interact.

SINIC Theory — first created in 1970 by OMRON's founder Kazuma Tateishi and later refined by HRI — describes a change in human attitudes that sees society go from optimisation to autonomy.

In the ‘Optimisation Society,’ the focus has been on streamlining processes and driving efficiency improvements over a period spanning 19 years (2005 - 2024). Whereas the ‘Autonomous Society’ heralds a change of tact, with systems becoming self-regulating, self-learning and capable of independent decision making.

According to HRI’s updated interpretation of the SINIC Theory, people are no longer passive recipients adapting to societal systems. Instead, they seek active participation in shaping the technologies and structures that influence their lives.

With that in mind, let’s explore the trends and technologies that are leading us into this new era.
 
Ethical AI

There’s no other place to start than with the use of AI. 2023 was the year the world discovered generative AI before organisations began using and deriving business value from this new technology in 2024.

In the latest McKinsey Global Survey on AI, 65% of respondents reported that their organisations regularly use gen AI, nearly double the percentage from the previous survey just ten months ago.

As a result, consumer expectations and evolving regulations have driven a "Do No Harm" approach, where AI systems are designed not only to perform efficiently but also to minimise societal risks.

For instance, the European Union's AI Act, implemented in late 2023, introduced stringent oversight for high-risk AI applications, setting a global standard for ethical governance.

In manufacturing, I expect AI to enable autonomous systems that optimise operations as well as ensuring transparency and accountability in decision making.

In 2025 and beyond, AI-driven technologies will automate routine tasks, enhance predictive maintenance and improve quality control, yet ethical AI will be crucial to ensuring that these systems are auditable and appealing to investors.

Electrification — not just EVs

Industries are also under increasing pressure to reduce their carbon footprints. This is particularly true for the manufacturing and production sectors, which account for one-fifth of global carbon emissions and 54 per cent of global energy usage.

That’s why we’re seeing a broader uptake toward electrification. The transition from fossil fuel-based energy systems to electric-powered solutions is enabling systems to not only self-regulate but also to work toward minimising environmental impacts.

As industries electrify, optimising energy use is vital to cutting costs and environmental impacts. This calls for rethinking energy consumption through innovative designs and advanced materials that enhance efficiency and performance.

OMRON's Gallium Nitride (GaN) technology is just one example that delivers compact, energy-efficient power conversion with minimal energy loss to support electrified systems.

More than ever, renewable energy sources, such as solar and wind, coupled with energy storage solutions, will be essential as the adoption of electrification will also extend to key areas, including industrial machinery, transportation fleets and energy generation.

What’s more, the push for electric vehicles (EVs), driven by regulations mandating higher EV sales and bans on petrol and diesel cars by 2035, will further accelerate electrification efforts.

It won’t be long before manufacturing operations become cleaner and more sustainable, helping to meet global decarbonisation targets and advancing the net-zero by 2050 agenda.

BESS

To achieve this, energy storage systems will become the backbone of this infrastructure. In particular, the capacity of all Battery Energy Storage Systems (BESS) is projected to increase more than sevenfold by 2028, reaching 260GWh.

Such growth is driven by innovations in battery technology, including lithium-ion and emerging alternatives like solid-state batteries, as well as advances in energy management software that enhance system efficiency and grid responsiveness.

By storing excess energy generated during peak conditions, BESS ensures a stable power supply even when renewable generation fluctuates. Data centres, which consume vast amounts of energy, are also adopting BESS to provide backup power and manage demand peaks, improving energy efficiency and reducing costs.

As a result, Goldman Sachs Research estimates that data centre power demand will grow 160% by 2030. Concerningly, carbon dioxide emissions of data centers are expected to more than double between 2022 and 2030 as a result, with AI gathering momentum.

As AI potentially adds 200 terawatt-hours to annual power consumption, the need for smarter energy management will continue to be a focus throughout the year.

Collaboration

Europe will likely need to invest over $1 trillion in upgrading its power grid infrastructure to deal with demand. A way to tackle this is by developing partnerships across industries to aid the transition into the 'Autonomous Society.’

This investment will be crucial for integrating renewable energy sources and enhancing energy storage capabilities to allow power systems to handle the growing consumption driven by technologies like AI and electrification.

Automation parts suppliers, like Foxmere, help to support companies leading this charge by supplying the essential components needed for these advancements.

By partnering with a trusted parts supplier, businesses can integrate emerging technologies and ensure a smoother, cost-effective societal transition from optimisation to autonomy.

https://foxmere.com/en

Nicknamed 'Wednesday', CASBOT 01 stands at 1.79m tall, weighs 60kg, and features 52 degrees of freedom with a computing power of 550 TOPS, enabling 550 trillion operations per second and over four hours of continuous operation.

CASBOT 01 adopts an ID design that merges mechanical engineering with organic aesthetics, precision dexterous bionic hands and high dynamic force control ability. Its head, with two degrees of freedom (DoF), is equipped with multiple sensors to interact visually and auditorily. These advanced features enable the robot to stand, walk, run, and jump steadily while also performing practical tasks such as organising clothes, assembling a table lamp, changing light bulbs, and twisting screws. This comprehensive functionality lays a strong foundation for multi-scenario applications.

In terms of mechanical structure design, CASBOT has balanced the motion range with styling aesthetics. By adopting high-strength metal materials and creating a reinforced ultra-thin hollow cavity, the structure ensures stability, impact resistance, and reduced overall weight, making CASBOT 01 a versatile and robust solution for various environments. CASBOT 01 features a self-developed high-efficiency joint system and an end-to-end model that guarantees precision operation and task adaptation. The innovative training technologies further improve the intelligence level and flexibility of the robots to meet cross-scenario needs.

The humanoid robot can be applied in a wide range of operations including smart home, commercial services, industrial manufacturing, aviation and navigation, emergency rescue, and other diverse scenarios.

In September 2024, CASBOT reached a strategic cooperation agreement with Lenovo and deployed the CASBOT humanoid robots in Lenovo's intelligent manufacturing factory. The compliant force control and high-precision operation improve the automation level of the production lines, reduce cost, and inject new vitality into manufacturing transformation and upgrades.

CASBOT is opening the foundational skills library of humanoid robots to all industry partners to collaboratively build a more refined professional skills library. This initiative aims to deepen the understanding of diverse industry needs, enabling humanoid robots and embodied intelligence technologies to effectively serve various scenarios. By harnessing intelligent technologies, CASBOT strives to integrate robots into human lives, realising a future of human-machine synergy.

www.casbot.tech

Zhao Tongyang, the founder and CEO of EngineAI, highlighted the company's vision to develop world-leading general-purpose humanoid robots while continuously accelerating innovation in the embodied intelligence revolution. He emphasized that EngineAI is committed to launching scalable products at competitive prices, aiming to achieve the production and sales of over a thousand units by 2025.

Zhao is a seasoned entrepreneur in the robotics industry, with a track record of pioneering advancements. His extensive experience has equipped him with substantial expertise and resources. In 2016, he founded Dogotix, pioneering humanoid robot research in China. By 2020, he launched a quadruped robot that quickly dominated the global market. After co-founding XPENG Robotics, Zhao formed a new team in early 2023 and created the humanoid robot PX5, which later gained significant attention at NVIDIA's GTC 2024. Following the success of PX5, Zhao left XPENG Robotics to establish EngineAI, soon securing nearly 100 million yuan (approx. USD 13.64 million) in angel funding and unveiling the next-gen humanoid robot SE01 on October 24, 2024.

The SE01 has garnered significant attention at CES 2025. As EngineAI's first full-size general-purpose humanoid robot, it marks EngineAI's commitment to the embodied intelligence sector. Designed for industrial labor scenarios, SE01 features high load capacity and can handle tasks such as heavy lifting and precision assembly in complex factory environments. It incorporates advanced harmonic force control joint modules, deep reinforcement learning, and imitation learning algorithms, along with an end-to-end neural network model. This robot has overcome the challenge of natural gait, eliminated the awkward movements of previous robots, and significantly enhanced work efficiency and precision. Standing at 170cm and weighing 55kg, SE01 can perform human-like actions such as squatting, push-ups, and running, with athletic performance comparable to international athletes.

Another highlight is the SA01, a pioneering robot designed for research and educational settings. It features an open-source platform, offering a highly customizable bipedal robot for research institutions and educational organizations. Weighing approximately 40kg, SA01 can perform actions such as running and jumping. It utilizes a reinforcement learning algorithm architecture and an efficient power module solution, with a walking power consumption of less than 200W. Constructed with high-quality, high-strength aluminum alloy, the SA01 boasts strong system rigidity and impact resistance, making it a durable choice for the research market. Priced at USD 5,400, it offers exceptional value, with orders quickly surpassing expectations. Now EngineAI has established a stable production capacity to meet the increasing market demand.

The PM01, EngineAI's latest release, is a lightweight, high-dynamic, fully open embodied intelligent robot. Standing at 138cm and weighing around 40kg, it offers both mechanical and humanoid natural gait walking modes. PM01 is the most flexible robot in EngineAI's lineup so far, with human-like movement and performance rivaling the flagship SE01. It features an interactive core screen for seamless interaction and enhanced dynamic performance with additional degrees of freedom in the neck and waist. The PM01 supports extensive hardware and software capabilities, enabling cross-platform algorithm deployment and validation, making it ideal for diverse research applications. The PM01 is now available in both commercial and educational editions. From now until March 31, 2025, both editions are offered at a price of USD 13,700. During this specific period, customers who purchase the commercial edition will automatically receive an upgrade to the educational edition.

With its debut at CES, EngineAI is poised to continue its innovation-driven approach, refining its product lineup while focusing on embodied intelligence development. The company aims to advance artificial intelligence solutions, linking and building ecosystems to serve and train professional models, ultimately contributing to the emergence of the AGI era.

www.linkedin.com/company/engineai-robot

In the AI era, Hyundai Motor Group is driving innovation through strategic AI integration, positioning itself at the forefront of smart mobility solutions. The Group operates a variety of AI initiatives and through this partnership aims to further enhance the application of intelligence to its core mobility products, such as software-defined vehicles and robotics, and across its business operations.

“Hyundai Motor Group is exploring innovative approaches with AI technologies in various fields such as robotics, autonomous driving, and smart factory,” said Heung-Soo Kim, executive vice president and head of global strategy office at Hyundai Motor Group. “This partnership is set to accelerate our progress, positioning the Group as a frontrunner in driving AI-empowered mobility innovation.”

As part of the agreement, Hyundai Motor Group will harness NVIDIA accelerated computing and AI Enterprise software to help manage the massive amounts of data required to safely develop and train its AI models for various applications.

The Group will also utilise the NVIDIA Omniverse platform to develop physical AI and digital twin applications to simulate its factories, helping improve manufacturing efficiencies and quality, and streamline costs. In addition, the Group will use the NVIDIA Isaac robot development platform to develop and safely deploy AI robots.

Both parties will also work closely to create virtual simulation environments for safe and reliable autonomous driving technology and robotics systems.

“Accelerated computing, generative AI, and Omniverse are unlocking a new era of mobility,” said Rishi Dhall, vice president of automotive at NVIDIA. “This partnership will drive the creation of safer, more intelligent vehicles, supercharge manufacturing with greater efficiency and quality, and deploy cutting-edge robotics to help build a smarter, more connected digital workplace.”

Starting with these initiatives, the partnership aims to drive the development of groundbreaking innovations going forward, with more to be announced at a later date.

The food and beverage industry is vulnerable to noise-related risks due to the sheer variety of equipment involved. Filling machines, conveyors, palletisers and refrigeration units all contribute to a constant cacophony. The same goes for the production of products like beet sugar, breakfast cereals and frozen fruits. According to the Centers for Disease Control and Prevention (CDC), around 46 per cent of manufacturing workers have been exposed to hazardous noise.  What’s more, the same report highlighted that close to 14% of manufacturing workers had some degree of hearing loss.

These hazards reflect how important it is for food manufacturers to adhere to legal obligations designed to protect employees and create safer working environments.

Legal requirements

In the UK, the Control of Noise at Work Regulations 2005 provides a legal framework for managing noise in the workplace. These regulations require employers to eliminate noise at its source wherever possible or reduce it to the lowest level reasonably practicable — referred to as “so far as is reasonably practicable” (SFAIRP).  
According to the second edition of a guide from the Health and Safety Executive, titled ‘A Recipe for Safety: Health and Safety in Food and Drink Manufacture HSG252’, page 49 defines two critical exposure thresholds.

At 80 decibels (dB), employers are required to inform workers about noise risks and offer hearing protection to those who request it. At 85 dB, stricter measures must be implemented, including controlling noise through quieter machinery or soundproofing.

If these options are not feasible, hearing protection zones must be established, clearly marked and enforced. In addition, health surveillance is also required for workers exposed to hazardous noise levels.

Quieter machinery

Manufacturing workers are exposed to several noises loud enough to cause noise-induced hearing loss (NIHL) and this Applied Sciences paper cites compressors, pumps, motors, fans, turbines, vents, steam leaks and control valves as some of the more significant sources of workplace noise.

However, modern automated machinery is often designed to minimise vibrations and reduce mechanical noise, improving both the working conditions for operators and the durability of the equipment.

A prime example is Clippard Spider valves, known for their exceptionally quiet operation with lower noise and vibration levels. These valves are especially effective in packaging lines, where pneumatic actuators perform tasks like bottle capping or product sorting.

Another example is Dematic’s global launch of its noise reduction portfolio. These products are specifically designed to address noise in environments like packaging lines and material handling systems, all of which are sources of high noise levels on food and beverage lines.

The portfolio uses 3D noise mapping to diagnose and visualise noise levels in a facility, much like how a heatmap identifies temperature hazards, providing a clear, data-driven understanding of the acoustic challenges within the space.

Dematic then recommends quieter equipment, such as rollers, slats and belt conveyors, which are designed to reduce noise levels by up to 15 dB.

Is PPE not enough?

Despite these advancements, 28% of workers in manufacturing report not wearing hearing protection in noisy environments — that’s according to the aforementioned CDC report.

This alarming statistic reiterates the importance of integrating quieter machinery and automation systems, which address noise at its source rather than relying solely on personal protective equipment (PPE).

That’s not to say PPE isn’t necessary, because it is. However, automation aligns with best practice for reducing noise exposure, especially when stricter measures are required for machinery louder than 85 dB.

As food and beverage manufacturers must focus on workforce health and safety, integrating quieter automation technologies is crucial not only for meeting regulatory standards but also for enhancing operational efficiency.

Partnering with experts like Foxmere can help ensure that your production runs smoothly, safely and in compliance.

Tom Cash is director Foxmere

foxmere.com/en 

 

ONE QUESTION on every plastics processor’s mind, in deed on the mind of many a manufacturer, as we enter this new age of connectivity and transparency is: How to get multiple technologies and periphery equipment from different sources to communicate, form a cohesive data set and offer meaningful insight into a factory’s processes?

Working with a small consortium of premium machinery and automation partners, Sumitomo (SHI) Demag UK recently delivered a factory-tested, CE marked machine that connected an IntElect injection moulding machine to all of the periphery moulding equipment, including four existing and very much functional boxing and packing systems. Then, using locally built software and hardware, successfully got everything interacting with each other through a single NC5 controller.   The project has made the finalist list in this year’s Plastics Industry Awards.

Gaining big value from big data

Data holds intrinsic value with the ability to optimise productivity and increase profitability, but only if it is collected and handled correctly. Big data, in its simplest terms, is a large, complex, proliferating data set that comes variety of sources in diverse forms.

Through the newest digital technologies, this data can be collected, processed and analysed to help manufacturers gain the greatest possible value from it. The amount and availability of data a factory can collect is accelerating rapidly, propelled by advancements in digital technology, from deep learning data analytics to cross-platform, open-source communication.

The core of Industry 4.0

Industry 4.0 introduced the term “Smart Manufacturing”, whereby Industrial Control Systems (ICS) monitor the physical processes of the factory and make decentralised decisions. These cyber physical systems become an Internet of Things, communicating and cooperating both with each other and with humans in real time.

At the very core of Industry 4.0 technology is cross-platform communication and big data analytics. In today’s modern market, plastics manufacturers need to meet higher demands for productive, energy efficient, sustainable processes, in order to achieve ambitious growth targets and meet stricter government standards.

“The key to success is total integration and seamless interactive connectivity between all devices, from injection moulding machines and robotics to temperature controllers and mould flow digital devices,” states SDUK director Nigel Flowers.

Can machines communicate?

The momentum and move towards smart manufacturing and Open Platform Communications Unified Architecture (OPC-UA) interoperability continues at pace. Facilitating scalable and extendible communication between the injection moulding machines, periphery equipment and automation aligned to the recommended standards is imperative to adopting a smart manufacturing approach.

The OPC-UA standard was created in 2006 by the OPC Foundation. This proposed a standardisation of communications between ICS machines,  integrating technology  to ensure the security of these communications. It can be implemented on most current ICS  device, running a compatible  operating system (e.g. Windows, Linux, IIOT).

OPC-UA is the communication technology which allows equipment and machines to talk to each other, it does not however define what they are going to talk about. For real information to be exchanged, there needs to be a “conversation” layer – what the equipment is going to talk to each other about and what data is going to be collected. With the help of the OPC Foundation, Euromap has created this cross- platform, universal reporting language.

Why do we need this?

“90% of the machines we install at SDUK require some form of integration with existing equipment and technologies. Many of this existing equipment is not from the same company. Plus, few factories would ever have the luxury of replacing all their legacy equipment at the same time, or with the same brand,” reports Nigel. This makes upgrading to a fully-networked value chain incredibly difficult. In fact, the cost of upgrading capital equipment is often cited as the biggest barrier to decarbonising processes and sustainability efforts, reported the MakeUK Industrial Strategy Report 2023.

For the vast majority of plastics manufacturers seeking to integrate digitalisation tools and achieve complete production line synergy, everything must be programmed to a standardised OPC-UA roadmap.

Is this feasible?

Despite the talk about smart technology solutions such as big data and OPC-UA, these systems are still in their infancy. Although many approaches and technologies have been developed, it still remains difficult to carry out machine learning with big data.

The overcomplexity and issues with enabling interoperability of ICS are only just being addressed. Few have been able to push the boundaries and mastered total integration, seamless communication, data collection and valuable analysis between devices. Furthermore, many plastics companies are struggling to capture the full transformative efforts that deliver a satisfactory return on investment.  

The value in being smart

To deliver the productivity, process and people improvements these new digital tools boast about, companies have to be strategic in their approach, urges Nigel. He believes that even the best technologies are compromised by a lack of long-term planning and reactive piecemeal purchases. “Urgency very often takes precedent over strategy. Piecemeal investments may get you by today, but it rarely accounts for the future.”

When digital transformations are scaled across the entire value chain, the gains can fundamentally transform a plastics processor’s competitive position. From increased production capacity to reduced environmental impact, unparalleled OEE, to higher employee satisfaction, fully leveraging the newest digital technologies can lead to a 50% reduction in machine downtime, a 30% increase in throughput and an 85% improvement in forecasting accuracy.  

www.sumitomo-shi-demag.uk