As traditional manufacturing wrestles with global supply chain disruptions, soon to be enforced tariffs, shifting consumer demands, and relentless calls for customisation, London-based subcontract 3D printing bureau 3D People is claiming that low-volume production on-demand is no longer a limitation — it’s a strategic advantage.
Today, 3D People announces plans to redouble its efforts to cater for low-volume production demands for its customers, a demand that is by no means new, but which reflects the increased orders for fast, flexible, and functional AM parts in batches that traditional manufacturing simply can’t handle efficiently.
With additive manufacturing (AM) increasingly proving its mettle beyond prototyping, 3D People is helping customers break away from costly tooling, rigid MOQs, and sluggish production cycles.
“Companies are waking up to the fact that tooling costs, minimum order quantities, and inflexible design cycles are killing their ability to adapt,” says Sasha Bruml, Co-Founder of 3D People. “That’s where AM shines. Low-volume production on-demand is no longer a compromise — it’s a competitive edge.”
Across a variety of industries and application areas, demand is rising for high-quality, functional end-use parts in small batches — with short lead times and the freedom to iterate. For many, the ability to produce 50 or 500 precision parts without tooling is reshaping how they launch products and serve customers.
“We’ve been running hundreds of low-volume projects across SLS and MJF for years,” says Felix Manley, Co-Founder of 3D People. “Today, however, the demand is becoming unprecedented. It’s not just about cost — it’s about speed, flexibility, and cutting out waste. Customers are rethinking manufacturing from the ground up, and additive is giving them a whole new playbook.”
Far from a fallback option, 3D People positions AM as the go-to solution for fast-moving, design-led production where performance, aesthetics, and agility matter more than volume.
With shorter product cycles and tighter feedback loops becoming the norm, the companies thriving in 2025 are the ones that can make parts on-demand, pivot without penalties, and go to market faster than ever.
“This isn’t about hype — it’s about hard truths,” adds Bruml. “The old rules don’t apply anymore. Agile, low-volume manufacturing is here, and it’s levelling the playing field for innovators everywhere.”
3D People’s continued focus on streamlining its low-volume production service is a direct response to this industry shift — a bold, practical step that redefines what smart manufacturing can look like today.
Apple says it has reached a major manufacturing milestone, announcing that all Apple Watch Ultra 3 and titanium Apple Watch Series 11 cases produced this year are made using 3D-printed enclosures created from 100 per cent recycled aerospace-grade titanium powder.
In a news release, the company said the shift marks the first time this approach has been achieved at scale for its products.
“It wasn’t just an idea — it was an idea that wanted to become a reality,” said Kate Bergeron, Apple’s vice president of Product Design. “We had to prove, with continuous prototyping, process optimisation, and a tremendous amount of data gathering, that this technology was capable of meeting the high standard of quality we demand.”
Apple said the new additive manufacturing process allows the watches to be produced with roughly half the raw material previously required. According to Sarah Chandler, Apple’s vice president of Environment and Supply Chain Innovation, the reduction represents a significant step toward the company’s Apple 2030 goal to eliminate carbon emissions across its entire footprint.
“A 50 per cent drop is a massive achievement — you’re getting two watches out of the same amount of material used for one,” Chandler said. “When you start mapping that back, the savings to the planet are tremendous.”
The company estimates that more than 400 metric tons of raw titanium will be saved in 2025 through the new process.
Apple said the technology also enables improvements in durability, performance and component design, including new textures that were not possible with traditional forging.
Dr J Manjunathaiah, senior director of Manufacturing Design for Apple Watch and Vision, said the transition reflects long-running work to test 3D-printed metal for cosmetic parts.
“Using less material to make our products has always been the intention,” he said. “Previously, we hadn’t been able to make cosmetic parts at scale with 3D printing.”
Apple’s environmental and design teams said the project required advances in materials science, reliability testing and precision manufacturing, including tightly controlled powder composition and laser-based printing conducted over hundreds of layers. “This was cutting-edge materials science,” Bergeron said.
Chandler said the adoption of 3D printing is intended to create permanent, system-wide improvements. “We’re never doing something just to do it once — we’re doing it so it becomes the way the whole system then works,” she said. “When we come together to innovate without compromise across design, manufacturing, and our environmental goals, the benefits are exponentially greater.”
Apple added that the breakthrough has also enabled new manufacturing possibilities beyond Apple Watch, including the titanium USB-C port enclosure on the new iPhone Air, which uses the same recycled titanium powder and 3D-printing process.
A new study from Mecalux and the MIT Intelligent Logistics Systems Lab at the MIT Center for Transportation and Logistics reports that artificial intelligence is now embedded in a majority of warehouses worldwide, signalling a rapid shift toward intelligent logistics operations.
The study, conducted by Mecaluxin partnership with the MIT Intelligent Logistics Systems Lab, draws on responses from more than 2,000 supply chain and warehousing professionals across 21 countries.
According to the report, more than nine in 10 warehouses now use some form of AI or advanced automation.
Over half of surveyed organisations reported operating at advanced or fully automated maturity levels, with larger businesses leading adoption.
The findings state that AI is now supporting a wide range of day-to-day warehouse functions, including order picking, inventory optimisation, labour planning, equipment maintenance and safety monitoring.
Mecalux CEO Javier Carrillo said the research shows clear performance benefits linked to intelligent systems.
“The data show that intelligent warehouses outperform not only in volume and accuracy, but in adaptability,” Carrillo said in the release.
He noted that companies with AI-enabled operations were demonstrating stronger resilience and predictability ahead of peak-season demand.
The report states that AI investments are delivering quicker returns than expected, with most organisations allocating between 11 and 30 per cent of their warehouse technology budgets to AI and machine-learning initiatives.
The study reports typical payback periods of two to three years, driven by gains in labour efficiency, inventory accuracy, throughput and error reduction. Motivations for AI adoption include cost savings, customer expectations, sustainability goals, labour shortages and competitive pressures.
However, the release notes that businesses continue to face challenges when scaling AI across their operations.
Dr Matthias Winkenbach, Director of the MIT ILS Lab, said the “last mile” of integration remains difficult as companies work to align people, data and analytics with existing systems.
Barriers identified in the study include limited technical expertise, system-integration issues, data-quality constraints and implementation costs. Even so, organisations reported strong foundations in data and project management and pointed to clearer roadmaps, expanded budgets and improved tools as key enablers for continued adoption.
The joint release also addresses ongoing concerns about automation replacing human workers, reporting that AI is contributing to workforce expansion rather than reduction.
More than three-quarters of respondents saw increases in employee productivity and satisfaction after implementing AI tools, and over half reported growing their workforce.
Emerging roles cited in the study include AI and machine-learning engineers, automation specialists, data scientists and process-improvement experts.
Looking ahead, the release states that nearly all surveyed companies plan to expand their use of AI within the next two to three years.
Eighty-seven per cent expect to increase their AI budgets, and 92 per cent are implementing or planning new AI projects.
Generative AI is identified as the next major area of value, supporting tasks such as automated documentation, warehouse-layout optimisation and code generation for automation systems.
Dr Winkenbach said generative AI is increasingly helping companies design solutions rather than simply predict problems.
As industries strive for peak efficiency and reliability, intelligent motor starting solutions are becoming the go-to choice for safeguarding critical machinery and ensuring flawless performance.
From processing plants to general manufacturing, these technologies are revolutionising motor control, bridging the gap between Information Technology (IT) and Operational Technology (OT), and driving digital transformation.
APS Industrial, in collaboration with Siemens, has launched state-of-the-art intelligent motor starting solutions designed specifically for fixed-speed applications in Australian industries.
With three offerings—SIMOCODE, SIRIUS 3RC7, and SIRIUS eStarter—businesses can choose the ideal technology to optimise productivity, reduce downtime, and enhance operational safety, according to a media release from APS Industrial.
The SIMOCODE motor management system is a comprehensive solution for processing applications requiring robust performance and advanced functionality.
Suitable for all motor sizes, it provides intelligent motor protection, in-depth diagnostics, and seamless programming via Siemens’ TIA Portal.
These features enable predictive maintenance and reduce operational risks, making SIMOCODE an indispensable choice for continuous, high-stakes operations.
For original equipment manufacturers in industries such as food, beverage, and general manufacturing, the SIRIUS 3RC7 Intelligent Link Module offers a tailored motor starting option for motors up to 15kW.
This solution integrates seamlessly with Siemens Contactors and Circuit Breakers, forming a complete Intelligent Load Feeder while providing reliable motor protection, straightforward automation system connectivity, and enhanced operational transparency.
For those seeking a fully electronic, future-ready option, the SIRIUS eStarter could be an ideal choice for motors up to 3kW.
Using advanced semiconductor technology, it offers wear-free operation and ultra-fast short-circuit protection, responding up to 1,000 times faster than traditional methods.
Designed for high-frequency start-stop cycles, APS Industrial said it minimises stress on electrical systems with Smart Start technology, aligning with Siemens’ EcoTech standards for sustainable performance.
By offering these intelligent motor starting solutions, APS Industrial and Siemens aim to provide Australian businesses with the flexibility to address diverse operational needs, ensuring enhanced efficiency, reliability, and sustainability.
For further details on these solutions, visit APS Industrial’s website.
The content of this article is based on information supplied by APS Industrial. Please consult a licence and/or registered professional in this area before making any decisions based on the content of this article.
As digital transformation reshapes industries worldwide, manufacturers in Australia and beyond are finding themselves at the forefront of an evolution. This shift presents opportunities for companies looking to improve efficiency, drive innovation, and boost growth. However, while the potential benefits are substantial, many manufacturers are finding digital adoption more complex than anticipated.
According to a recent paper by The Futurum Group’s research director, Keith Kirkpatrick, in partnership with SAP, industrial manufacturers in the past often focused on specific technologies and certain areas instead of approaching digital transformation in a holistic fashion. This approach has led to inefficiencies and limited scalability that hinder organisations’ ability to adapt and evolve.
Kirkpatrick emphasised that now is the time for a “full-on mental shift” on how the industry gets things done — starting with identifying and addressing the challenges in industrial manufacturing’sdigital transformation.
Key transformational trials
As industrial manufacturers navigate the digital transformation landscape, they encounter a series of complex challenges that require strategic foresight and adaptability. These challenges impact various facets of the manufacturing process and business operations. These include:
Shifting customer demands: Modern consumers seek personalised solutions and flexible purchasing options, pushing manufacturers to innovate their business models and offer tailored solutions.
Need for new revenue streams: With shortening product lifecycles, manufacturers must pivot to operational expenditures over capital expenditures, aligning with trends towards subscription-based services.
Global market differentiation: As geographical boundaries blur, manufacturers must distinguish themselves through innovation and service excellence, focusing on delivering outcomes rather than just products.
Rapid technological evolution: The integration of automation, AI, and machine learning enhances productivity but demands significant resources and a cultural shift within organizations.
Supply chain disruptions: Global events and geopolitical tensions exacerbate material shortages, transportation delays, and labour shortages, requiring manufacturers to be agile and responsive.
Sustainability focus: Regulatory pressures and consumer expectations drive manufacturers to implement sustainability initiatives, addressing emissions, waste management, and circular economy practices.
By tackling these issues head-on, manufacturers can position themselves for sustained growth and success in an increasingly competitive landscape.
From survival to growth
For industrial manufacturing leaders, embracing digital transformation is no longer optional; it is a necessity for survival and growth in an ever-changing landscape. With this in mind, Futurum says organisations must begin developing comprehensive strategies that align with their business and leverage advanced technologies to stay competitive.
SAP offers a robust Digital Manufacturing solution, which is underpinned by SAP Cloud ERP, providing a scalable and secure foundation for innovation. This platform supports core processes and enables manufacturers to become intelligent, sustainable enterprises.
To explore how SAP can help drive your organisation’s digital transformation, visit sap.com.
To further explore how AI and smart ERP systems are reshaping the manufacturing landscape, SAP, in partnership with Australian Manufacturing, recently hosted a webinar titled “Unleashing the Power of Smart ERP: How AI is Redefining Manufacturing in Australia.”
The session explored how AI and ERP technologies are helping manufacturers drive growth and remain competitive. Access the full webinar recordinghere.
Enterprise Resource Planning (ERP) systems have long been essential for managing critical business functions—finance, inventory, procurement, human resources, and manufacturing. With rapid advancements in technology, artificial intelligence (AI) is reimagining what’s possible.
ERP systems are much more than repositories of data and business processes. With AI in the picture, ERP evolves into a smart assistant—proactively guiding your team, identifying patterns, flagging issues, and helping you stay two steps ahead of the competition, reducing supply chain issues and optimising customer service.
If you’re still running on a traditional ERP solution, it’s time to explore how AI can take your business to the next level.
What we’ll cover:
How is AI impacting ERP systems?
The role of AI in data and business intelligence
Current use cases for AI in manufacturing
AI-enhanced user experience in ERP
Leading AI-enabled ERP solution
How to get started
How is AI impacting ERP systems?
AI-enabled ERP solutions allow team members to make smarter decisions faster by using AI learnings from real-time and historical data. ERP solutions hold enormous data libraries across customer, supplier, inventory and order management. Using AI learnings will lead to better outcomes across multiple functions of a business—from finance and procurement to manufacturing and supply chain management.
Automates repetitive tasks: AI and machine learning automate labour-intensive work like sales order creation, invoice matching, order processing, bank reconciliations and compliance checks, freeing your team for strategic, value add work. As an example, businesses with high volumes of sales orders that are manually captured from a customer order might consider Robotic Process Automation to create the order automatically in the ERP system. Once the order is automatically created, the AI bot can review the order and learn from previous orders/decision-making. For example, the AI Bot might run several checks to suggest next steps:
Customer credit check
Stock availability check
Available to promise overview to see when stock will be available to ship to the customer
Suggested email output to notify the customer of the order scheduled delivery
Improves data quality and integration: AI continuously scans for anomalies, missing fields, or inconsistencies, validating and correcting data automatically to ensure clean, reliable inputs across all departments. It also integrates data from emails, PDFs, spreadsheets, and third-party systems—eliminating manual data input and reducing errors.
Provides predictive insights: AI analyses trends from historical and real-time data to forecast demand, plan inventory, and anticipate maintenance needs—helping you prepare proactively. Use natural language to write reports, use AI to make recommendations about data analytics and recommendations for further data insight to business trends.
Enables conversational interaction: Natural Language Processing (NLP) lets users query the ERP systems in plain language, making reporting and insights instantly accessible.
Delivers contextual, actionable reporting: Beyond numbers, AI highlights drivers of profitability, emerging risks, and supplier performance to support informed decisions.
AI’s impact on data handling and business intelligence
AI enhances ERP systems by transforming how data is processed, analysed, and acted upon. Rather than simply reporting on past performance, AI uncovers patterns, identifies root causes, and delivers predictive insights that support faster, smarter decisions. It adds depth to business intelligence—connecting the dots between data points to show not just what happened, but why it happened and what’s likely to happen next. This frees up team members’ time for customer interaction and value add processes.
AI automatically cleans, prepares, and integrates data for you—reducing time spent wrangling spreadsheets.
Pattern recognition and anomaly detection highlight unexpected shifts in performance.
Proactive alerts let you know when action is needed, before problems escalate.
With AI-driven BI, companies move from reactive to proactive—shifting from hindsight to foresight.
Current use cases for AI in manufacturing
AI is transforming manufacturingacross multiple areas by automating routine tasks, improving decision-making, and boosting productivity. Key use cases include:
Enterprise Search & Data Access: Natural Language Processing (NLP) enables users to ask plain-language questions like “What are my open sales orders?” and instantly retrieve answers from across ERP systems, documents, and databases. The AI understands context, not just keywords, to return the most relevant results—reducing search time by up to 50% and accelerating decisions.
Demand and Inventory Planning: Advanced machine learning models analyse historical sales, seasonal trends, supplier data, and external factors like weather or market shifts to generate dynamic demand forecasts. The AI also detects and corrects anomalies in data automatically, resulting in more reliable planning and significantly less manual intervention.
Inventory Management: AI simplifies inventory setup and data cleansing by extracting, organising, and validating data from sources such as spreadsheets, PDFs, product spec sheets, and diagrams. By automating classification and reducing duplication, it shortens onboarding time by up to 75% and cuts inventory creation effort by 80%.
Sales and Order Automation: AI uses historical patterns to auto-complete missing fields in sales orders—like pricing, delivery terms, or product codes—ensuring accuracy even with incomplete customer input. Intelligent recommendation engines analyse past buying behaviour and unstructured customer requests to suggest optimal product configurations, reducing quoting costs by 20% and speeding up sales cycles.
Invoice Processing: AI-powered Optical Character Recognition (OCR) extracts data from invoices—whether scanned, PDF, or digital—and validates it against purchase orders and goods receipts. By automating capture, matching, and approvals, it minimises manual data entry, reduces errors, and accelerates accounts payable workflows.
Production Engineering & Maintenance: AI analyses equipment logs, past error patterns, and sensor data to help engineers troubleshoot faults faster. It generates automated task lists and recommends solutions based on similar historical issues, improving engineering productivity by 25%, helping maintenance teams plan better, and reducing unplanned downtime by 1%.
Financial Operations: AI transforms natural language inputs—such as “Upload journal entries for Q4 accruals”—into validated journal entries using embedded business rules. It automates generation, validation, and posting processes, cutting up to 85% of the manual effort involved in period-end closing and improving data accuracy.
Customer Service & Support: AI extracts critical information—like case details, product names, and issue types—from unstructured communications such as emails and chat messages. It structures this data for faster case handling, suggests next best actions, and drafts responses for agents, increasing service productivity by 50% and reducing repeat issues by 30%.
These examples represent only a fraction of AI’s capabilities within ERP systems for manufacturing—an area that continues to rapidly evolve and unlock new efficiencies.
How AI makes ERP more user-friendly? AI tools create intelligent interfaces, personalised dashboards, and built-in assistance so that ERP solutions offer a more personalised user experience.
Virtual agents AI-powered ERP systems offer bots that can answer questions, automate repetitive tasks, and assist users without waiting on IT support.
Adaptive interfaces AI can learn how different users work—offering shortcuts, insights, and data that match their roles and preferences.
Document summarisation & report generation Need to send a financial update or executive summary? AI can generate a polished report in seconds, tailored to the audience, with an email overview and summary that helps the audience focus on key data.
Embedded co-pilots AI can sit beside your team, suggesting the next steps, flagging unusual transactions, or even writing draft emails or reports.
Coding AI will enable coding – creating apps, reports, user specific training and user manuals. With limited technical experience, users will be able to achieve technical tasks that would previously have required IT skillset.
AI in SAP S/4HANA Public Cloud
SAP S/4HANA Public Cloud is leading the evolution of ERP systems with embedded artificial intelligence and machine learning capabilities. These smart technologies enable automation across core business functions—streamlining processes like demand forecasting, invoice matching, and detecting anomalies in financial data. By minimising manual tasks and enabling faster, more informed decision-making, SAP S/4HANA Public Cloud empowers manufacturers to operate more efficiently and proactively.
Time to act on AI ERP solutions
AI ERP systems are no longer a future vision—it’s here, and it’s rapidly becoming the standard.
Combining the structured power of ERP and the data sets available within ERP with the intelligence of AI enables faster decisions, fewer errors, greater efficiency, and a better user experience.
To stay competitive, businesses should consider:
Review their current ERP setup to assess compatibility with AI technologies
Engage with ERP providers that support AI-enabled tools and capabilities
Equip teams with the skills to interpret AI-driven insights and work alongside intelligent systems
Done right, AI ERP systems don’t just help you run your business. It helps you transform it.
Unlock the power of AI in ERP with Leverage Technologies
AtLeverage Technologies, we help Australian manufacturers harness the full potential of Artificial Intelligence within ERP systems. From intelligent automation to predictive analytics and conversational interfaces, we guide you through real-world applications of AI that drive efficiency, accuracy, and smarter decision-making.
Want to explore how AI can elevate your ERP and transform your operations? Speak to an expert at Leverage Technologies today.
The content of this article is based on information supplied by Leverage Technologies. This information is general in nature and has been prepared without taking your personal/ professional/business objectives, circumstances and needs into account. You should consider the appropriateness of the information to your own circumstances and, if necessary, seek appropriate professional advice. Consider the terms and conditions for the product before making any decision.
La Trobe University, in partnership with Cisco and Optus, has opened its Digital Innovation Hub to boost R&D in sectors such as healthcare, education, advanced manufacturing, and smart urban infrastructure, fostering greater collaboration among students, researchers, businesses, and industry.
The hub, which leverages Optus’ 5G connectivity, is the first of its kind in Australia to host Cisco’s Webex Hologram technology, providing a platform for enterprises, students, start-ups, and Victorian businesses to collaborate, innovate, and transform.
Victorian Minister for Skills and TAFE Gayle Tierney said Victoria is an innovation powerhouse, and its universities, as international leaders in research and development, play a critical role in the State’s success.
“Facilities like La Trobe University’s Digital Innovation Hub give students a chance to test and improve their skills while helping businesses to tackle real-world challenges – this means everyone involved is transforming and growing”, Minister Tierney stated.
La Trobe Vice-Chancellor John Dewar AO said the purpose-built centre confirms the University’s leadership in tech innovations relevant to global industries.
“The Digital Innovation Hub builds on the University’s deep connection to business, creating a catalyst for new research and commercial developments that could transform sectors like healthcare, education, and telecommunications. It offers real-time opportunities for our students, academics and business to collaborate and innovate,” Professor Dewar added.
Cisco’s vice president for Australia & New Zealand Ben Dawson commented, “The Digital Innovation Hub in Melbourne brings together a range of partners, projects and precincts to offer students with access to the industry right at their doorstep. That’s why we’ve chosen it as the home of Innovation Central Melbourne – a facility that brings tech leaders together with industry and academia to address the skills gap and solve real world challenges. The Digital Innovation Hub offers the next wave of technologists with the latest tech and connections at their fingertips to turbocharge innovation.”
According to Optus Head of Government, Enterprise and Business Kavin Arnasalon, Optus is investing in Australia’s universities to become leading research institutions, utilising its fast and reliable 5G connectivity to enable students and researchers to explore the potential of AI, virtual and augmented reality, and ultra-reliable low latency.
“Through our strategic partnership with La Trobe University and Cisco, Optus looks forward to supporting students, researchers and the next generation of business leaders to have hands on experience in integrating technology into the workplace to drive business transformation,” Arnasalon added.
Meanwhile, Cisco Innovation Central Melbourne Director Jeffrey Jones emphasises the importance of social interaction in fostering innovation. He believes that innovation occurs when people are in the same place simultaneously, and the new Digital Innovation Hub will facilitate these interactions.
The hub’s other functions include:
Augmented reality experiences, including through Cisco’s Webex hologram: Various smart glasses are being tested for various healthcare, education, and agriculture applications.
Interactive touch wall: a cutting-edge presentation where guests can interact with interactive multimedia displays to learn about various research projects.
Optus 5G co-design showcase: experimentation and prototyping in fields such as IoT (Internet of Things), augmented reality (AR), and virtual reality (VR).
National Industry Innovation Network showcase: Featuring interactive maps, case studies, success stories, and a database of member organisations from Cisco’s ‘National Industry Innovation Network,’ highlighting their creative projects, technology, and contributions to their respective industries across Australia.
The Digital Innovation Hub houses three top digital labs: Cisco & La Trobe’s AI and IoT Centre, Innovation Central Melbourne, and Optus 5G Lab.
A schematic diagram of Safeghi Goughari's experimental setup. Image credit: University of Waterloo
University of Waterloo engineers have developed a new imaging system equipped with artificial intelligence (AI) to advance cancer treatment monitoring.
According to the university, the new imaging system will enable high-intensity, concentrated ultrasound to eradicate a broad spectrum of cancerous, often fatal, tumours more safely and effectively.
“We are addressing a huge challenge for focused ultrasound treatment,” explained project leader Moslem Sadeghi Goughari, a research associate in the university’s Department of Mechanical and Mechatronics Engineering. “Our imaging system can tell doctors exactly how much of a cancerous tissue is destroyed. And it’s the first AI-powered ultrasound technique developed for focused ultrasound treatment.”
Focused ultrasound treatment, used since the 1970s to treat prostate, breast, and liver cancers without incisions, uses high-frequency sound waves to heat and kill cancer cells, effectively eliminating the need for surgery.
However, while targeted ultrasound treatment can be performed as day surgery and provides for a faster, more pain-free patient recovery, it has significant limitations that restrict its widespread use. Because it is difficult to manage precisely, the treatment may mistakenly damage healthy tissue around a tumour or leave part of a tumour untreated, allowing cancer to spread.
Sadeghi Goughari and his three engineering colleagues believe that accurate monitoring of this therapeutic treatment, while it occurs, is the key to resolving these issues. They began creating their own system because there was no effective solution available.
Researchers used a focused ultrasound transducer and ultrasound imaging probe to deliver ultrasound energy to a targeted area, capturing images. Aligning the probe and transducer was crucial for proper monitoring, and a robotic arm was used to ensure alignment. This allowed for precise images even as the transducer moved during treatment, ensuring accurate monitoring of the tiny area being ablated.
An AI framework has been developed for software integration with ultrasound procedures. It can quickly compare ultrasound images before and after treatment, handling 45 frames per second. This system can detect treatment area changes in less than 22 milliseconds, allowing real-time monitoring of focused ultrasound treatment.
The system, which has a 93% accuracy rate, can detect the extent of a tumour’s destruction, allowing doctors to accurately measure the ablated area margin within micrometres. This could aid in better controlling focused ultrasound treatment, ensuring tumour destruction without causing damage to healthy tissue.
The team plans to enhance their research by expanding their method and real-time monitoring of the growth of a treated area during treatment.
Researchers have proven the possible use of multimode optical fibre to increase power in fibre lasers by three to nine times without compromising beam quality, allowing them to focus on distant targets.
UniSA Future Industries Institute researcher and research co-author Dr Linh Nguyen suggests the new approach to extract high power from fibre lasers, making them more useful for defence, remote sensing, and gravitational wave detection.
“High-power fibre lasers are vital in manufacturing and defence, and becoming more so with the proliferation of cheap, unmanned aerial vehicles (drones) in modern battlefields,” Dr Nguyen said.
“A swarm of cheap drones can quickly drain the missile resource, leaving military assets and vehicles with depleted firing power for more combat-critical missions. High-power fibre lasers, with their extremely low-cost-per-shot and speed of light action, are the only feasible defence solution in the long run.”
Dr Nguyen noted that this is known as an asymmetric advantage: by playing the large number, a less expensive approach can defeat a more expensive, high-tech system.
The advanced capability offers a strong deterrent effect, aligning with Defence Strategic Review and AUKUS Pillar 2 objectives through an asymmetric advantage.
Dr Ori Henderson-Sapir, project investigator at the UoA’s Institute for Photonics and Advanced Sensing, highlights Australia’s long history in developing innovative fibre optics technologies.
“Our research launches Australia into a world-leading position to develop the next generation of high-power fibre lasers, not only for defence applications, but to aid new scientific discoveries.”
The method has been demonstrated in fibre lasers, and the researchers will present their findings at Photonics West, the premier international conference on photonics technology, in early 2024.
Researchers from Monash University’s Faculty of Information Technology and CSIRO’s Data61 have developed the most efficient quantum-secure cryptography algorithm, ‘LaV’, to improve end-to-end encryption security, with potential applications in instant messaging services, data privacy, cryptocurrency, and blockchain systems.
End-to-end encryption is a method to secure digital communication between sender and receiver, ensuring no one, including communication system providers, telecom providers, internet providers, or hackers, can access the transmitted information between the sender and receiver.
According to Monash University, a large-scale quantum computer could break current encryption within minutes, allowing it to access encrypted information more easily than a normal computer or supercomputer, which would take millions of years to do so.
Dr Muhammed Esgin, lead researcher of the collaborative quantum security project co-funded by Monash University and CSIRO’s Data61, said the new cryptography tool will help make end-to-end encryption more secure, allowing online services to withstand future hacks or interference from the most powerful quantum computers.
“While end-to-end encryption protocols are quite well established and are used to secure data and messaging in some of the most popular instant messaging applications across the world, currently they are still vulnerable to more sophisticated attacks by quantum computers,” Dr Esgin said.
“This new cryptographic tool can be applied to various mobile applications and online transactions that use end-to-end encryption and is the first practical algorithm that can be used to fortify existing systems against quantum computers.”
Associate Professor Ron Steinfeld, research co-author and a quantum-safe cryptography expert, stated that current technology software is not being developed in anticipation of the emergence of more powerful computing devices.
“Over the past few years we have seen many significant cyberattacks and data leaks in Australia alone, clearly showing that we need to pay much more attention to cybersecurity and mitigate vulnerabilities in our systems before such vulnerabilities are exploited by attackers,” Associate Professor Steinfeld explained.
Associate Professor Steinfield said governments and Standards organisations worldwide are preparing for the potential emergence of large-scale quantum computers, which could potentially compromise encryption systems.
“Our past experience has shown the process of updating encryption algorithms deployed in existing online systems can also take a decade or more to complete. This means that we need to urgently start updating our cybersecurity infrastructure to use quantum-safe cryptography, to ensure our systems are protected before the approaching quantum threat is realised,” Associate Professor Steinfeld added.
The research, conducted in collaboration with Dr Dongxi Liu and Dr Sushmita Ruj from CSIRO’s Data61, was presented at Crypto 2023, the 43rd International Cryptology Conference, held in Santa Barbara, USA.
“The National Institute of Standards and Technology has been standardising methods like encryption and digital signatures to protect basic internet security in a post-quantum world. However, these measures are not enough to protect advanced security applications. Our research is filling this gap,” Dr Liu said.
“Our new algorithm has been implemented into code by Dr Raymond Zhao from CSIRO’s Data61 and is available open source.”
As the next step, the research team is advancing to develop a fully quantum-secure key transparency protocol that can be easily integrated into encryption applications.
