This video is brought to you by the Italian Trade Agency via its Machines Italia project.

Ask any engineering professional, or even a person on the street about Italy, and you’ll hear similar answers: history, high-fashion and technology, particularly automotive in aviation technology, will be the inevitable response. In manufacturing, Italy has long been known as a source for advanced production equipment in sectors such as machine tools, injection molding and automation.

A recent innovation in die casting which has attracted global attention in the electric car industry, was developed in Italy by IDRA, based in Travagliato, near Brescia. That large format innovation appears to be spreading across the automotive industry, fundamentally changing the way light vehicles are designed and built.

Engineering.com’s Jim Anderton discussed this technology and Italian engineering in general by IDRA Group’s General Manager & CEO John Stokes. 

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Learn more about Italian manufacturing technology by downloading the latest edition of Machines Italia magazine – Volume XVII from our sister site, Design World.

In an era of economic volatility and industry disruption, North American manufacturers are turning to partners who can help them adapt, automate, and grow. This issue of Machines Italia spotlights how Italian machine builders are meeting that call, delivering flexible, sustainable, and highly automated solutions that empower end users to navigate today’s toughest challenges.

And, visit Machines Italia to learn more about the Italian manufacturing sectors and how to connect North American buyers to Italy’s manufacturers.

The post Large format die casting showcases advanced Italian technology appeared first on Engineering.com.

The deal is an all-cash transaction of $350 million, subject to closing adjustments.

“From its earliest days, Misumi’s culture of ingenuity and innovation has made us a leader and pioneer in making it easier for customers to procure manufacturing components that fit their needs,” said Ryusei Ono, representative Director and president of Misumi Group Inc.

Misumi says the acquisition will accelerate the development of its manufacturing and supply chain solutions that “deploy the power of AI through digitally native tools built on top of robust physical infrastructure to build real-world products in an efficient, scalable way.”

“Fictiv and Misumi share a joint vision to make world-class manufacturing and supply chain capabilities easier, more accessible, more intelligent, and democratized. Giving more teams the tools to take their ideas from concept to reality will unlock innovation to fuel the advancements we want to see in the world,” says Dave Evans, Fictiv Co-Founder and CEO.

Fictiv’s Global Manufacturing Supply Chain brings together AI-driven technology workflows, a global manufacturing network, and teams of local manufacturing experts to simplify sourcing for custom mechanical components. Fictiv says customer adoption of its solution has grown at a rapid pace, accelerated further by the recent launch of production manufacturing solutions.

“This acquisition will enable us to turbocharge our mission to simplify sourcing. Fictiv and Misumi bring together digital and physical infrastructure to support a full bill-of-materials, and reduce the time, cost, and risk associated with building and scaling products,” said Nate Evans, Co-Founder and CXO for Fictiv.

Misumi supplies mechanical components, tools, consumables, and other products to more than 318,000 companies worldwide. Combining with Fictiv will help make their solution even better and more scalable, benefitting customers by offering a single global platform capable of building an entire bill-of-materials with world-class speed, price, and quality.

The deal is subject to customary closing conditions including regulatory approvals.

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By now, you probably already know that digital transformation is a manufacturing strategy that integrates advanced digital technologies to enhance efficiency, reduce waste and optimize resource use. But one angle that’s not always talked about is that the right application of these digital technologies can significantly improve sustainability.

Below is an examination into key areas where digital transformation drives environmental and operational benefits. It includes foundational steps for beginners and a few advanced techniques for more experienced engineers further down the digital transformation road.

Data-driven decision-making for sustainable operations         

At the foundation of digital transformation is data collection and analytics, which enable real-time tracking of key sustainability metrics such as energy consumption, material waste and emissions. IoT (Internet of Things) sensors, SCADA (Supervisory Control and Data Acquisition) systems and AI-driven analytics help manufacturers make informed decisions that optimize production efficiency while minimizing waste.

Beginners can start with IoT-enabled sensors to monitor machine performance, energy usage and material waste. From there, use basic dashboards to visualize trends and identify inefficiencies. The next step is to implement predictive maintenance using these insights to reduce unexpected breakdowns and extend machine lifespan.

Advanced users may have already deployed digital twins to create a virtual model of production systems, allowing their engineers to test optimizations before making real-world changes. AI-powered anomaly detection can automatically adjust machine parameters and reduce energy waste while integrating machine learning (ML) algorithms to analyze historical data to improve production scheduling and minimize resource-intensive downtime.

Energy efficiency and carbon footprint reduction

Manufacturing facilities are obviously energy-intensive, but energy management systems can significantly lower power consumption and carbon emissions without disrupting production.

For beginners, smart meters and IoT sensors can track energy consumption at different production stages. Once you have this data, implement automated power-down schedules for non-essential equipment during off-peak hours.

More advanced users can integrate AI-driven load balancing to redistribute energy usage across equipment dynamically. They may decide to explore microgrid solutions that combine renewable energy sources (solar, wind) with energy storage for more sustainable operations. Carbon footprint tracking software will make it easier comply with environmental, social and governance (ESG) standards and improve sustainability reporting.

Optimization for sustainable sourcing and logistics

A sustainable supply chain reduces emissions, optimizes material use and ensures responsible sourcing throughout a manufacturer’s network. Digital tools help companies improve inventory management, optimize transport routes and reduce overproduction.

If you haven’t already, implement cloud-based inventory management systems to track raw materials, reducing excess stock and waste. PLM and ERP software are the gold standard for this, but smaller manufacturers may not need all of the functionality these platforms provide and might decide to piece together the functionality they want using smaller software platforms that require less investment and cause less disruption during start-up. The goal is to gather enough data to use demand forecasting to avoid overproduction and prevent obsolete inventory or costly overstock. Next, implement a supply chain platform to ensure ethical sourcing and reduce supplier-related inefficiencies.

Advanced users are likely at least considering AI-driven dynamic routing systems for delivery fleets, optimizing transportation routes to reducing fuel consumption. RFID and GPS tracking will monitor product movement and optimize storage conditions, reducing spoilage. Next, establish closed-loop supply chains, where returned or defective materials are reintegrated into production rather than wasted.

Waste reduction and circularity

Everyone knows minimizing waste is critical for sustainable manufacturing. Advanced digital tools help manufacturers keep a handle on waste by tracking, sorting and helping repurpose materials efficiently.

Start with robust defect detection to reduce waste caused by faulty production runs. Introducing 3D printing (additive manufacturing) to minimize material waste and create precise, on-demand parts could make sense for a growing number of manufacturers. A basic data-fuelled recycling programs for metal, plastic and other byproducts can keep waste under control.

For advanced users, AI-powered sorting systems to automatically separate and classify waste materials for recycling can improve results of any recycling program. Digital product lifecycle tracking will accommodate customer product returns for disassembly and reuse, potentially taking the edge of raw material costs as digital and smart advanced remanufacturing strategies will help refurbish returned components and reintroduce them into production lines.

Smart manufacturing for sustainable production

Industry 4.0 technologies like automation, robotics, cloud computing and AR (augmented reality) can significantly reduce resource waste and improve efficiency in manufacturing environments.

Beginners can start by implementing basic robotics for repetitive tasks to improve precision and reduce material waste. Cloud-based collaboration tools will reduce paperwork and streamline production planning. Adopting AR-based training modules allows employees to learn new skills without exhausting physical materials.

Advanced users might look to deploy AI-powered collaborative robots (cobots) to enhance precision manufacturing and minimize errors, all while collecting valuable data. Edge computing from devices on the line analyzes machine data locally (rather than in the cloud). This reduces energy consumption for data processing and gives the impetus to implement real-time digital simulation models that predict potential disruptions and adjust production accordingly.

Key takeaways for manufacturing engineers

For those just starting, begin by implementing IoT sensors, analytics and basic automation to monitor and improve sustainability.

For experienced engineers: Use advanced AI, blockchain and digital twins to optimize energy, supply chains and circularity.

Whether you are just starting your digital journey or are an advanced user of the latest digital technologies, it’s important to understand efficiency is just one piece of the payback delivered by digital transformation—it’s about future-proofing operations which includes reducing environmental impact.

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The property data ecosystem is further augmented by critical alliances with strategic analytical providers who broaden the depth, scope, and utility of the data extracted from EagleView’s high-resolution imagery. This superior asset intelligence can then be accessed by customers directly via the provider’s API integration into their proprietary systems, workflows, and decisioning models. EagleView property data can be tailored to best suit customers’ specific use cases.

“We have a significant data warehouse of accurate asset intelligence with data derived from 25 years of proprietary aerial image capture. In addition to leveraging our 3 billion images that cover more than 94% of the US population, we’ve formed best-in-class property data alliances to supplement and enhance our offering,” said EagleView’s CEO Piers Dormeyer. “Now is the right time to make this unrivaled property intelligence resource available to the market and let innovators scale the possibilities.”

This property data resource harnesses artificial intelligence to complement EagleView’s decades of human expertise and multi-industry know-how. The result is this new ecosystem has been designed to power critical business decisions in real time and at scale which can now be leveraged by a host of industries and applications including:

• Property Valuations and Appraisals: Mortgage lenders, property appraisers, and REIT managers can leverage data on property dimensions, roof age, roof material and conditions, and property conditions to enhance the accuracy of property valuations, enabling better risk assessment for loans or investments.
• Real Estate Portfolio Management: Property managers, real estate brokers, portfolio managers, and private equity firms can use EagleView’s data to analyze trends across properties to assess risks, optimize performance, or identify emerging opportunities.
• Loan Underwriting and Servicing: Mortgage lenders and home equity lenders can help automate and streamline underwriting processes with precise property data including roof age, roof condition and property condition insights to inform decisions.
• Insurance and Claims: Property and casualty insurers can access detailed roof age, property condition, roof pitch and heights, and structure identification data to process claims more accurately.
• Roofing and Solar Installations: Solar installers and roofing contractors can assess solar suitability across a range of properties as well as the roof age and roof condition of individual properties to better identify roofing prospects, solar host prospects, design optimized PV systems, calculate installation costs, and generate accurate solar project proposals.
• Civil Engineering: Civil engineers can leverage asset intelligence such as dimensions, roof age, roof material and conditions, and property conditions to effectively analyze current conditions for project planning and ongoing management.

The data helps decision makers make critical choices, conduct in-depth analysis, automate processes and workflows, analyze business risks, enhance business applications, and identify objects. Furthermore, it helps users to make more informed business decisions more efficiently, more effectively, and at the speed of business. Some recent case studies include: 

• A state energy trust who uses EagleView’s asset intelligence to help residential and commercial businesses save on energy costs and identifies ideal candidates to move to renewable resources. 
• A contractor CRM which uses EagleView’s property data to provide accurate, instant quoting tools to their customers for high-conversion lead capture applications. 

EagleView is interested in exploring potential integrations across all industry verticals which may have a use case for the data. Potential industries could include home equity lenders, REITs, property managers, home inspectors, commercial and residential real estate brokers, mortgage-issuing banks and FIs, asset management companies, property appraisers, and more.

For more information, visit eagleview.com/product.

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This acquisition will enable the introduction of a component and parts management application into Altium’s highly efficient cloud-based collaboration platform, Altium 365, allowing us to support new groups of customers in supply chain and procurement, including Electronics Supply Chain and Category Managers, further uniting industry stakeholders. The acquisition also achieves strategic synergy with Altium enterprise solutions, offering large-scale supply chain and procurement capabilities for enterprise companies, with a comprehensive parts catalog across entire programs, a critical element in achieving full Electronics Lifecycle Management.

“Altium’s acquisition of Part Analytics represents a further step forward toward our vision of a fully connected electronics value chain and electronics lifecycle management system for enterprise organizations,” said Aram Mirkazemi, president of Altium. “Part Analytics will play an important role in our transformative pursuit of the electronics industry,” he added.

Part Analytics was founded by former GE HealthCare supply-chain leaders, who bring with them deep domain expertise in enterprise-level customers and their procurement requirements. Their robust procurement application was built specifically for electronics to make supply chains more cost-efficient, resilient, and agile, enabling more effective supply chain management. Incorporating Part Analytics’ capabilities into Altium’s electronics creation ecosystem will strongly support Altium’s enterprise-level solution by adding an advanced fulfillment function to our current sourcing capabilities.

Jithendra Palasagaram, founder & chief executive officer at Part Analytics, said, “By joining Altium, Part Analytics’ electronics supply management platform will become part of a vast, connected electronics creation ecosystem in which customers can more efficiently execute design, sourcing, component acquisition, and lifecycle management through a single platform. Customers can continue to expect the same great product experience and customer support from Part Analytics going forward as part of Altium.”

The acquisition of Part Analytics brings Altium another step forward towards its open electronics system design and lifecycle management platform.

For more information, visit altium.com.

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In the context of AI, particularly language models like GPT (Generative Pretrained Transformer), input tokens and output tokens refer to the units of text that the model processes and generates, respectively. These tokens are the building blocks that allow the model to interpret and generate language.

Input tokens

Input tokens are the pieces of text that you provide to the model as input. This could be a sentence, a question or any other kind of prompt the model needs to process.

When you enter text, the language model first breaks it down into smaller units called tokens. These tokens can be individual characters, words or sub-words, depending on the model’s tokenization process.

For example, the sentence “Hello, how are you?” might be broken down into several tokens, such as: “Hello”, “,”, “how”, “are”, “you”, “?”.

The model uses these tokens to understand the meaning and context of the input and generate a response.

Tokenization is typically done using a process called Byte Pair Encoding (BPE) or similar algorithms that aim to split text into the most efficient and meaningful pieces for the model.

Output tokens

Output tokens are the pieces of text that the model generates as a response to the input. After processing the input tokens, the model predicts the next most likely tokens to produce a coherent and contextually relevant output.

The model generates output tokens one at a time, predicting the next token based on the previous ones, until it reaches a predefined limit or completes the response.

For example, if the input is “What is the capital of France?”, the model might generate the output “The capital of France is Paris.” Each word or punctuation mark in this output is considered a token.

Tokens and model limitations

Language models like GPT have a token limit, which refers to the maximum number of tokens they can handle in a single input-output interaction. This limit includes both the input tokens and the output tokens. For example, if a model has a token limit of 4096 tokens, that means the total number of tokens in the input plus the output must not exceed that number.

If the input is too long, the model may truncate it or may not be able to generate a sufficiently long output.

Token limits vary between different models. For example, GPT-4 may handle up to 8,000 or 32,000 tokens in one prompt, depending on the version.

Why tokens matter

Tokenizing text into manageable pieces allows the model to process and generate language more efficiently. It also helps the model deal with the complexities of human language, such as word variations, sentence structures, and punctuation.

In many AI systems, the number of tokens processed can directly influence the cost of using the model, as AI services may charge based on the number of tokens in both the input and output.

Other modalities for handling inputs and outputs

Tokens might be the primary method language models like GPT use to handle inputs and outputs, but they are not the only method.

While large language models (such as GPT) focus on text-based tokens, AI systems can also handle other types of inputs and outputs beyond text tokens.

AI models like DALL·E, CLIP and Stable Diffusion handle images as inputs and outputs. In these cases, AI processes pixels or embeddings of images, rather than textual tokens. The input might be an image (for image recognition) or a text prompt that generates an image.

For speech recognition or text-to-speech models—such as Whisper or Tacotron—the input could be audio signals (converted into spectrograms or other representations) or text, and the output could be transcriptions of speech or spoken responses.

Video AI models process and generate sequences of frames, allowing for tasks like video analysis, generation and transformation.

Some AI models are designed to process structured data such as graphs, tables and databases. These models do not use tokens in the same way that text-based models do. For example, AI used in graph neural networks (GNNs) works with nodes and edges, and models that deal with tabular data (such as AutoML models) process features in a structured form.

Some advanced AI systems, like GPT-4 and CLIP, are multimodal, meaning they can handle both text and images. These models don’t always use tokens in the traditional sense but instead work with various embeddings (vector representations) of input data, like a combination of textual and visual features.

Is token-based pricing the only model for AI?

No, token-based pricing is not the only model used for pricing AI services, but it is the most common model for text-based AI models. The pricing model varies depending on the type of AI service, the complexity of the model, and the application. Here are some common pricing models for AI:

1. Token-Based Pricing

Common for Text Models: In the case of large language models like GPT, token-based pricing is often used because it directly correlates with the amount of text processed (both input and output). Since token count determines the processing effort required, it serves as a fair metric for charging users based on resource usage.

2. Time-Based Pricing

Usage in Real-Time Processing: Some AI systems, particularly those with more real-time needs like speech recognition or video processing, may charge based on the time spent processing the input, such as seconds or minutes of audio or video analyzed.

3. Subscription or Tiered Pricing

For SaaS Models: Many AI services, particularly in cloud-based platforms, use subscription models where customers pay a fixed price based on the volume of usage (like API calls) or a set of features included. These may include monthly or yearly subscriptions. Some platforms offer tiered pricing, where higher levels come with more features, increased usage limits, or priority processing.

4. Pay-Per-Request or Pay-Per-Feature

For Specialized AI Services: Certain AI platforms, especially those in fields like image recognition, video processing, or AI-driven analytics, may charge based on specific requests or features used. This might be based on the complexity of the task (e.g., detecting objects in an image vs. simple image tagging).

5. Resource-Based Pricing

For Model Training or Compute-Intensive Tasks: When training large models or using cloud-based AI infrastructure, pricing may be based on the compute resources used (such as CPU/GPU time or memory). In these cases, you’re paying for the underlying infrastructure that the model runs on.

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“Huge strides have been made in resin printing, but absolutely nothing for printing with more than just one” said JF Brandon, CEO of MAASS. “The Shimmy MMSLA represents that shift by enabling unprecedented control over multiple materials at the microscale, opening new possibilities for integrated electronics, smart materials, and next-generation manufacturing processes.”

Breakthrough Multi-Material Technology

MMSLA introduces several technological innovations that address long-standing challenges in additive manufacturing:

• Dual-vat system enabling simultaneous printing of two distinct materials
• Resolution capabilities ranging from 0.5 to 50 microns
• Integrated cleaning system that prevents cross-contamination between materials
• Support for high-conductivity and dissolvable support materials
• Build volume of 78mm x 51mm x 141mm optimized for R&D applications

Enabling Next-Generation Applications

The system’s unique capabilities make it particularly valuable for several emerging applications:

• 3D printed electronics with 2-mil (50 micron) conductive traces
• Complex non-planar circuit designs
• Dissolvable support structures for ultra-high-quality surface finishes
• Research and development of smart materials
• Rapid prototyping of multi-functional devices

Strategic Focus on R&D and Future Manufacturing

While the initial release targets the research and development market, MAASS’s technology platform is designed to scale for future manufacturing applications. The company’s roadmap includes developing higher-throughput systems based on the same core technology for mass production of multi-material parts.

A Winning Team

MAASS was developed internally by Nectar Labs, a boutique Research and Design lab. JF Brandon is a partner at Nectar with Nicholas Coluccino, and together led the team from concept to reality in less than 18 months. Nectar has developed software and hardware products for major entities like Forest Stewardship Council and Heifer International. JF Brandon is an accomplished entrepreneur in the additive manufacturing space. Brandon’s track record includes key roles in successful ventures like GrabCAD and BotFactory, and his innovations have been recognized through major awards including the $50,000 Change the Course Competition and special mention from Autodesk/Future of Manufacturing.

For more information, visit maass3d.com.

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This Service Pack features the integration of TurboCAD Copilot and over 50 tweaks and bug fixes, showcasing cutting-edge innovation.

AI-Powered TurboCAD Copilot Technology

The TurboCAD Copilot feature introduces an AI-driven companion engineered to elevate the design journey. It serves four primary purposes:

• Responding to help related queries how to use the software.
• Delivering data-driven insights about CAD files.
• Utilizing an extensive AI knowledge base for a wide range of questions.
• Using Text to Image AI to create textures and backgrounds for photo rendering.

This integrated AI tool streamlines navigation, expedites the project’s progress, and enhances design endeavors with insightful analysis.

TurboCAD Copilot supports two levels: TurboCAD Copilot Help (free 1-year-subscription for all TurboCAD variants) and TurboCAD Copilot Professional (available as 1-year-subsription service).

TurboCAD Copilot Help uses “RAG” (Retrieval-Augmented Generation) and documentation to search for content relevant to questions. TurboCAD Copilot Professional includes these Help features and offers additional capabilities such as “Talk to your CAD Data”, general AI access, and Text to Image. Furthermore, it adeptly handles diverse multilingual requests, from guiding through the initial steps of using TurboCAD^® to sharing intriguing details about a file or providing insights on design trends and principles.

“With the introduction of TurboCAD Copilot, we are entering a new era of CAD that allows the designer not only the ability to talk to documentation but to talk directly to their CAD data. Talk to CAD revolutionizes how AI can interact directly to data in the context of CAD objects, geometry, topology, parameters, and custom attributes. Designers can now ask questions that were previously constrained by traditional UI conventions—questions like ‘Examine my file for 3D printing suitability and suggest ways to reduce printing costs’—providing a new way to enhance quality and productivity,” states Tim Olson, vice president of IMSI Design”

A Closer Look at TurboCAD 2024.1’s Exciting New Features

Dive into the innovative enhancements of TurboCAD 2024.1 with their comprehensive video overview. This visual guide showcases all the new features that make TurboCAD 2024 a leader in CAD software.

To quickly see the list of new and key features in TurboCAD Platinum, TurboCAD Professional, TurboCAD Deluxe 2D/3D, and TurboCAD Designer 2D, check out TurboCAD 2024 New Feature Comparison and TurboCAD 2024 Key Feature Comparison.

Availability and Pricing

They’re delighted to share that this Service Pack is available at no additional cost for existing TurboCAD 2024 users. For those using older versions of TurboCAD, upgrade pricing is available.

“Our commitment to delivering top-tier design tools is evident in the TurboCAD 2024.1 Service Pack, which not only introduces TurboCAD Copilot but also includes numerous enhancements, over 50 bug fixes, and critical maintenance updates. These improvements demonstrate our dedication to providing a seamless and advanced user experience,” outlines Rita Buschmann, senior product manager, CAD and Home Design.

Their product lineup includes:

• TurboCAD Platinum: Their premium package, priced at $1,499.99.
• TurboCAD Pro: Packed with advanced tools for detailed design work, available for $999.99.
• TurboCAD Deluxe: Offers a comprehensive toolkit for various design projects, for $299.99.
• TurboCAD Designer: Dedicated to 2D design, perfect for beginners, at an affordable $99.99.

For those seeking a more dynamic design experience, TurboCAD Copilot Professional is offered with a 1-year-subscription for $199.99.

Additionally, their Training Guides, Add-ons and Symbols are specially designed to complement and expand capabilities when working with TurboCAD 2024.

For more information, visit TurboCAD.com or imsidesign.com.

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