How 3D Printing and AI are Transforming the Product Development Landscape
In today’s market, companies are pressured to innovate faster, reduce costs, and deliver quality products. Many are adopting advanced technologies like 3D printing and AI to meet these demands. Together, they are reshaping how products are conceptualized, designed, and manufactured.
3D Printing in New Product Development
3D printing has been around since the 1980s, but it didn’t break into the mainstream until the 21st century. It was once used primarily for prototyping, and has now evolved into a versatile technology that supports every stage of product development, from early design concepts to full-scale manufacturing.
Integration with CAD
FDM (fused deposition modeling) printers are the most common type of 3D printers. FDM printers deposit heated filament layer by layer according to design specifications. These models are designed in CAD programs, which give developers a wide range of tools to improve detail and accuracy. One of the most significant benefits of CAD modeling is how easy it is to change your designs. Rapid prototyping not only increases efficiency by creating prototypes faster than those not made with 3D printing; it also increases efficiency in the redesign process simply because prototypes are produced and ready for testing in a matter of hours.
Choosing the right CAD program can be tricky, as there are many different software available. Two extremely popular options include AutoCAD (geared towards architects) and SolidWorks (optimal for product development). Altium focuses on PCB design, while Fusion360 is aimed at hobbyists. These are only a small portion of the many options available to designers.
3D printing prototypes are common because of their reduced cost and time. More advanced 3D printers are used further in product development. Additive manufacturing 3D prints end-use parts and products. All parts of product development are connected through cloud-based programs, which streamline processes.
Benefits of 3D Printing in Product Development
As discussed above, 3D printing has led to rapid prototyping, allowing for faster creation and testing of prototypes. In addition to physical benefits, 3D printing has impacted other parts of business operations.
Cost Efficiency
Rapid prototyping saves companies money because it does not require specialized labor, and engineers who would have devoted their time to building a physical prototype are freed up to complete other tasks. No special tools are needed, and materials costs are lower than old-fashioned prototypes. Changing prototyping strategies and buying machines could be costly, but companies quickly see a return on their investment both financially and in their new capabilities.
Flexibility
CAD modeling is not the only flexible part of rapid prototyping. Unlike traditional prototyping, which requires specialized molds and processes, 3D printing creates precise designs in a single machine that were once too challenging or expensive to make. This allows teams to explore ideas and refine products in the time they previously would have spent building prototypes. Projects are kept on schedule and achieve results.
3D Printing in Action
At Pivot International, we use CAD and rapid prototyping to effectively and efficiently reach the best designs for our partners. One of our UK-based companies, Wideblue, has a 3D printer and prototype workshop, development lab, and optical lab where product designs are tested.
The medical industry is a key benefactor of 3D printing. Like other products, 3D printing saves money and time in the design and manufacturing process of medtech devices. Beyond this, the adaptability of 3D printing helps medical professionals create items tailored to their patients. Design programs can build precise models of body parts, such as organs, for doctors to study. 3D printing is also becoming increasingly common in manufacturing prosthetics, which are custom-designed for each patient.
How AI is Reshaping Technology and Product Development
AI is revolutionizing how we approach product design, process efficiency, and customer insights.
AI is involved in product design through generative design. Generative design generates alternative product designs based on an existing specification. These new designs account for performance, manufacturing, and other requirements. AI programs do not have the expertise and intelligence of human design teams. Still, they are a valuable tool for exploring different design ideas and finding the optimal product design for a project.
AI in Management Systems
Traditional management systems are programmed to run following a predefined set of rules. AI systems, on the other hand, continuously learn from data to adapt to new situations.
AI systems’ ability to take in data about production gives them the ability to recommend optimized processes. These systems can also recognize potential disruptions when they are still in the early stages, so teams have time to respond before problems affect the market.
Integrating Customer Preferences
The primary benefit of AI is that it is constantly learning and adapting to new situations. This is useful for keeping up with supply chain and manufacturing changes. However, tracking external trends can be as important as monitoring internal ones. Market research tracks consumer feedback, runs competitor analyses, and follows market conditions to keep businesses at the forefront. AI can recommend design alterations based on market research results, improving user experience.
The Intersection of 3D Printing and AI in Product Development
AI and 3D printing work together to optimize product development. As we’ve talked about, AI enhances CAD programs, which create the designs produced by 3D printers. AI can also analyze data from prototypes and products and recommend changes to save costs, speed up production, and increase quality. Customization also increases since AI can quickly personalize designs based on consumer preferences.
The Future of AI and 3D Printing
The growth of additive manufacturing, especially end-use products, points to a future where AI can manage 3D printing-powered production from beginning to end. As companies buy into these technologies and they become more advanced, additional factors will be tracked by AI, such as the sustainability of products and processes.
3D printing and AI are reshaping the way products are developed. These technologies allow companies to refine designs, improve production, and better respond to customer needs. The scale of 3D printing is also evolving. At its height, 3D printing is not just creating components or products; it has created entire houses.
As they continue to evolve, 3D printing and AI will open up new possibilities for businesses, driving innovation and transforming industries. Embracing these tools now sets the stage for better products and stronger competitive advantages in the future.
Pivot International has over 50 years of design and manufacturing experience. Our global teams of engineers are experts in optical, sensor, motor control, and more technologies. We work with our partners to develop their products from concept to market by offering design, rapid prototyping, and worldwide manufacturing services. We can be your partner throughout the entire product development process, from concept through full-scale production. To learn more about how we can help develop your next product, contact our team today.
Software Development in the Age of AI: What You Need to Know
Software development is undergoing a paradigm shift. Data is superseding code and rewriting the industry. The catalyst? Artificial intelligence. Here are the key takeaways from this ongoing evolution:
Developers will have to become more proficient in machine learning algorithms
Software developers’ skill sets must adapt to this new era. Applications are no longer just deterministic. A deterministic algorithm will produce identical output, specific to the input. When issues arise in these systems, developers are trained to go into the code. From there, they will debug and rewrite.
AI-powered systems, however, operate differently. These systems rely on open source library standard algorithms or the options made available in AI platforms. Following selection, the algorithms become working systems. Data points or features are highlighted and weighted accordingly, depending on their level of importance.
According to Todd Schiller, head of engineering at MOKA, a disruptive technologies firm, the most successful developers will be those who “have the best understanding [of] the essential complexity of their domains: which data are important [and] the impact of uncertainty on decision making…”
Software 2.0
At the 2018 Spark+AI Summit, Tesla AI Director Andrej Karapathy discussed in his keynote how “A lot of our code is in the process of being transitioned from Software 1.0 (code written by humans) to Software 2.0 (code written by an optimization, commonly in the form of neural network training).” Software 2.0 involves developers moving their attention away from “designing an explicit algorithm” to “curating large, varied, and clean datasets, which indirectly influences the code.”
Microsoft’s DeepCoder is an indicator of what’s to come in the world of software development. DeepCoder creates new applications by predicting which properties the application must have to generate the coveted outputs.
A less experimental example is Ubisoft’s Commit Assistant AI, which has already been integrated into the Rainbow Six and Assassin’s Creed games. Commit Assistant AI can actively identify coding defects as the programmers write them.
Continually testing models against real data is necessary for efficacy
Data can be likened to a moment in time. In other words, when conditions change, so must the model. This drift can be managed by the continual testing of models against real data.
An example of this is an AI system analyzing historical data to determine when factory equipment is due for maintenance. If the factory uses these predictions and schedules a servicing, subsequent forecasts must factor in this information to remain accurate.
The question of data security
Companies shifting towards AI-powered software development practices are encountering new security challenges. Third-party AI algorithms can contain insecure dependencies. Using the code’s latest version safeguards security.
Artificial intelligence is reshaping software development, and product developers and manufacturers must adapt to these changes or be left behind.
At Pivot International, we’re at the forefront of the latest advancements in AI that are changing the game for software development. As a premier single-source partner, we’ve worked with some of the most successful names in software to bring products from design to development to production. Contact us to learn more about how Pivot can help your company prepare for the future.
AI and the Future of Manufacturing
The original assembly line made it cheap to make a million of the same thing, but adaptive manufacturing makes it cheap to make a million unique things.
-Nick Pinkston, Founder and CEO of Plethora
The Digital Age has not only dawned but is exploding with applications across every field and industry. Just as AI is changing the face of medicine, engineering, and even education, so too is it changing the way manufacturing is conceived and executed in three key ways, rendering factories and fabrication processes as “smart” as any iPhone and as connected to the Internet of Things (IoT) as Amazon’s Alexa.
Heading off problems at the pass
Traditionally, industrial equipment has been scheduled for maintenance at regular intervals, regardless of operating condition. In practice, this meant that it was not uncommon for a company to dispatch technicians to routinely “service” a machine in perfectly good working order or a machine with unanticipated and undiagnosed equipment failures, wasting costly time and labor in the process.
But with advances in AI, machines can be fitted with sensors and networked, allowing their performance to be monitored, analyzed, and even enhanced or tuned, eliminating unnecessary and costly “check ups” or the need for unexpected “major surgery.”
The emerging practice of digital “twinning” involves creating virtual models of industrial-scale machinery, making it possible for either AI programs or human operators to “mind the shop” and generate predictive analytics.
Over half a million twins of varying complexity are are currently in use. Explains Mariya Yao, an expert in applied AI and Chief Technology & Product Officer at Metamaven, “Complex twins like those of gas turbines interpret data from hundreds of sensors, understand failure conditions, track anomalies, and can be used to regulate production based on real-time demand.”
But even twinning of simple machines can result in significant business benefits. For example, many elevator manufacturers generate the lion’s share of profit on servicing costs rather than equipment sales. Using traditional servicing practices in inefficient and wasteful, and by fitting elevators with sensors and twinning them, service is rendered only as needed.
Higher Quality Quality-Control
As the saying in evolutionary biology goes, “Rocks don’t get cancer, dogs get cancer,” pointing to the principle that higher-order, more complex structures bring with them exponential possibilities for breakdowns and complications of all kinds.
The more highly complex a product (a laptop, for example), the more manufacturers must contend with unanticipated downtimes, low yields (the percentage of units that fail to meet quality control standards), and low productivity (the time required for production).
Productivity is often in competition with yield, because the more quickly a a production process is pushed, the greater the likelihood for errors or poor quality product.
But the faster feedback loops afforded through AI enable heightened monitoring and adaptive control that can accelerate productivity without compromising the quality of yield.
Specifically, quality inspections can be handed off to AI rather than to humans, the latter of which are poorly equipped to keep up with both the complexification and proliferation of products.
While human-lead anomaly-detection on hundreds of units can consume many hours, inspections conducted by cameras run on AI vision-algorithms can do so in mere seconds, enabling manufacturers to identify and resolve production issues before costly delays accrue.
Consumer-Informed, On-Demand Production
As long as there have been buyers and sellers, accurately estimating consumer demand has posed a major challenge. Overestimating (or underestimating) consumer demand can be a sink or swim proposition, and many manufacturers have been left “holding the bag.” (And in manufacturing, a bag that is too full is not necessarily better than a bag that is empty.)
AI-enabled real-time demand-visibility allows manufacturers to strategically respond (rather than blindly react) to consumer trends through apps connected to commercial IoT and industrial IoT. Thanks to the rising ubiquity of smart devices like Amazon Echo and Google Home, consumer browsing and buying habits are increasingly transparent and, with consumer consent, this data can be fruitfully fed downstream to keep manufacturers abreast of changes they need to make in their supply chain and production activities.
While end-to-end networks that completely close the feedback loop for on-demand production are still the stuff of the future, companies like Amazon are leading the way and smaller businesses are optimizing supply chains by combining multiple business processes on a single, user-friendly platform.
If you’re a company looking to make your manufacturing “smarter” or are looking to bring a smart product to market, we can help. At Pivot, we’re industry leaders in smart manufacturing and have helped hundreds of businesses take their next step to success. Contact us today and see what we can do for you.
