Additive manufacturing, commonly known as 3D printing, presents a speed paradox: it can be fast for prototyping and small-batch production but slower and less economical for high-volume manufacturing.
While prototyping and small-batch production can sometimes be completed in under a week, conventional tooling can take 6–18 months. At volumes of hundreds of thousands or millions of parts, however, established methods such as injection moulding and stamping can be faster and cheaper despite their longer pre-production lead times.
Navigating the Speed Paradox in 3D Printing
Roman Arkhangelskiy, founder and managing partner of Greater Boston-based manufacturing company Upside Parts, believes artificial intelligence (AI) could help address this conflict, transforming how parts move from design to delivery.
This speed paradox has long been a challenge for the industry. Discussions about the challenges of high-speed 3D printing, including the trade-offs between speed and quality, emerged by February 2022. These discussions highlight that increasing print speed can reduce print quality, dimensional accuracy, and, in some cases, part strength, posing a complex trade-off for manufacturers.
The additive manufacturing industry has spent years addressing the perception that 3D printing is primarily suited to prototypes and short production runs. While 3D printing can produce individual parts rapidly, its economics and throughput at high production volumes remain challenging in some applications.
One source of this challenge is the trade-off between print speed and factors such as quality, accuracy, and process stability.
Rapid production can be useful for iterative design and bridge production. At production volumes of hundreds of thousands or millions of units, established manufacturing methods can offer advantages in throughput and unit cost.
This dichotomy means that despite isolated examples of successful additive manufacturing in production environments, such as aerospace components and medical devices, wider adoption for high-volume production remains challenging.
The trade-off arises partly because increasing print speeds places greater demands on 3D printer components and process control. The hotend’s capacity to melt filament, the motion system’s ability to manage rapid changes in direction, and the cooling system’s efficiency in solidifying each layer must operate at higher rates and respond more quickly to changes in the printing process.
Increasing print speed can contribute to defects and reduced structural integrity, which may affect the quality and reliability of the final part.
The Amazon Effect Transforms Manufacturing Expectations
Consumer expectations shaped by rapid delivery in e-commerce are increasingly influencing expectations in manufacturing. Roman Arkhangelskiy terms this phenomenon “The Amazon Effect,” where some clients increasingly expect rapid fulfilment of industrial components. He describes receiving after-hours calls from clients seeking parts within hours, sometimes arriving with the required models and waiting on-site for production.
This creates a gap between rapid quoting and actual production: an instant quotation does not necessarily mean that a part can be manufactured immediately. The journey from a finalised design to a shipped part traditionally involves considerable friction and lead times. Arkhangelskiy points out that while printers and materials are constantly improving, the challenge lies less in individual components than in coordinating the different stages of the manufacturing process.
Upside Parts addresses this by using AI to streamline its operations. This includes automating project onboarding, routing jobs to suitable production pathways, and continuously monitoring production capacity and printer availability. According to Upside Parts, this integrated approach reduces the time between order placement and the start of printing.
Arkhangelskiy says the company has reduced its pre-production wait time from around four minutes to two or three minutes, meaning printing begins almost immediately after an order is placed.
AI’s Expanding Role in Production Workflows
Artificial intelligence is increasingly being applied across engineering, with reported productivity gains in some applications. AI models are now routinely used in advanced design and simulation, enhancing quality assurance processes, and improving root cause analysis in complex industrial systems. These applications suggest that AI could also help address some of the workflow and scheduling constraints associated with additive manufacturing.
At Upside Parts, AI is not merely an auxiliary tool; it is central to the production philosophy. The technology is used to support production decisions and route projects through available manufacturing pathways. By coordinating machine availability, material requirements, and production schedules, AI minimises downtime and can improve machine utilisation and throughput.
This intelligent orchestration of resources represents a crucial step towards bridging the divide between rapid prototyping and large-scale production. The approach could help additive manufacturing compete more effectively with conventional methods in applications where production volume, lead time and unit cost are important considerations.
The Human Element in Advanced Manufacturing
The increasing use of AI in manufacturing also raises questions about its implications for human labour and engineering roles. Roman Arkhangelskiy acknowledges these concerns, admitting his views on the future role of professional engineers have changed. He initially believed the era of human engineering would endure longer but now sees a different trajectory.
This change of heart stems from observing a rise in AI-generated CAD files submitted to Upside Parts, which he says have contained relatively few design errors. Coupled with his company’s AI-driven production cells, Arkhangelskiy envisions a future with “fully robotised and AI-driven manufacturing cells.” This development also raises questions about how humans should maintain oversight of AI-powered systems and their outputs.
The notion of autonomous manufacturing cells, While raising questions about potential job displacement, also points to an evolution in roles. Instead of hands-on production, human expertise might shift towards managing, optimising, and innovating within these advanced AI-driven environments. It suggests a future where engineers collaborate with AI, leveraging its speed and precision for increasingly complex challenges.
Reshoring Manufacturing Through AI and Automation
Global manufacturing has long been characterised by competition among producers in different regions, including China. Roman Arkhangelskiy identifies this dynamic as one factor behind Upside Parts’ strategy. He says that, around two years ago, customers reported receiving parts from China with lead times of about three weeks, despite the trade barriers then in place. Upside Parts’ initial ambition was to surpass this, aiming for faster turnarounds.
This competitive drive aligns with broader discussions about reshoring manufacturing capabilities, particularly in Western economies. While trade barriers have generally increased, the practicalities of bringing manufacturing back onshore are complex and span decades. Arkhangelskiy doesn’t claim to have all the answers for this long-term shift, but he is emphatic about the role of AI and robotics.
He argues that slower adoption of AI and robotics could widen productivity and competitiveness gaps between manufacturing economies. Arkhangelskiy argues that wider adoption could improve efficiency, production speed, and cost competitiveness. He frames the issue not only in terms of domestic employment but also in terms of global industrial competitiveness.
Future Outlook for Additive Manufacturing
Arkhangelskiy believes AI can address several of the operational limitations facing manufacturing. He sees its application as critical for overcoming the speed paradox in 3D printing, supporting faster production workflows, including for urgent orders. His vision extends beyond software to advances in materials and hybrid additive-subtractive manufacturing systems.
Upside Parts’ vision is for a facility in which machines using different materials and manufacturing technologies operate within an automated production environment, with what Arkhangelskiy describes as “Amazon-like speed”. The proposed integration would use AI to coordinate different technologies and could reduce some of the workflow constraints associated with additive manufacturing.
Arkhangelskiy predicts that the industry could realise more of this vision within five years. Such a future would redefine expectations for lead times and production flexibility, enabling manufacturers to respond with unprecedented agility to market demands. If these developments scale successfully, they could reduce some of the workflow and throughput constraints that have limited high-volume additive manufacturing.
