Mass customization is a manufacturing paradigm where custom products are produced at large volumes that are traditionally only achievable by conventional mass production. Additive manufacturing (AM), or 3D printing, has long been heralded as the technology that will realize this vision because of its ability to produce complex and unique parts, hence providing “complexity for free”.
The idea that complexity is free is at best a half-truth and at worst a blatant lie, especially when it comes to mass customization. Part complexity is often used as a proxy for process complexity, but this is facile. While there are geometries that are impossible to produce with conventional mass production methods, these are far and few. Most high-volume applications of AM produce parts that are not impossible if made conventionally but add process complexity if done so: Complexity of producing multiple unique parts in a single build, complexity of producing unique builds consecutively, and similarly the complexity of tuning the entire value-chain to deal with unique parts at scale.
Mass production gets around this by dealing with complexity upfront. The part is designed, tested, refined for manufacturing, and locked in. Each step of the value-chain is then optimized for the specific part to be produced. This allows the process to scale and achieve superior unit economics.
If we are to realize true mass customization with AM that can compete with these superior unit economics, the value chain needed must differ in the following ways:
While digitization can be a misleading term, the objective of this first step is to acquire data from the customer. This can take many forms including pressure-map scanning of feet for footwear, CT scanning of dental impressions for intraoral products, optical scanning of faces for eyewear, or even just a click of a mouse button that confirms a customer’s choice. Whether this data is a 3D file or plain text, the digitization step needs to obtain parameters that can be used to drive subsequent steps of the process and produce the end part.
This is arguably the most important step in the value-chain. Many (if not most) practitioners still employ an entirely manual design process. This is difficult to scale and can become prohibitively expensive at high volumes. The design pipeline is not to be confused with product design and validation that occurs during the product development process which sets constraints and guidelines for the final part. These may include overall form and function, dimensional guidelines, aesthetic considerations, and other recommendations based on the AM technology of choice. The design pipeline on the other hand focuses on converting the digitized data from the first step to a print-ready design. The pipeline must therefore be engineered to accept a broad range of digitized inputs, apply design rules to varying geometries, and account for limitations posed by the specific printer or material used.
The production step, or the printing step, eases dealing with complexity due to the nature of additive manufacturing; But it does not make it free. While there is little to no tooling required between print jobs of unique parts, the parameters of the printing process need to be considered at the part-level. Something as simple as part orientation, for example, can affect mechanical properties, surface finish, nesting efficiency per print job, and printing time. In many ways, the digital nature of AM as a manufacturing technology transforms tooling into a software problem that is dealt with during slicing.
The inherency of mass customization makes automating the post-processing step challenging. Varying part geometries may require varying parameters for support removal, heat or chemical treatment, and/or finishing. Post-processing solutions have improved significantly in recent years and will continue to do so, but each process has its own limitations and design considerations. These need to be accounted for and fed into the design pipeline to make the process(es) as repeatable as possible.
Despite the many benefits of AM, complexity is not free; Not when you consider the entire value-chain required for mass customization. The discounted complexity AM affords during the production step does not account for the complexity that is introduced in the other steps. True mass customization can be achieved only when the entire value-chain is engineered to handle this complexity that simply does not exist in conventional mass production.
Glidewell is participating at Additive Manufacturing Strategies, taking place in New York City from February 7-9, 2023. Ankush Venkatesh, Intrapreneur of Additive Manufacturing at Glidewell will be taking part in Session 2, Panel 3: Low-Cost Mass Customization in Dentistry on February 8. Register for your ticket to attend here.
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