Layer-by-Layer: Opportunities in 3D printing Technology trends, growth drivers and the emergence of innovative applications in 3D printing
Bibliographic record
Abstract
Additive Manufacturing (AM) is a manufacturing process that deposits materials layer-by-layer to build a tangible product. The most common, and the most popular currently, is 3D printing. AM is claimed to have triggered a third industrial revolution because the technology presents new and expanding technical, economical and social impacts (Economist, 2012). Particularly, the increased accessibility to 3D printing capabilities has allowed mass customization to become more widespread in industries such as healthcare and consumer markets. Since the advent of mass production in the early 20th century, consumers’ demands have been met by producing large numbers of goods in significantly less time than ever before. While production time and price decreased, they did so at the expense of customization. AM makes it possible to offer customers options to personalize the products and goods they are purchasing, from custom-made prosthetics to a personalized smartphone case. The importance of customization cannot be understated. Researchers agree that customization will continue to grow as a major trend across industries. J.P. Gownder, vice president and principal analyst for infrastructure and operations professionals for Forrester, says that while “mass customization has long been the next big thing in product strategy … changes in customer-facing technology are opening up new opportunities for product strategists to bring customers into product design, creating both customer loyalty and higher margins” (Forrester, 2011, p. 12). Marina Wall of the Heinz Nixdorf Institute at the University of Paderborn also contends that, “individuality or mass customization are important trends driving change so increased product diversity is important for the future and for meeting individual customer requirements. AM has great potential for freedom of design that can cope with these challenges” (as cited in AM Platform, 2013, p. 29). 3D printing is expected to play a significant role in the future of mass customization. This report explores the potential impact that this technology may have in various sectors. Through secondary research and conversations with business analysts, investors, members of the 3D printing community, experts and entrepreneurs, we investigated some of the potential market opportunities the technology is unveiling. We also explore sources of capital and nascent business models for those innovators interested in capitalizing on this technology. As part of our investigation, we also profile some organizations involved with 3D printing or related markets. These entrepreneurs are actively and creatively pushing the limits of 3D technology. For the purposes of this document, the terms 3D printing and additive manufacturing will be used interchangeably.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".