Coming soon: 3D foodprinting. Commercieel aantrekkelijke toepassingen van 3D foodprinting.
Bibliographic record
Abstract
The first developments of 3D printing (Rapid Manufacturing) came during the late ‘80s and were based on the principle of building a 3D object layer by layer. The process starts with designing a virtual 3D CAD model, which can be uploaded to a 3D printer and fabricated via several printing methods. Each method uses the same principle (building up an object layer by layer), however uses a different technique. FDM builds up an object by printing a material like for example ABS directly on a platform. SLA and SLS make use of a laser which respectively hardens out layers of a photopolymer and metal powder. In comparison with conventional methods, RM distinguishes itself by adding material where required, instead of removing material. The advantages of Rapid Manufacturing are generally the ability to produce complex geometries, positioning and dosing a material with extreme precision, often leading to lower production costs compared to conventional methods, moreover the ability to print objects without the use of specific tools (with the additional changes in the logistics) and products which can be produced/tuned for/in the individual. Rapid Manufacturing also has a number of “disadvantages” which are expected to be solved in the future. For example, the hardware is (especially for the private consumer) often too expensive, the existing software is often too complicated for the general consumer, the production rate for large amounts of products is still too low and the costs of production are still too high for the majority of the products. The high costs are a good example of the relativity of the disadvantages because they depend upon, inter alia, the function of the product. By replacing a raw material like ABS in the process of FDM by a nutrient such as chocolate, a new application of RM arises; 3D foodprinting. 3D foodprinting is having a lot of media attention because of the futuristic nature and the potential impact 3D foodprinting might have on society in the future. On the Internet there are several 3D foodprinting concepts circulating, which can be divided into visually- appealing but unrealistic concepts, visually unattractive but realistic concepts and variants of 3D foodprinting from a medical and industrial perspective. 3D foodprinting is a new application of an existing technology and is yet commercially unattractive and can not be applied on a large scale. However 3D foodprinting is yet a fun way to experiment with RM and play with food. To make 3D foodprinting commercially attractive and applicable on a larger scale, more research has to be done to contemporary problems (commercial opportunities). Examples of these opportunities are the applicability of printable nutrients, the usability of both the software and the hardware and the acceptance of 3D foodprinting by society. It is expected that, as with Rapid Manufacturing, these problems will be solved in the near future and that these improvements will make 3D foodprinting commercially more attractive. Het Foodatelier from Enschede and the University of Twente both see great potential in 3D foodprinting and decided to examine the commercial use of 3D foodprinting. Based on the research and an organized workshop, three different concepts/applications for different sectors were set up; a home use of 3D foodprinting, the printing of an ice cube for the catering industry and a concept where printing two types of chocolate has an added value. The printed ice cube was elaborated into a visual and commercially attractive concept. The printing of ice is an existing technology (developed by McGill University) but yet not been applied as 3D foodprinting. By combining the printing of ice with (existing) software in which a 3D model can be obtained from pictures, the consumer will be able to easily print the contour of his/her face in an ice cube. The ice cube is built up in layers where at the “end”, the cavity of the contour will be filled with liquor. Both a liquor brand as a club/restaurant can thus present itself as an innovative and leading company. The ice cube adds nothing to the taste, but does increases the whole experience when going out, responds to the need for personalized products and besides it is very fun to have an ice cube containing your face. To finally commercially apply the utopian concept of the ice cube, Het Foodatelier and the University of Twente will perform a follow-up study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.153 | 0.067 |
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".