Design for additive manufacturing. Guidelines and case studies for metal applications: Presentation held at The Cutting Edge, CMTS 2017, Canadian Manufacturing Technology Show, Toronto, 25 - 28 September 2017
Bibliographic record
Abstract
Additive Manufacturing (AM), often simply called 3D printing, provides nearly unrestricted freedom to design parts in order to optimize their functionality. It offers designers and manufacturers the ability to produce shapes and designs that would be impossible to produce using conventional manufacturing technologies such as moulding or machining. Optimizing the design of parts can be achieved by reducing their weight, incorporating internal features or reducing the need for assembling separate components. AM also offers the opportunity to reduce or eliminate waste that results from manufacturing, and to reduce the need for warehousing while enhancing the value of local production. The efficient use of AM technologies requires a rethinking in 3D design, which currently still poses a barrier particularly for small and medium-sized enterprises (SMEs) of metal industry. Advantages and opportunities as well as restrictions of additive manufacturing must be well known in order to pave the way for a successful commercialisation and to make AM a competitive manufacturing method. The presentation summarizes a study on "DESIGN FOR ADDITIVE MANUFACTURING - Guidelines and Case Studies for Metal Applications" and is based on seven components, which were developed and manufactured in the scope of separate projects, but have been selected, reviewed and assessed in a detailed case study particularly and retrospectively within this task. The design of each component was tailored to the specific needs of the chosen AM technology. The development and manufacturing activities were performed by Fraunhofer Institutes, who are members of the Fraunhofer Additive Manufacturing Alliance. The alliance integrates seventeen Fraunhofer Institutes across Germany, which deal with subjects concerning additive manufacturing and represent the entire process chain including the development, application and implementation of additive production processes as well as associated materials. The presentation identifies leading edge industrial applications and trends associated with the design for additive manufacturing and limitations related to current AM technologies. The evaluation of the seven case studies highlights general design principles to take best advantage of the powder bed based additive manufacturing techniques Laser Beam Melting (LBM) and Electron Beam Melting (EBM). Moreover, the design optimisation and material characterisation are analysed. Finally, there are given overall conclusions with focus on AM-specific design optimisation, main flaws and weaknesses of the considered metal AM processes as well as aspects of AM commercialisation.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".