Recent advances in additive manufacturing of engineering thermoplastics: challenges and opportunities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There are many limitations within three-dimensional (3D) printing that hinder its adaptation into industries such as biomedical, cosmetic, processing, automotive, aerospace, and electronics. The disadvantages of 3D printing include the inability of parts to function in weight-bearing applications, reduced mechanical performance from anisotropic properties of printed products, and limited intrinsic material performances such as flame retardancy, thermal stability, and/or electrical conductivity. Many of these shortcomings have prevented the adaptation of 3D printing into product development, especially with few novel researched materials being sold commercially. In many cases, high-performance engineering thermoplastics (ET) provide a basis for increased thermal and mechanical performances to address the shortcomings or limitations of both selective laser sintering and extrusion 3D printing. The first strategy to combat these limitations is to fabricate blends or composites. Novel printing materials have been implemented to reduce anisotropic properties and losses in strength. Additives such as flame retardants generate robust materials with V0 flame retardancy ratings, and compatibilizers can improve thermal or dimensional stability. To serve the electronic industry better, the addition of carbon black at only 4 wt%, to an ET matrix has been found to improve the electrical conductivity by five times the magnitude. Surface modifications such as photopolymerization have improved the usability of ET in automotive applications, whereas the dynamic chemical processes increased the biocompatibility of ET for medical device materials. Thermal resistant foam from polyamide 12 and fly ash spheres were researched and fabricated as possible insulation materials for automotive industries. These works and others have not only generated great potential for additive manufacturing technologies, but also provided solutions to critical challenges of 3D printing.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it