Bibliographic record
Abstract
Manufacturing industry is currently at the dawn of a new industrial revolution. Within the past two centuries, humanity passed through three industrial revolutions leading us to the ability to mass produce complex products in affordable ways. However, manufacturing industry lost a significant element in this journey: individuality. Today’s customer is part of a global society in which connectivity and access to information plays a key role. As such a customer wants to be part of the world but in a very personal way, for example through unique and personal products. Industry recognized this new trend. Novel business models emerged. New key words are: cloud services, smart manufacturing, mass personalization. However, this results in a new challenge for manufacturing industry. Instead of mass fabricated products small series (batch-size 1) are required. Individual products can be fabricated by rapid-prototyping, but it is very challenging to produce personalized products in an economical way. New processes need to be developed which are of a new kind. This new revolution was recognized recently by industry and in Germany the key word industry 4.0 was introduced to characterize this “fourth industrial revolution”. The aim of industry 4.0 is to design smart factories in which batch-size 1 products on demand can be produced economically. This means that any cost not directly related to the final product must be reduced to zero. In mass production such indirect cost could be removed by spreading them over the immense number of identical fabricated products. In batch-size 1 production this is no longer possible, eliminating processes which require expensive tooling. Manufacturing processes must further be able to adapt themselves quickly and show a very high flexibility. Even process optimization becomes a real challenge. Among these challenges post-processing technologies take a prominent place. Manufacturing of a product is never achieved in a single step, even upon using technologies as additive manufacturing (AM). AM parts require post-processing in terms of surface finish. However, as printed parts are generally complex (much more complex parts can be produced by AM, reducing needs for assembly, driving costs down) methods for surface finishing become difficult to identify. AM Parts with narrow inner surfaces (dimensions < 1 mm) are particularly challenging to post-process. Currently few processes exist, which are all very labor intensive. Electro-polishing (EP) is a promising approach to tackle these issues. These considerations show that suitable manufacturing processes for batch-size 1 production must be highly flexible and have little overhead (in particular the need for tooling). As such, electrochemical processes are very promising. Such processes require little to no specialized tooling and are able to handle virtually any shape, including inner surfaces. In the present communication, it is shown how electrochemical processes can be used to design new manufacturing processes for industry 4.0. Some examples are discussed in the field of hard to machine materials and surface functionalization, and post-processing technologies as EP are discussed in more depth with experimental setup, parameter settings and used geometries. Titanium alloy (Ti6Al4V) AM parts (landing gear bracket) with surface roughness from Ra 13.9 µm | Rz 50.42 µm are successfully polished down to Ra 1.8 µm | Rz 6.26 µm with EP technology (see figure). Figure 1
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".