Indication of cracking during pressing of advanced thin plates using an AE-based monitoring system
Bibliographic record
Abstract
The acoustic emission that is detectable in the forming of complex thin part plates contains information about the quality of the pressed plates. The information can be used as process control and to filter out advanced thin plates with defects such as cracks. The article describes the results obtained in a try-out pressing process in special developed punch-die pairs with different pressing depths in a press at Alfa Laval in Lund. At the trials two module testing tools called SE30 and SE25 were used to create a different degree of formability by different surface enlargement in the same complex original design. Used sheet materials were stainless steel and two qualities of titanium. The used measuring system was based on a PC with AE-sensor of model 8152 from Kistler. The results of experiments show that it is possible to detect and distinguish the plates that fracture during the forming operation. For a large number of plates detectable signals from areas close to cracks with only local necking were also obtained. This local necking cannot or can hardly be detected by light-test or light detecting equipment. The left figure shows a metal sheet with fractures and the right figure describes measured acoustic emission as a function of time and the number of the details. In this example the first four plates are fractured and the last five plates are without any fractures. There are several differences between the two types of signals of acoustic emission. The signal that indicates cracking of the plate differs by a sudden high acoustic emission and also a slightly lower level before the crack formation is started. Before the fracture occurs the sheet metal is stretched out and the lower level of acoustic emission also indicates less movement of the sheet metal just before the time of fracture. The purpose of the conducted experiment was to find out whether the selected approach was appropriate. The obtained result shows unambiguously that the chosen approach is pertinent and that an AE-based monitoring system can be used as a process control system to ensure the quality of production of advanced thin plates in stainless steel and titanium. Information can be saved from the main forming operation and can be used to see trends both long and short term.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".