---
pretty_name: Production Press Sensor Data
dataset_info:
- config_name: Decoy
  features:
  - name: dowel_deep_drawing_ow
    sequence: float64
    length: 4096
  - name: label
    dtype:
      class_label:
        names:
          '0': normal
          '1': defect
  - name: speed
    dtype: int32
  - name: mask
    sequence: int32
    length: 4096
  splits:
  - name: train
    num_bytes: 56735256
    num_examples: 1154
  - name: test
    num_bytes: 54572040
    num_examples: 1110
  download_size: 71727761
  dataset_size: 111307296
- config_name: Normal
  features:
  - name: dowel_deep_drawing_ow
    sequence: float64
    length: 4096
  - name: label
    dtype:
      class_label:
        names:
          '0': normal
          '1': defect
  - name: speed
    dtype: int32
  - name: mask
    sequence: int32
    length: 4096
  splits:
  - name: train
    num_bytes: 54375384
    num_examples: 1106
  - name: test
    num_bytes: 56931912
    num_examples: 1158
  download_size: 70358066
  dataset_size: 111307296
configs:
- config_name: Decoy
  data_files:
  - split: train
    path: Decoy/train-*
  - split: test
    path: Decoy/test-*
- config_name: Normal
  data_files:
  - split: train
    path: Normal/train-*
  - split: test
    path: Normal/test-*
license: cc-by-4.0
size_categories:
- 10K<n<100K
task_categories:
- tabular-classification
---

# Production Press Sensor Data (P2S)

## Dataset Description

- **Paper** https://arxiv.org/abs/2402.12921
- **Point of Contact:** antonia.wuest@tu-darmstadt.de

### Dataset Summary
In this data set we provide sensor recordings of a part production process which contains a speed-based decoy which needs to be overcome. The production of parts in multiple progressive forming stages using stamping, deep drawing and bending operations with progressive dies is generally one of the most economically significant manufacturing processes in the sheet metal working industry and enables the production of complex parts on short process routes with consistent quality. This dataset contains sensor recordings of that process. The recording of the internal forces are provided as time series and based on these the task is to detect whether a run of the press resulted in a normal or faulty metal part. The sensor data has been recorded with different production speeds. The run speed does not affect whether the result in normal or faulty, but it changes the sensor recordings. The speed of a run can be a confounding factor, distracting models from the relevant data charateristics.

In addition to the sensor data, this dataset contains annotations on which parts of the sensor data are mostly influenced by different run speeds, i.e. which parts potentially confound a model. These annotations can be used to evaluate whether a model uses the confounders, and even to revise them. 


## Dataset Structure

The data was generated in 12 different experiments, over 6 different speeds, with either normal or faulty outcomes. 
Each sample includes: the sensor data (4096 steps), a binary annotation mask (4096 steps), the run speed and the label (normal/defect). 


| Experiment | Outcome | Speed | Samples |
| --- | --- | --- | --- |
| 1  | Normal | 80  | 193 |
| 2  | Normal | 100 | 193 |
| 3  | Normal | 150 | 189 |
| 4  | Normal | 175 | 198 |
| 5  | Normal | 200 | 194 |
| 6  | Normal | 225 | 188 |
| 7  | Defect | 80  | 149 |
| 8  | Defect | 100 | 193 |
| 9  | Defect | 150 | 188 |
| 10 | Defect | 175 | 196 |
| 11 | Defect | 200 | 193 |
| 12 | Defect | 225 | 190 |
| Total | | | 2264 |

The following plot shows four example data points from the dataset including annotations. 
Experiments of different speeds alter the time series by a lot, while the difference between normal and defect runs are more subtle.
<img src="p2s_data.png" alt="p2s_data" width="600"/>

### Data Splits

The dataset has two variants, each with a train and test split. In the normal variant both normal and defect experiments of the same speed are in training and test.
This should ensure that a model learns to ignore the effects of speed in the sensor recordings. In the decoy settings on the other hand, there are experiments with normal
and defect outcomes of different speeds in the training data. In this case, the run speed represents a spurious correlation in the data. 
Both splits in both variants have roughly the same amount of normal and defect samples.


| Variant | Split | Size | Experiments |
| --- | --- | --- | --- |
| Normal | Train | 1106 | 1, 3, 5, 7, 9, 11 |
| Normal | Test | 1158 | 2, 4, 6, 8, 10, 12 |
| Decoy | Train | 1154 | 1, 2, 3, 10, 11, 12 |
| Decoy | Test | 1110 | 4, 5, 6, 7, 8, 9 |



## Dataset Creation

### Sensor Recordings
The sensor recodrings stem from a four-stage progressive die, 
used on a Bruderer BSTA 810-145 high-speed press with varying stroke speed. The strip material to be processed 
is fed into the progressive die by a BSV300 servo feed unit, while the tools are not engaged. 
The part to be produced remains permanently connected to the sheet strip throughout the entire production run. 
The stroke height of the tool is 63 mm and the material feed per stroke is 60 mm.

The process has four stages: a pilot punching stage, a round
stamping stage, deep drawing and a cut-out stage. In the first
stage, a 3 mm hole is punched in the metal strip. This hole
is used by guide pins in the subsequent stages to position
the metal strip. During the stroke movement, the pilot pin
always engages in the pilot hole first, thus ensuring the
positioning accuracy of the components. In the subsequent
stage, a circular blank is cut into the sheet metal strip. This
is necessary so that the part can be deep-drawn directly from
the sheet metal strip. This is a round geometry that forms
small arms in the subsequent deep-drawing step that hold
the component on the metal strip. In the final stage, the
component is then separated from the sheet metal strip and
the process cycle is completed. The respective process steps
are performed simultaneously so that each stage carries
out its respective process with each stroke and therefore a
part is produced with each stroke. Sensor recordings are provided for the deep drawing stage.

An indirect piezoelectric force sensor (Kistler 9240A) was used for data acquisition. The sensor is located
directly above the punch and records not only the indirect
process force but also the blank holder forces which are
applied by spring assemblies between the upper mounting
plate and the blank holder plate. The data is recorded at
a sampling frequency of 25 kHz. The material used is
DC04 with a width of 50 mm and a thickness of 0.5 mm.
The voltage signals from the sensors are digitised using a
”CompactRIO” (NI cRIO 9047) with integrated NI 9215
measuring module (analogue voltage input ± 10 V). Data
recording is started via an inductive proximity switch when
the press ram passes below a defined stroke height during
the stroke movement and is stopped again as it passes the
inductive proximity switch during the return stroke move-
ment. Due to the varying process speed caused by the stroke
speeds that have been set, the recorded time series have a
different number of data points. Further, there are slight
variations in the length of the time series withing one experiment. 
For this reason, all time series are interpolated to a
length of 4096 data points and we discard any time series
that deviate considerably from the mean length of a run (i.e.,
threshold of 3). To simulate a defect, the spring hardness of the
blank holder was manipulated in the test series that were
marked as defect. The manipulated experiments result in
the component bursting and tearing during production. In a
real production environment, this would lead directly to the
parts being rejected.

### Annotations

For each sample, the dataset contains a binary annotation mask. With expert information, several intervals in the process have been identified, 
which are the most affected by the run speed. The annotation mask marks these intervals, and thus the part of the time series which are affected 
when the run speed presents a spurious correlation. However, the mask does not cover all time points which are affected by the speed, but only the
most important intervals.

### Personal and Sensitive Information

The dataset does not contain personal nor sensitive information.


## Considerations for Using the Data

### Social Impact of Dataset

The dataset has no social impact.

### Known Limitations

While the annotations cover intervals of the time series which are the most affected by the run speed, they do not mark all of its influence. 
Overall, the run speed affects the time series in its total and it is difficult, if not impossible to completely locate and separate the effect of speed.

## Citation Information
If you use the dataset, please consider citing the accompanying paper:
@misc{kraus2024right,
      title={Right on Time: Revising Time Series Models by Constraining their Explanations}, 
      author={Maurice Kraus and David Steinmann and Antonia Wüst and Andre Kokozinski and Kristian Kersting},
      year={2024},
      eprint={2402.12921},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
## Contributions
Maurice Kraus, David Steinmann, Antonia Wüst, Andre Kokozinski, Kristian Kersting.

The data comes from the Priority Program (SPP)
2422 in the subproject “Optimization of active surface design
of high-speed progressive tools using machine and deep
learning algorithms“ funded by the German Research Foundation (DFG).