### Introduction

This report outlines a data dissemination and analysis exercise carried out by
LOE working with Nottingham Trent University (NTU). As part of the funding of
the RADICLE project it is intended that weld signal data generated should
become publicly available for wider learning and understanding. NTU used
internal funding to enable 3 of their staff to work with LOE on behalf of
RADICLE to investigate the limitations of the data formats and how to make it
accessible for wider sharing.

### Work outline

LOE have worked with Nottingham Trent University (NTU) to explore how the open
access data set can be analysed by third parties to the RADICLE project. At
EWF’s suggestion, TWI undertook a set of welding experiments on steel S355 as
this a conventional material with little proprietary knowledge involved, but
of much wider interest than some of the specialised aerospace grade materials
welded for end users.

Lecturer Dr Georgina Cosma and Research Associates Tolulope Oluwafemi and
Sadegh Mousaabadi Salesi were given a set of photo diode, acoustic, LDD and
camera data recorded during the welding. Weld quality data was provided in the
form of x-rays of the welded part and a ‘traffic light’ status of the weld.

Due to the limited time available it was decided that rather than try to make
a feedback process, a more limited quality prediction tool would be
considered. This allows the computational algorithm more time to analyse the
data it is fed so that less optimisation is required. As a result, the only
outcome is a quality prediction, rather than a specific defect location. Each
of the welds had one of three statuses: good, questionable or bad.

The first observation was that the data set was very limited in size making it
difficult to sufficiently train a machine learning algorithm. A number of the
data recordings were incomplete or had other issues, meaning that just 8 welds
were suitable for use. None of those considered useable had the questionable
status. As a result, classification and prediction of the data set can only
produce 2 outcomes a positive or negative status. This means that very high
statistical predictions are achieved, a success which would not be replicated
with a much larger and more representative data set.

In order to be able to make any predictions about a data set it is first
necessary to ‘train’ a model and to prepare the data. Preparing the data
includes stages such as defining the start and end point to remove any ‘non-
signal’ data and aligning the different signal types. LOE provided x-ray data
produced by TWI in image form and in normalised numerical form which required
development of an image processing tool.

Training the model then requires the data set to be classified. A number of
these methods are available and are detailed in the NTU reports attached.
Clustering the datasets breaks down the raw data into computer defined
significant features. The number of these clusters varies and the derivation
of them is statistically derived. Using the best approach tested, it was found
that 3 clusters showed the best quality indicator for the available data. If
the majority of data points are in Cluster 1, then the quality is ‘bad’. If
Clusters 2 and 3 are bigger, then the quality is ‘good’. Due to the limited
data set it has not been determined if there is more meaning that could be
derived from clusters 2 and 3.

Once the clustering has been performed, the true machine learning can be
applied. Again a number of algorithms can be applied. These can be tested by
removing the quality indicator and processing one of the data sets. The
algorithm then compares it to the datasets for which it has a quality status
and looks for similarities. By matching it to the closest data set, a
predicted quality status can be generated. In testing, a number of algorithms
showed very similar and very good performance. This is misleading as the data
set is very small and the range of statuses tested binary. The prediction made
is therefore binary; anything other than a near 100% prediction would be
closer to guess work than a statistical indictor.

### Future work

To aid wider use of the data set annotations will need to be made to explain
the data. At present the start and end points of the weld are observed
manually: for wider use there needs to be an explanation of where these occur
and how to identify them. This is complicated by the length of path travelled
while the beam is on being different to the heat affected area: the keyhole
was a width which extends beyond the nominal point of incidence of the beam.

A major challenge that still remains is to align a higher resolution status
with the signal data. This means converting the resolution of an x-ray,
typically 600 pixels (for a 70mm weld), to the same resolution as the video
data (3000) and photodiode data (180,000) points. To achieve this,
interpolation is required. It also requires interpretation of the x-ray to
define acceptable pores or clusters and bad areas for each defect category.
While x-rays showing the presence of low density areas was available, the
cause of these was not available during this study.

For dissemination purposes, the method of sharing the data remains a
challenge. A typical set of signal data generated by the LOE system equates to
around 100mB per weld. The Permanova seam tracking video potentially adds
several hundred mB. In addition to this the quality data, X-ray, CT scans and
surface profiles add significantly more data, especially in their raw form.
Sharing this data on physical memory or the server time and space to make it
available on a ‘sharepoint’ ftp site are costs which have not been factored in
to this aim.

This study has simply attempted to find a way to predict global quality of a
weld. To turn this into a system which can predict the cause of a change
quality and make a process parameter change to correct it is a significantly
harder challenge. This work suggests that there is useful data in all the
signals generated which allows quality predictions to be made. This provides
evidence that the signals gathered could, in theory, allow real time control
and analysis to be achieved.

LOE would like to thank VTT for help critiquing NTUs work and for helping
provide feedback to direct the study.