Machine learning could sharpen factory quality control, study finds

<p>Is there room for improvement in your production process? Would it not be great if we could assign an algorithm to solve all those problems? Unfortunately, production processes tend to be complex and no algorithm that potent has been identified within the scope of this thesis. But maybe there are ways to use algorithms so that they can provide a bit of help.</p><p>Quality Management &amp; Quality Technology is the research area that for decades has worked on the continuous improvement of industrial and other processes. A cornerstone of the methods from Quality Management &amp; Quality Technology is to approach problems in these industrial and other processes in a scientific manner. A big part of this approach is to make decisions based on data: and thereby, statistical methods are an integrated part of the approach in order to understand process behavior. The statistical approach has proven to be very effective, but the specific methods used were developed during the 20th century, for the industry context of that time and within the technical abilities that then existed.</p><p>Machine learning (ML) is the concept of data-driven algorithms learning patterns to enable prediction, classification, and decision making. It has exploded as a research topic in recent years and, in the midst of the fourth industrial revolution, a belief is expressed that together with the rapidly growing amounts of industrial data, ML will drastically change industry by making the production processes increasingly efficient and supporting decision making, etc. The big issue is that it is hard to find how ML is to be applied in industry. Would it not make sense to combine the knowledge from the research area that has applied numerical methods and benefited industry for decades and combine that with the new type of numerical methods? In this thesis, the aim has been to do just that. In essence, how can you combine the traditional concepts and knowledge from Quality Management &amp; Quality Technology together with the more novel algorithms from ML? This thesis advances concept generation by assessing the use of statistics in Quality Technology. It highlights the limitations of traditional methodologies in data-intensive environments and proposes ML algorithms as more effective solutions for managing large datasets. Within Root Cause Analysis (RCA), the thesis proposes a specific approach that is useful in paperboard manufacturing. With appropriate adaptations depending on the specific technical case, it is likely that this could also benefit other process industries. Additionally, it explores how ML can enhance statistical process control, offering insights into its potential to deliver more precise guidance and operate effectively across different organizational levels.</p>