Quality forecasts in manufacturing using autoregressive models

Open Access
Article
Conference Proceedings
Authors: Jan MayerRoland Jochem

Abstract: Companies in the manufacturing industry are facing a variety of challenges such as increasing product complexity and variety and the accompanying complexity of production processes. The developments for more sustainability and optimized use of resources are additional societal requirements. Consequently, the demands of efficient solutions in quality management are also increasing. Innovative processes are needed to meet industrial challenges. Therefore, enhanced availability of data in production offers an opportunity. Hence, the combination of associated process and manufacturing knowledge and data availability creates the possibility to improve product- and process-related quality as well as the use of resources. As a consequence, machine learning methods are used to utilize and evaluate the collected data volumes. Their application in quality control enables the operation of smart solutions like the detection of anomalies in both product and process quality. However, there is no standardized algorithm to implement in any desired production environment. Conclusively, the application of specific algorithms is highly dependent on the desired project output, human factors and the underlying infrastructure. As main manufacturing branch, mass production combines the potential benefits of machine learning applications and their occurring challenges for product and process and monitoring. Existing reporting tools like the statistical process control (SPC) enhance process owners to continuously monitor manufactured products and processes. Nonetheless, the execution of the SPC is naturally reactive, once the monitored products have been already produced. Thus, process owners require a proactive, user friendly and interactive forecast application regarding their product and process quality.Predictive quality control is one way of improving product- and process-related quality while taking advantage of greater data availability. It represents an implementation of quality control in conjunction with data-driven quality forecasting. This application enables companies to conduct data-driven forecasts of product- and process-related quality. The aim is to use machine predictions as a basis for decision-making for action measures to be derived by the user. On the basis of the large amounts of data and algorithmic evaluation, measures can be derived by process and utilization investigations. Among other things, future events with influence on the quality can be controlled in an improved way. In quality management, decision-making processes are based on extensive data collection and analysis. Predictive quality should be seen as a supplement to conventional methods, e.g. SPC.Convenient implementation methods are key to achieve effective quality monitoring in terms of product and process control. For this reason, automated machine learning can be used to ease the realization of forecasting methods. Specifically, autoregressive models are robust and optimized statistical methods which fit to both forecasts of product and process quality. An observed evaluation metric like the mean absolute error for the next ten forecast items has been decreased by more than 50% from 0.141 to 0.66 with an underlying data range from 0.38 to 1.998. Since this calculation was processed including a univariate feature vector, improvements can be achieved by adding connected features, i.e. sensor data, for a higher accuracy in the forecasting results.

Keywords: Predictive Quality Control, Machine Learning, Forecasting, Quality Management, Statistical Process Control

DOI: 10.54941/ahfe1002848

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