In discrete manufacturing the variation in process parameters and duration is often large. Common data storage and analytics systems primarily store data in univariate time series, and when analysing machine components of strongly varying lifetime and behaviour this causes a challenge. This paper presents a data structure and an analysis method for outlier detection which intends to deal with this challenge, as an alternative to predictive maintenance which often requires more data with higher quality than what is available. A case study in aluminium extrusion billet manufacturing is used to demonstrate the approach, predominantly detecting anomalies at the end of a critical component’s lifetime.

The concept of Digital Twins (DTs) can be utilized to solve complex problems in manufacturing based on the principle of Cyber-Physical Systems (CPS). While there are several reference architectures for CPS, there seems to be a knowledge gap between such high-level outlines and actual shopfloor-level implementations. This paper focuses on process control applications of the DT concept and proposes a specific implementation setup using fieldbus communication with a computer, aiming to fulfil a set of defined requirements. The setup is tested in an industrial use-case and the resulting characteristics of the solution are presented. A resulting assertion is that a DT of a process must be as specialized and customized as the system controlling it.

The main interface in human-machine interaction is identification and visualization technologies. In the future, more automated data analysis is needed so that data can be continuously aggregated, so that value added information is produced and that knowledge is extracted from this information that can improve the productivity and the quality of a product. Visualization of real-time production data in a user-friendly manner is, more specifically, a basis for decision-making for overall equipment losses. This research is based on a case study which aims to provide insight into how to successfully increase useful and user-friendly digital decision-making human-machine interaction support that can enable the improvement of overall equipment efficiency.

Robots and in-process inspection systems equipped with machine vision solutions are used for increased flexibility and quality in automated manufacturing. Although vision systems have found wide industrial use, there are still problems regarding optimization of vision system robustness and capabilities. This paper presents a comprehensive case study of vision system functions, techniques and capabilities in an automotive 1-tiers supplier. Based on the study, the paper further describes a method for systematic improvement of industrial vision systems on a continuous basis. This is proposed to be done by establishing a data store and data analysis system, based on training machine learning models in an off-line mode using the historical data, as well as on on-line stream processing.

The use of vision systems for industrial robot guidance and quality control becomes much harder when the manufactured products and their components are small and possess reflective surface. To assure an effective automated visual inspection of such components, novel solutions are required, able to perform more advanced image analysis and tackle noise and uncertainty. This paper proposes a concept of multi-camera/multi-pose inspection station for star washers inspection, and presents the first results of a functional prototype implementation of it in a robotic cell. The processes of vision-guided part picking from a flexible feeder and close-range inspection in a dedicated rig are described. Solutions for the vision-based tasks of parts identification, machine learning-based classification, circular objects image analysis and star washer teeth segmentation are presented, and further directions are outlined.