Qualitätsprüfung

The complex dynamics of block warehouses pose major challenges to the manual stocktaking process. Frequent relocation of pallets, crates or pallet cages without fixed storage locations leads to a time-consuming and error-prone inventory process, wherein goods often have to be searched for and damages due to improper storage can occur. The use of (semi-)autonomous drones offers a promising solution to enable automated stocktaking, especially if these are appropriately equipped for optical goods detection.

While the digital world has already made its way into many other production areas, the bakery industry has so far benefited little from these technological developments. This is because many baking processes are manually controlled and rely on expert knowledge. In addition, the process data from the machines is still not automatically captured via sensors. This paper describes a procedure for digitizing baking processes by developing an IoT framework consisting of an IoT device including measurement methods, an edge gateway, and a simulation-based solution for process optimization.

Customers are increasingly demanding electronic components with high quality, which forces companies to continuously fulfil these requirements. This leads to a high number of inspection gates with high inspection severity and a high number of pseudo defects. Double inspections by process experts reduce these defects but generate high inspection costs. Autonomously acting inspection systems meet this challenge. Within this article, a machine learning algorithm was integrated into the solder paste inspection process to form an autonomous quality inspection system.

Visual inspections of product surfaces are predominantly carried out by employees, whereby automation approaches with camera and image processing systems show great potential. Cobots are also being incorporated into quality assurance processes. In the following, the integration possibilities of cobots in visual inspection are discussed and a decision model is presented that can be used to check visual inspection processes for their cobot suitability. The decision model is designed for direct integration into already existing cobot suitability inspection processes and serves as an initial strategic decision-making aid.

Optical systems are a popular choice for quality inspection because they are not only contactless and precise but also comparably fast. Particularly in cases where a 100% quality inspection is required low measurement and evaluation time is of essential importance. With high production rates of several parts per second, manual inspection is not feasible anymore and the evaluation needs to be automatically carried out by algorithms. In this article we propose a fast method for anomaly detection in image data based on principal component analysis and filtering. The method shows competitive performance on a data set of challenging surface inspection.

As manufacturing technologies are scaled down from macro to micro level, unexpec-ted process behavior emerges, so called size effects. This also regards reliable optical quality inspection, as tolerances in micrometer range require microscopic solutions. Optical magnification comes with a strong reduction in field of depth as well as a decrease in the measured area, which makes exact positioning and possibly measurements at different depth levels necessary. Here we present and evaluate a demonstrator platform for the automatic measurement of micro parts using a light field camera.

Due to the increased product miniaturization, a number of new applications and market opportunities open up for mechanical micro-manufacturing. In the manufacturing process with part dimensions less than one millimeter and tolerances in the micrometer range occur so-called “size effects”. These prevent a simple scaling of processes known methods from the macro level and lead to an increased occurrence of quality deviations. In conclusion, the process capability according to ISO 21747 is affected and therefore the application of statistical process control (SPC) is more difficult. In this paper, the interaction between technical and logistical quality objectives in terms of logistical quality control are analyzed at the example of micro cold forming. Thereby, methods of statistical process control and fuzzy control are used.

The article describes the quality inspection of micro cold formed components in the collaborative research center 747 “micro cold forming” at the University of Bremen. These components, e.g. small springs in cell phones, are manufactured with a speed of up to 300 parts per minute in a micro cold forming machine. Overall dimensions are below 1mm. These dimensions lie in a field between tactile measuring techniques of form and position and surface metrology, e.g. roughness. Yet a high inspection rate is impossible due to problems in production engineering and metrology. Therefore, fast optical metrology, new evaluation methods and quality control is needed to ensure an economical manufacturing process for micro cold formed parts.