Energy harvesting has an essential role in the development of reliable devices for environmental wireless sensor networks (EWSN) in the Internet of Things (IoT), without considering the need to replace discharged batteries. Thermoelectric energy is a renewable energy source that can be exploited in order to efficiently charge a battery. The paper presents a simulation of an environment monitoring device powered by a thermoelectric generator (TEG) that harvests energy from the temperature difference between air and soil. The simulation represents a mathematical description of an EWSN, which consists of a sensor model powered by a DC/DC boost converter via a TEG and a load, which simulates data transmission, a control algorithm and data collection. The results section provides a detailed description of the harvested energy parameters and properties and their possibilities for use. The harvested energy allows supplying the load with an average power of 129.04 $\mathsf{\mu }$W and maximum power of 752.27 $\mathsf{\mu }$W. The first part of the results section examines the process of temperature differences and the daily amount of harvested energy. The second part of the results section provides a comprehensive analysis of various settings for the EWSN device’s operational period and sleep consumption. The study investigates the device’s number of operational cycles, quantity of energy used, discharge time, failures and overheads.

Perfectly coated surfaces are an essential quality feature in the automotive and consumer goods industries. They are the result of an optimized, controlled coating process. Because entire assemblies could be rejected if Out-of-Specification (OOS) parts are installed, this has a severe economic impact. This paper presents a novel, line-integrated multi-camera system with intelligent algorithms for anomaly detection on small KTL-coated aluminum parts. The system also aims to automatize the previously used human inspection to a sophisticated and automated vision system that efficiently detects defects and anomalies on coated parts.

Visual inspection often represents a bottleneck in the production chain due to limited receptivity and human fatigue. Depending on the inspection task, human classification error decisions greater than 10% are not uncommon. In order to make the manufacturing process more robust and sustainable, this paper presents innovative automated inline monitoring to realize a zero-error strategy in the field of industrial manufacturing. The central idea is to detect surface defects in the running production process by data fusion of a multi-camera system. Due to the required data analysis in conjunction with the targeted high throughput rates in production, this generates large amounts of data to process. Massively parallel hardware structures are used to enable defect detection within the production cycle. The paper especially addresses how to tackle boundary conditions in productive press-lines like the software-based mitigation of placement tolerances of parts and real-time capabilities. As a result, benefits could be achieved in terms of minimum detectable failure sizes and inspection speed, enabling 100% inline inspection of produced parts. The feasibility of the presented approach is demonstrated on realistic press-hardened deep-drawn automotive parts.

The production process grinding deals with finishing of hardened workpieces and is one of the last stages of the value-added production chain. Up to this process step, considerable costs and energy have been spent on the workpieces. In order to avoid production rejects, significant safety reserves are calculated according to the present state of the art. The authors introduce two approaches to minimize the safety margin, thus optimizing the process’ economic efficiency. Both control concepts use the feed rate override of the machining operation as regulating variable to eliminate thermal damage of the edge zone. The first control concept is developed to avoid thermal damage in cylindrical plunge grinding by controlling the cutting forces. Therefore, the industrial standard Open Platform Communications Unified Architecture (OPC-UA) is used for the communication between a proportional–integral–derivative (PID) controller and the SINUMERIK grinding machine tool control system. For noncircular workpieces, grinding conditions change over the circumference. Therefore, thermal damage cannot be ruled out at any time during the grinding process. The authors introduce a second novel control approach, which uses a micromagnetic measure that correlates with thermal damage as the main control variable. Hence, the cutting ability of the grinding wheel and thermal damage to the workpiece edge zone is quantified in the process. The result is a control concept for grinding of noncircular workpieces, which opens up fields for major efficiency enhancement. With these two approaches, grinding processes are raised on higher economic level, independently of circular and noncircular workpiece geometries.