Rapid technological advances in recent years have established Big Data, Industrie 4.0, Internet of Things (IoT) and Artificial Intelligence (AI) as data-driven concepts that are increasingly being implemented and proving value in real-world scenarios, transforming the future across all sectors—including manufacturing. Especially in the area of industrial automation, these fast-paced and transformative changes collide with a long-established industry. Despite the current state-of-the-art in technology, the lifetime of equipment is measured in decades— with the need to provide long-term support even to outdated (legacy) systems in heterogeneous production environments. Incorporating changes in such systems is challenging and mostly driven by software due to its adaptability. This paper contributes a generalized, lightweight approach for integrating machine-level data collection and analysis with agent-based systems, aiming to facilitate the adoption of CPPS in the industry and easing the integration with legacy control systems. As a use case for production performance optimization, the monitoring and detection of outliers for interconnected transportation systems has been defined. A key feature of the approach is that it utilizes only data from presence sensors. Thus, only minimal information about the system and no further configuration or development is required while maintaining full functionality. The generalizability and transferability of this approach have been validated using two representative laboratory production systems, demonstrating the potential of PLC-based data (pre-) processing and its scalability to multiple IEC 61131-3 conform control applications.

A Diaphragm Wall Hydraulic Grab (DWHG), used in civil engineering for bulk excavation, is featured with a cable-suspended end-effector (also named attachment tool). The end-effector begins to oscillate during DWHG operation. Essential insights into the DWHG’s performance or productivity in operation could be derived from end-effector oscillations. However, due to limited robustness, the end-effector oscillation curves from a DWHG in operation can not be tracked directly by motion sensors attached to the end-effector. This article presents and evaluates a novel approach for DWHGs to infer cable-suspended end-effector oscillations from their hydraulic actuators’ responses. Hydraulic actuators’ responses depict the changes of hydraulic parameters in hydraulic actuators due to end-effector oscillations. The main contribution of this article is an investigation of how far oscillations of a cable-suspended end-effector in a DWHG can be inferred from their hydraulic actuators’ responses. For this purpose, a workflow is conducted: firstly, end-effector oscillations and their hydraulic actuators’ responses are collected for a typical DWHG steering sequence. Secondly, the collected end-effector oscillation and hydraulic actuators’ response curves are analyzed and processed. Thirdly, a model to infer end-effector oscillations from hydraulic actuators’ responses is built. Fourthly, an evaluation shows that the inferred oscillation curves (model output) are similar to the true end-effector oscillationsNote to Practitioners—This article is motivated by the demand to determine the oscillation curves of the cable-suspended end-effector (also named attachment tool) at a Diaphragm Wall Hydraulic Grab (DWHG) used in civil engineering for bulk excavation. Existing approaches for construction machines commonly install motion sensors at the end-effector to track its oscillations. However, mounting any sensors at the end-effector in DWHGs is not practicable because the end-effector is exposed to mechanical shocks and moisture. Thus, mounted sensors would be damaged or demolished. This article suggests and investigates a novel approach for DWHGs to infer end-effector oscillations from pressure data in hydraulic actuators that actuate the end-effector. Therefore, the pressure curves at the hydraulic actuators are tracked while the end-effector oscillates and are utilized afterward to infer these oscillations. A practical guide for a DWHG is presented and evaluated to collect, analyze, process, and utilize pressure data from hydraulic actuators to infer end-effector oscillations.

The rising demand for customized products necessitates the integration of multiple robotic systems, underscoring the need for advanced production planning and scheduling. This paper introduces an ontology-based, artificial intelligence-enhanced method for dynamic task planning and scheduling, aimed at improving the efficiency of production process, reducing machine downtime, and consequently increasing throughput in assembly operations. Designed to generate and execute feasible production plans dynamically, this method minimizes manual planning and scheduling efforts. We evaluate its effectiveness using two gear assembly use cases with various robot skills, highlighting its flexibility in planning and scheduling and its contributions to the evolution of smart manufacturing. The method’s adaptability suggests its applicability across diverse smart factory environments.

The usage of cloud applications in industrial automation domain is boosted by the desire to apply computational-intensive machine-learning (ML) models and access production data from all over the world. Accomplished by the demand for powerful hardware platforms for ML and centralized data storage, the connectivity performance also increases and allows high data transmission rates. Nevertheless, requirements for data transmission, like safety, security, and real-time must be considered in line with the production use case. Finding a suitable infrastructure with communication protocols for data transmission to transform legacy production plants into cyber-physical production systems (CPPS) is overwhelming due to the many available communication methods. Design support for CPPS would increase the acceptance of cloud-based solutions by engineers of automated production systems that are not specifically familiar with high-level information technology systems. This paper introduces an investigation and design recommendations for application-layer communication protocols usually used in industrial applications. The overall goal is to support the engineering for the different automation levels on field-, edge-, and cloud-level. In this paper, design measures are firstly derived from related work. Secondly, an analysis of the timing behavior and the CPU resources are carried out. Finally, the findings are collected in a summarizing rating table that briefly suggests adjusting the performance and the impact on the overall system design by selecting suitable communication protocols.

One of the limitations of classical SysML is that it does not provide information on how to model system component's constraints, which can result in individual model solutions for the same elements. To address this issue, we propose utilizing the reference mechanism in SysML to refer to REXS and ECLASS data standards, which can unambiguously define characteristic properties. Environmental factors such as temperature or humidity constraints play a vital role in designing components of mechatronic systems, like transmissions, or electrical and electronic components like sensors and actuators. Mechatronic component's properties may change depending on temperature and humidity and may show degradation. Such information is provided to some extent in the component's classified properties, like ECLASS and/or REXS, the engineering tool to configure gears. Such properties and constraints are further detailed in specification sheets provided by the manufacturer, e.g., by multi-dimensional manufacturer-specific parametric curves which are proposed to be explicitly included in the modeling process as SysML constraints. The proposed approach allows to include additional, not yet standardized environmental effects such as dirt layers in a formalized way. The formalized properties are modeled in parametric diagrams of SysML.

Mobile and stationary mechatronic systems are often driven by hydraulic actuators which are controlled using valves. Hydraulic systems have a high power density, are robust and cost-effective in the application and can work reliably under rough environmental conditions. For many years, PID controllers build the state-of-the-technology for valve control. Although PID controllers are easy to synthesize and can be executed on low-performing devices, the demand for more adaptive control approaches to deal with non-linearities in hydraulic circuits is increasing. Adaptive controllers tend to be more computationally expensive than PID controllers, so the capability of common hardware devices to execute the algorithms in real-time must also be considered. This paper contributes a feasibility study examining whether adaptive control approaches can be used for valve control in hydraulic-driven mobile machines. Thus, a literature review on adaptive valve control approaches is conducted and a model-based linear adaptive control approach consisting of system identification and control logic execution is extracted. The adaptive controller is applied to a hydraulic testbed and the controller performance is measured in dependence on the cycle time of the controllers to estimate the worst-case execution time. It is investigated whether common control units in mobile machines can reach the execution times and how many valves can be controlled by one control unit in terms of the execution time.

Modern Cyber-Physical Production Systems get more and more intelligent by higher capacities of the used resources and more resource-efficient AI-algorithms. However, a significant challenge is finding the fitting architecture for hardware and software cost-efficiently and with low effort. Currently, this process consists of trial and error or selecting overpowered hardware resources, which leads to expensive and time-consuming processes in the development. This paper deals with a quantitative benchmark of the timing behavior of selected algorithms for preprocessing to enable AI on representative hardware platforms in cyber-physical production systems, building on previous approaches that take a model-based view of hardware/software co-design. This approach is a first step away from a purely qualitative system design, towards a quantitative approach.

Intelligent algorithms and learning are the basis for evolving smart cyber-physical production systems (CPPSs) and Industrie 4.0. A smart image detection algorithm shall be added to reduce downtime due to the extended operation of glass bottles in a yogurt producing plant. To support the engineers in doing so, a comprehensive domain-specific language (DSL), DSL4hDNCS, is introduced, enabling rapid analysis, addressing hardware/software architectures and network-related delays and uncertainties. DSL4hDNCS is defined by a metamodel to avoid ambiguity and enriched by aspects, such as safety, calculation power, and network transmission time. DSL4hDNCS is used to compare (re)deployment alternatives using different technologies, such as edge, fog, and cloud to implement the additional smart algorithm. The evaluation of DSL4hDNCS using an acknowledged Industrie 4.0 demonstrator plant as a case study confirmed the benefit for engineers during the redesign.