In today’s fast-paced market, companies are challenged to meet increasing customer demands and shorter product life cycles. To successfully respond to these demands, companies must produce a wide variety of different products. This requires the determination of necessary processes and resources for each product, which can be difficult for process engineers due to the high manual effort and expertise involved. The current state of research has not yet provided explicit definitions of the necessary knowledge and has not fully achieved complete process planning automation. To address this challenge, a digital twin is a valuable tool for automating and understanding process planning. This paper presents a digital twin concept for process planning. It automatically analyzes the product, determines production processes, and selects appropriate resources by linking information about products, resources, and processes. The effectiveness of the digital twin concept is demonstrated through verified and validated use cases, including the production of a compressor element.

Frequent changes in customer needs and large product variety are forcing manufacturing companies to move from mass production to mass customization. Customized production can be achieved by introducing reconfigurable production systems (RMS). The customized flexibility and several characteristics of RMSs provide many opportunities in terms of process and production planning. However, those characteristics greatly increase the complexity of the design and planning of production systems. This paper presents a decision support system relying on a skill-based approach to design a reconfigurable assembly line considering the planning of assembly processes and monitoring. The proposed decision aid system is modular in design and is composed of four modules. The main input data is a CAD model of a new product variant for the identification of the assembly and monitoring requirements. Besides, a current assembly system layout with its resource descriptions exists. In the first developed module, assembly-by-disassembly and a skill-based approach are used to generate different assembly plans. In the second module, feature recognition and skill-based approaches generate process monitoring alternatives. The third module uses a linear program (LP) that aims to minimize the total cost of workstation activation and reconfiguration, as well as cycle time, and to maximize the process quality of the assembly tasks. A user-based generative model design approach is applied to optimize the values of three objective functions. In the fourth and final module, a simulation of the optimized assembly plan allows either the validation of the assembly plan and process monitoring plan or initiates a new iteration due to their infeasibility. To further demonstrate how the proposed methodology works, some computational experiments are provided for two use cases.

Process understanding and process monitoring are of great importance in production in order to control processes and guarantee a high quality. Demanding customer requirements with an increasing number of variants pose an even greater challenge to the quality of the processes, as this must be maintained at the highest level even in the event of process changes. In addition, new regulations and standards require process data to be recorded and stored, especially in manufacturing environments for medical and safety equipment (e.g., surgical instruments, camera systems in the automotive industry). Continuous variations in production processes and changes to products and the production system mean that the planning effort required to implement process monitoring has become vast. This is where automated planning and decision support systems become important. They are able to manage the complexity arising from alternative solutions and present suitable alternatives to the user. This article deals with the computer-aided identification of assembly features, which influence process monitoring and the generation of production system-neutral tasks for process monitoring. Computer-aided feature recognition methods were used to derive features from three-dimensional models. Furthermore, a skill-based approach was used to formulate tasks for process monitoring. This publication thus aims at the automated and product-specific generation of processes for process monitoring.

The importance of process monitoring has increased significantly in today’s manufacturing landscape due to several factors. These factors include stricter quality standards, the presence of complex and diverse processes for customized products, and a focus on sustainability in energy and material consumption. In order to seamlessly integrate process monitoring into the production planning process, it is essential to automate the monitoring process planning while also taking into account production planning. Given the increasing importance of high-quality processes and products, assembly processes have become critical, as errors in assembly can lead to expensive production costs. However, generating and validating different process monitoring alternatives in assembly planning can be challenging because different processes and resources exist for different assembly plans. This research aims to solve this problem by providing a method to validate process monitoring alternatives more efficiently, thereby reducing the amount of manual effort and expert knowledge. This goal is achieved through automated validation using two simulation approaches: Collision Testing and Ray Casting. The results of this study are presented to demonstrate the effectiveness of the proposed solution.

Machine learning has been finding its way into industrial systems with use cases like predictive maintenance, process optimization, anomaly detection, and many more. Low data coverage limits the usability of these machine learning models to only a small fraction of the overall parameter space. To make predictive machine learning models feasible for industrial processes, it is important to collect relevant data to reduce epistemic uncertainty in all areas of the parameter space. Approaches from experimental design aim to patch areas with low data coverage and provide useful information for previously unknown process settings. These methods assume that the factor limits in the parameter space are known. However, the high-dimensional parameter spaces in industrial processes with complex factor interdependencies make it difficult to find the dynamic physical limits of the system. Therefore, it is hard to define a set of experiments that are physically possible to reach. We provide a novel method to explore these high-dimensional parameter spaces. Through in-distribution data projection and out-of-distribution predictions with autoencoders, we estimate the physical limits of the parameter space of industrial processes.