In the context of the European Green Deal, the manufacturing industry faces environmental challenges due to its high demand for electrical energy. Thus, measures for improving the energy efficiency or flexibility are applied to address this problem in the manufacturing industry. In order to quantify energy efficiency or flexibility potentials, it is often necessary to predict or forecast the energy consumption. This paper presents a systematic review of state-of-the-art of existing approaches to predict or forecast the energy consumption in the manufacturing industry. Seventy-two articles are classified according to the defined categories System Boundary, Modelling Technique, Modelling Focus, Modelling Horizon, Modelling Perspective, Modelling Purpose and Model Output. Based on the reviewed articles future research activities are derived.

Laser powder bed fusion (LPBF) has indisputable advantages when designing new components with complex geometries due to toolless manufacturing and the ability to manufacture components with undercuts. However, fatigue properties rely heavily on the surface condition. In this work, in-process surface parameters (three differing contour parameter sets) and post-process surface treatments, namely turning and shot peening, are varied to investigate the influence of each treatment on the resulting fatigue properties of LPBF-manufactured specimens of the aluminium–magnesium–scandium alloy Scalmalloy^®. Therefore, metallographic analysis and surface roughness measurements, as well as residual stress measurements, computer tomography measurements, SEM-analyses, tensile and fatigue tests, along with fracture surface analysis, were performed. Despite the fact that newly developed in-process contour parameters are able to reduce the surface roughness significantly, only a minor improvement in fatigue properties could be observed: Crack initiation is caused by sharp, microscopic notches at the surface in combination with high tensile residual stresses at the surface, which are present on all in-process contour parameter specimens. Specimens using contour parameters with high line energy show keyhole pores localized in the subsurface area, which have no effect on crack initiation. Contours with low line energy have a slightly positive effect on fatigue strength because less pores can be found at the surface and subsurface area, which even more greatly promotes an early crack initiation. The post-process parameter sets, turning and shot peening, both improve fatigue behaviour significantly: Turned specimens show lowest surface roughness, while, for shot peened specimens, the tensile residual stresses of the surface radially shifted from the surface towards the centre of the specimens, which counteracts the crack initiation at the surface.

Digital twins are among the technologies that are considered to have high potential. At the same time, there is no uniform understanding of what this technology means. Definitions are used across disciplinary boundaries, resulting in a multitude of different interpretations. The concepts behind the terms should be clearly named to transfer knowledge and bundle developments in digitalization. In particular, the Reference Architectural Model for Industry (RAMI) 4.0, as the guiding concept of digitalization, should be in harmony with the terms to be able to establish a contradiction-free relationship. This paper therefore summarizes the most important definitions and descriptions from the scientific community. By evaluating the relevant literature, a concept is derived. The concept presented in this work concretizes the requirements and understanding of digital twins in the frame of RAMI 4.0 with a focus on manufacturing. It thus contributes to the understanding of the technology. In this way, the concept is intended to contribute to the implementation of digital twins in this context.

Additive manufacturing, in particular the powder bed fusion of metals using a laser beam, has a wide range of possible technical applications. Especially for safety-critical applications, a quality assurance of the components is indispensable. However, time-consuming and costly quality assurance measures, such as computer tomography, represent a barrier for further industrial spreading. For this reason, alternative methods for process anomaly detection using process monitoring systems have been developed. However, the defect detection quality of current methods is limited, as single monitoring systems only detect specific process anomalies. Therefore, a new methodology to evaluate the data of multiple monitoring systems is derived using sensor data fusion. Focus was placed on the causes and the appearance of defects in different monitoring systems (photodiodes, on- and off-axis high-speed cameras, and thermography). Based on this, indicators representing characteristics of the process were developed to reduce the data. Finally, deterministic models for the data fusion within a monitoring system and between the monitoring systems were developed. The result was a defect detection of up to 92% of the melt track defects. The methodology was thus able to determine process anomalies and to evaluate the suitability of a specific process monitoring system for the defect detection.

Against the backdrop of the European Union’s commitment to achieve climate neutrality by 2050, efforts to improve energy efficiency are being intensified. The manufacturing industry is a key focal point of these endeavors due to its high final electrical energy demand while simultaneously facing a growing shortage of skilled workers crucial for meeting established goals. Expert systems (ESs) offer the chance to overcome this challenge by automatically identifying potential energy efficiency improvements, thereby playing a significant role in reducing electricity consumption. This paper systematically reviews state-of-the-art ES approaches aimed at improving energy efficiency in industry with a focus on manufacturing. The literature search yields 1668 results, of which 62 articles published between 1987 and 2024 are analyzed in depth. These publications are classified according to the system boundary, manufacturing type, application perspective, application purpose, ES type, and industry. Furthermore, we examine the structure, implementation, utilization, and development of ESs in this context. Through this analysis, this review reveals research gaps, pointing toward promising topics for future research.