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Laser wire-feed metal additive manufacturing (LWAM) is an innovative technology that shows many advantages compared with traditional manufacturing approaches. Despite these advantages, its industrial adoption is limited by complex parameter management and inconsistent process quality. To address these issues and improve geometric accuracy, this study explores how process parameters influence bead geometry. We conducted a parameter study varying laser power, wire feed rate, traverse speed, and welding angle. Using a full factorial design with a central composite design methodology, we assessed bead height and width. This allowed us to develop a model to estimate ideal process parameters. The findings offer a detailed analysis of parameter interactions and their effects on bead geometry, aiming to enhance geometric accuracy and process stability in LWAM. Moreover, we have evaluated the proposed process parameters from our developed model, which showed a significant enhancement to the overall quality. This was validated via printing a single layer and multi-layer structures. The quality of the final predicted sample using the proposed method was improved by 40% compared to the best sample produced for the Design of Experiment trials.

Laser beam welding of miscellaneous material combinations is an effective joining technology useful for diverse industrial applications because it can provide high speed, flexibility, and precision. However, welding defects like solidification cracking are some of the challenges in the joining process. The past decade has seen an extended effort to deal with this issue in many studies. However, there remains to be more comprehensive research regarding preventive procedures for solidification cracking by changing the grain structure. Following a thorough understanding of the solidification crack mechanism theories, we reviewed recent research on the critical role of metallurgical factors in the solidification cracks during laser welding. It considers the influence of the grain structure, intermetallic compounds, and laser welding parameters to propose preventive procedures to suppress the solidification cracks. Recent achievements show grain refiners, laser beam oscillation, ultrasonic vibration, and implementation of double laser sources are the main strategies that suppress or minimize solidification cracks. Furthermore, in laser beam welding of dissimilar materials, like steel-hard metal and copper-aluminum, brittle intermetallic compounds are recognized as one of the main reasons for the solidification crack susceptible increment. Recent approaches to overcome the formation or reduce the number of intermetallic compounds through various laser parameters and setups are discussed.

Laser joining of polymers to metals is a rising research subject due to the potential of considerably reducing the weight of structures. This article deals with the laser joining process between polypropylene and aluminum. Without pre-treatment, laser joining of these materials is not feasible, and the method applied in this study to circumvent this issue is a surface modification of aluminum with a pulsed laser to create mechanical interlocking for the heat conduction laser joining technique. Different patterns and various laser parameters are analyzed with the design of experiments to best understand the effects of each parameter along with microscopic observations. It is found that engraving weakens the mechanical properties of the aluminum samples. The compromise between the engraving depth and the mechanical properties of the samples is optimized, and the engraving process with a 0.28 mm line width, 27.3% density and 150 mm/s speed provides the highest mechanical performance of the assembly with minimum degradation of aluminum samples. Moreover, by adjusting the laser power and using power modulation below 300 W, the decomposition of polypropylene occurring at high temperatures is reduced to a minimum. After the final optimization, the joined samples reliably withstand a maximum force of 1500 N, which is, approximately, a shear strength of 20 MPa.

There has been a rapid increase in the use of collaborative robots in manufacturing industries within the context of Industry 4.0 and smart factories. The existing human–robot interactions, simulations, and robot programming methods do not fit into these fast-paced technological advances as they are time-consuming, require engineering expertise, waste a lot of time in programming and the interaction is not trivial for non-expert operators. To tackle these challenges, we propose a digital twin (DT) approach for human–robot interactions (HRIs) in hybrid teams in this paper. We achieved this using Industry 4.0 enabling technologies, such as mixed reality, the Internet of Things, collaborative robots, and artificial intelligence. We present a use case scenario of the proposed method using Microsoft Hololens 2 and KUKA IIWA collaborative robot. The obtained results indicated that it is possible to achieve efficient human–robot interactions using these advanced technologies, even with operators who have not been trained in programming. The proposed method has further benefits, such as real-time simulation in natural environments and flexible system integration to incorporate new devices (e.g., robots or software capabilities).

Improving the vibration isolation for the seat of small vehicles under low excitation frequencies is important for providing good comfort for the driver and passengers. Thus, in this study, a compact, low-dynamic, and high-static stiffness vibration isolation system has been designed. A theoretical analysis of the proposed quasi-zero stiffness (QZS) isolator system for vehicle seats is presented. The isolator consists of two oblique springs and a vertical spring to support the load and to achieve quasi-zero stiffness at the equilibrium position. To support any additional load above the supported weight, a sleeve air spring is used. Furthermore, the two oblique springs are equipped with a horizontal adjustment mechanism that is aimed to reach higher frequencies with the existed stroke when a heavy load is applied. The proposed system can be fitted for small vehicles, especially for B-segment and C-segment cars. Finally, the simulation results reveal that the proposed system has a large isolation frequency range compared to that of the linear isolator.

In Laser Wire Additive Manufacturing (LWAM), the final geometry is produced using the layer-by-layer deposition (beads principle). To achieve good geometrical accuracy in the final product, proper implementation of the bead geometry is essential. For this reason, the paper focuses on this process and proposes a layer geometry (width and height) prediction model to improve deposition accuracy. More specifically, a machine learning regression algorithm is applied on several experimental data to predict the bead geometry across layers. Furthermore, a neural network-based approach was used to study the influence of different deposition parameters, namely laser power, wire-feed rate and travel speed on bead geometry. To validate the effectiveness of the proposed approach, a test split validation strategy was applied to train and validate the machine learning models. The results show a particular evolutionary trend and confirm that the process parameters have a direct influence on the bead geometry, and so, too, on the final part. Several deposition parameters have been found to obtain an accurate prediction model with low errors and good layer deposition. Finally, this study indicates that the machine learning approach can efficiently be used to predict the bead geometry and could help later in designing a proper controller in the LWAM process.

The concept of the circular economy (CE) is receiving encouraging attention among scholars and practitioners, as a convenient solution to move away from the linear economy concept without neglecting the goals of sustainable development. The main goals of the CE are the closing of resource loops and the keeping of resources in the system for as long as possible at the highest utility level. However, as a result of the lack of internationally accepted definitions of the CE and several unsolved barriers, an excessive and inconsistent number of different CE applications exist. Most fields are mainly focusing on making a linear system circular instead of applying the CE principles in a holistic way. This paper presents a strategy to close the mentioned inconsistency gap, by contrasting currently discussed CE barriers and goals and thereof deriving two areas with a need for action (1. identifying the needed collection, sorting, and recovery infrastructure, and 2. developing circular product design guidelines). The strategy itself consists of connecting these two areas through an improved information exchange between the end-of-life (EOL) and beginning-of-life (BOL) of products. The result is CE design guidelines which are in accordance with the available or needed collection, sorting, and recovery infrastructure. The proposed strategy presents an innovative solution to apply CE principles in a holistic manner, based on EOL-driven product design.

The manufacturing sector is under constant pressure to increase profitability in a growingly competitive international market in which differentiation is not tied to manufactured products or utilized technologies but to business processes optimization. In this context, business analytics offer the opportunity to harness the knowledge and value hidden within enterprise information systems to revolutionize innovation, enhance supply chain management and production, accurately target marketing and sales efforts, as well as develop and manage profitable after-sales services. While the literature to date presents numerous specific applications in which business analytics techniques were successfully deployed to improve specific business units, it is evident that a comprehensive enterprise approach is missing. In the present work, a pathway to attain market leadership through the effective use of business analytics is defined suggesting focus must center on three increasingly challenging barriers. Firstly, “standardization” of collection, aggregation and storage of data must be accomplished. Then, an “organizational culture evolution” that outgrows intuition and embraces data-driven decision-making is needed to create the perfect ecosystem for business analytics to produce actionable results and recommendations. In turn, these must guide “business model innovation” efforts to tackle new value creation, and capture and secure market leadership. Keywords: Manufacturing, Business analytics, Big data, Industry 4.0, MSC: 00-01, 99-00

Information entropy metrics have been applied to a wide range of problems that were abstracted as complex networks. This growing body of research is scattered in multiple disciplines, which makes it difficult to identify available metrics and understand the context in which they are applicable. In this work, a narrative literature review of information entropy metrics for complex networks is conducted following the PRISMA guidelines. Existing entropy metrics are classified according to three different criteria: whether the metric provides a property of the graph or a graph component (such as the nodes), the chosen probability distribution, and the types of complex networks to which the metrics are applicable. Consequently, this work identifies the areas in need for further development aiming to guide future research efforts.