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Understanding the relationship between injection molding parameters and the acoustic properties of polymers is crucial for optimizing the design and performance of acoustic-based polymer devices. In this work, the impact of injection molding parameters, such as the injection velocity and packing pressure, on the acoustic parameters of polymers, namely the elastic moduli, is studied. The measurements lead to calculating material parameters, such as the Young’s modulus and Poisson’s ratio, that can be swiftly measured and determined thanks to this method. Polymethyl methacrylate (PMMA) was used as the molding material, and using PMMA LG IG 840, the parts were simulated and injection molded, applying a ‘design of experiment’ (DOE) statistical method. The results indicated a correlation between the injection molding process parameters and the acoustic characteristics, such as the elastic moduli, and a specifically decreasing trend with increase in the injection velocity. Notably, a relative decrease in the Young’s modulus by (Formula presented.) was observed when increasing the packing pressure from (Formula presented.) to (Formula presented.). Similarly, a decrease in the Poisson’s ratio of (Formula presented.) was observed when the injection velocity was increased from (Formula presented.) to (Formula presented.). This method can be used to fine-tune the material properties according to the needs of a given application and to facilitate the characterization of different polymer acoustic properties essential for acoustic-based polymer devices.

The paper describes the state-of-the-art in modeling and simulation of surface texture and topography generation at micro and nano dimensional scales. Three main classes of manufacturing processes used for the generation of engineering surfaces are considered: material removal processes, material conservative processes, and material additive processes. Types of modeling techniques for the simulation of surface generation are reviewed and discussed including analytical models, numerical multi-physics models, and data-driven methods. After presenting the application of those modeling techniques for the prediction of characteristics and geometry of surfaces generated by different manufacturing processes, their performance, implementation, and accuracy are discussed. Finally, a roadmap for the realization of a complete surface generation digital twin in manufacturing is outlined.

In-situ co-axial meltpool monitoring has become a popular tool for digitising the laser powder bed fusion (L-PBF) process, providing baseline data for certification. Each layer produces an image where the pixel position represents the laser coordinates and the pixel intensity denotes the sensor response. The 3D image stacks represent the infrared emission during the manufacturing of the physical component. However, interpreting monitoring data remains a challenge. To address this issue, this study evaluates the performance of a near-infrared photodiode in detecting typical geometrical features such as porosity and overhanging structures ranging from the micro-to-meso scale. Monitoring data is highly sensitive to heat accumulation around overhanging structures and can quantify dross formation based on hotspots. Cold spots, which represent a lack of fusion porosity at scan track intersections, can indicate a probability of defect formation. However, the sensitivity and predictive value of monitoring data for porosity are low due to the healing of defects in subsequent layers. Local process variables, such as the scan strategy and part orientation, significantly influence dross and hot spot formation. This study shows the potential of NIR photodiodes in deriving metrics for in-line certification of L-PBF components, leading to improved process control and quality assurance.

The evolution from current to future factories is supported by research contributions in many fields of technology. While lean manufacturing techniques represent a main improvement paradigm, the integration of new processes and technologies is a breakthrough for step-change improvements and systems evolution. The dominant paradigm of Industry 4.0 is a common framework under development. Accordingly, this Special Issue presents research results focused on the latest technologies and the design/operation of manufacturing systems contributing to this evolution towards the digitalization of production as well as the factory itself. The Special Issues consists of 14 original research papers and two review papers, which cover both fundamental and key enabling digital technologies, as well as the design and application of these technologies for the digitalization of production, covering six main domains: (1) Lean Manufacturing and Industry 4.0; (2) Internet of Things in Manufacturing; (3) Virtual Reality/Augmented Reality in Manufacturing; (4) Digital Technologies for Production Planning; (5) Machine Learning in Manufacturing; (6) Digitalization in Handling and Assembly. (1)Lean Manufacturing and Industry 4.0. Peças et al. [1] proposed a conceptual approach for the continuous improvement cycle Plan-Do-Check-Act, depicting how Industry 4.0 technological concepts should be used for its enhancement. Gil et al. [2], using a systematic literature review process, clarified the origin, evolution, and diversification of the lean concept and concluded that the use of “lean” as systemic thinking is likely to be further extended to new research fields. (2) Internet of Things in Manufacturing. Hosseinifard et al. [3] developed a simple wireless and passive RFID system for analogic pressure manometers, showing an affordable digitalization of analogic industrial systems. Vuković et al. [4] presented a low-cost prototyping solution for the legacy machine retrofit

Injection molding is one of the most important processes for the mass production of plastic parts. In recent years, many researchers have focused on predicting the occurrence and intensity of defects in injected molded parts, as well as the optimization of process parameters to avoid such defects. One of the most frequent defects of manufactured parts is blush, which usually occurs around the gate location. In this study, to identify the effective parameters on blush formation, eight design parameters with effect probability on the influence of this defect have been investigated. Using a combination of design of experiments (DOE), finite element analysis (FEA), and ANOVA, the most significant parameters have been identified (runner diameter, holding pressure, flow rate, and melt temperature). Furthermore, to provide an efficient predictive model, machine learning methods such as basic artificial neural networks, their combination with genetic algorithms, and particle swarm optimization have been applied and their performance analyzed. It was found that the basic artificial neural network (ANN), with an average accuracy error of 1.3%, provides the closest predictions to the FEA results. Additionally, the process parameters were optimized using ANOVA and a genetic algorithm, which resulted in a significant reduction in the blush defect area.

Demand trends towards mass customization drive the need for increasingly productive and flexible assembly operations. Walking-worker assembly lines can present advantages over fixed-worker systems. This article presents a multiproduct parallel walking-worker assembly line with shared automated stations, and evaluates its operational performance compared to semiautomated and manual fixed-worker lines. Simulation models were used to set up increasingly challenging scenarios based on an industrial case study. The results revealed that semiautomated parallel walking-worker lines could achieve greater productivity (+30%) than fixed-worker lines under high-mix low-volume demand conditions.

While attracting increasing research attention in science and technology, Machine Learning (ML) is playing a critical role in the digitalization of manufacturing operations towards Industry 4.0. Recently, ML has been applied in several fields of production engineering to solve a variety of tasks with different levels of complexity and performance. However, in spite of the enormous number of ML use cases, there is no guidance or standard for developing ML solutions from ideation to deployment. This paper aims to address this problem by proposing an ML application roadmap for the manufacturing industry based on the state-of-the-art published research on the topic. First, this paper presents two dimensions for formulating ML tasks, namely, ’Four-Know’ (Know-what, Know-why, Know-when, Know-how) and ’Four-Level’ (Product, Process, Machine, System). These are used to analyze ML development trends in manufacturing. Then, the paper provides an implementation pipeline starting from the very early stages of ML solution development and summarizes the available ML methods, including supervised learning methods, semi-supervised methods, unsupervised methods, and reinforcement methods, along with their typical applications. Finally, the paper discusses the current challenges during ML applications and provides an outline of possible directions for future developments.