Search

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.

The long cooling time of the powder matrix in selective laser sintering (SLS) hinders the maximum system productivity exploitation within daily production cycles. The present research aims at reducing the cooling time of SLS without compromising on quality, by investigating the feasibility of integrating in-printed cooling channels within the powder matrix. A cooling solution is realized using printed channels, and corresponding thermal simulations are developed to predict cooling rates for different channel configurations. Experimental and simulation results show that a 45% cooling time reduction could be achieved while maintaining the same geometrical characteristics in terms of dimensions and form error.

Featured Application: In this work, a methodology for the quantitative assessment of additive manufacturing and conventional manufacturing technology value chains is presented for the production of injection molding tools. The development of injection molding tools is an expensive, time-consuming, and resource-intensive process offering little to no flexibility to adapt to variations in product design. Metal additive manufacturing can be used to produce these tools in a cost-effective way. Nevertheless, in an industrial context, effective methods are missing for the selection of the most suitable technology for the given tooling project. This paper presents a method to compare process chains based on additive and conventional subtractive technologies for the manufacturing of metal tooling for injection molding. The comparison is based on a technology focused-performance analysis (TFPA) through computer simulation performed using Tecnomatix Plant Simulation developed by Siemens Digital Industries Software combined with a customized cost–benefit economic analysis tool. The analysis of the technology comparison highlights potential bottlenecks for production, such as the printing phase and the heat treatment. It also gives a deeper understanding of the technology maturity level of conventional milling machines against laser powder bed fusion machines. The result is that the total costs for an insert made by AM and CM are indeed rather similar (the cost difference between the two tooling process chains is lower than 5%). The cost analysis reveals major costs drivers in the production of high-performance molding tools, such as the cutting tools employed for the milling steps and their changeover frequency. The industrial case of a 32-cavity mold insert for plastic injection molding is used to perform the study, develop the analysis, and validate the results.

Micro and nano molding technologies are continuously being developed due to enduring trends such as increasing miniaturization and the higher functional integration of products, devices and systems [...].

Metal additive manufacturing (MAM) of mould components is associated with cost-intensive post-process finishing operations, which are currently one of the main drawbacks preventing wider industrial adoption of MAM. This work analyses the current possibilities and limitations of MAM for plastic injection moulding tool applications, demonstrating the opportunity to limit post-processes to heat treatment and the machining of the MAM insert outer geometry, which was only intended to ensure the assembly of the insert in the injection moulding frame. The results focused on evaluating the as-built insert cavity geometrical characteristics and the dimensional replication performance of the injection moulding process. The as-built cavity surface texture was comparable to a surface roughness that would be alternatively manufactured by electrical discharge machining (EDM). The cavity texture and features was replicated to the plastic injection moulded samples with an average replication fidelity higher than 95 %, enabling the direct production of inserts with integrated enhanced surface properties and aesthetics. Additional geometrical characteristics, such as global cavity dimensions, corner radii, and the staircase effect of selected features, typical of MAM products, were examined to gather further knowledge of the MAM process resolution and the corresponding replication capabilities of the IM process. Additional considerations with regards to the insert mechanical loading conditions and its durability in terms of the expected fatigue lifetime were addressed, verifying the usability of the MAM insert for production batches of at least 2⋅106 injection moulding cycles. Finally, the improvements in terms of lead-time reduction and production costs associated with MAM compared with conventional tooling technologies were evaluated and discussed, confirming the potential of the presented process chain of MAM for injection moulding tools production.

The manufacturing of inserts for micro injection moulding made of mortar material is presented in this work. The fabrication of the mortar insert described in this publication relied on a versatile and relatively fast rapid prototyping process based on soft tooling. The mortar insert has a QR code with micro features on its surface, which was replicated in acrylonitrile butadiene styrene (ABS) polymer by the micro injection moulding process. With this approach, it is possible to fabricate hard inserts for micro injection moulding purposes that are able to compete with conventional-made inserts made of tool steel.

The formation of blister in the injection moulded parts, especially in the film insert moulded parts, is one of most significant causes of part rejection due to cosmetic requirements or functionality issues. The mechanism and physics of blister formation for molded parts are not well-understood by the state-of-the-art literature. The current paper increases the fundamental understanding of the causes for blister formation. In the experiment, a membrane strip of 5 mm in width was overmoulded with Polypropylene (PP), which formed a disc-shaped part with a diameter of 17.25 mm and a thickness of 500 μm. To investigate the influence of the processing parameters, a full factorial design of experiments (DoE) setup was conducted, including mould temperature (Tm), barrel temperature (Tb), injection speed (Vi) and packing pressure (Pp) as variables. The degree of blistering at the surface was characterized by the areal surface roughness parameters Spk and Smr1, measured with a confocal laser microscope. The measurements were taken on the 10 mm long section of the membrane surface in the centre of the moulded part across the entire width of the film. In addition, the film insert moulding (FIM)-process was simulated and the average shrinkage of the substrate material under the membrane was investigated. Eventually, a method and processing window could be defined that could produce blister-free parts.

Micro polymer parts can be usually manufactured either by conventional injection moulding (IM) or by micro-injection moulding (µIM). In this paper, functional analysis was used as a tool to investigate the performances of IM and µIM used to manufacture the selected industrial component. The methodology decomposed the production cycle phases of the two processes and attributed functions to parts features of the two investigated machines. The output of the analysis was aimed to determine casual chains leading to the final outcome of the process. Experimental validation of the functional analysis was carried out moulding the same micro medical part in thermoplastic elastomer (TPE) material using the two processes by means of multi-cavity moulds. The produced batches were assessed using a precision scale and a high accuracy optical instrument. The measurement results were compared using capability indexes. The data-driven comparison identified and quantified the correlations between machine design and part quality, demonstrating that the µIM machine technology better meets the accuracy and precision requirements typical of micro manufacturing productions.

Molded pulp products are experiencing increased importance due to their environment-friendly characteristics with regards to both production and disposal. In this study, the thermoforming process for the production of molded pulp products was examined by means of two experimental campaigns, with the scope of distinguishing between the water removal due to only compression (better known as cold pressing) and the water removal under additional thermal conditions. The underlying multiphysics of the processes were identified by comparison with similar manufacturing processes found in available literature, and then implemented using a 1-dimensional finite element model in COMSOL software. This fast, simple model was able to predict the water removal for a specific set of process conditions and the results were supported by corresponding experimental observations. Within a process time of up to 15 s, temperatures ranging from 80 °C to 120 °C, and a final dryness between 40% and 65%, the simulations gave accurate estimates, with a difference in prediction down to 0.3%. The paper thus documents the first combined experimental-simulation study for thermoforming of molded pulp products.