Search results

The purpose of this study is to compare the overall performance of the injection moulding process by using metallic inserts produced by both conventional technologies and selective laser melting (SLM).

A systematic methodology is proposed for prior evaluation of the effectiveness of conformal cooling channels to reduce cycle time and/or to reduce the scrap rate.

The mould was reengineered considering the SLM process and manufactured. Injection trials were carried out to validate expectations provided by injection simulations, which resulted on good quality parts and a significant decrease on cooling time, and, consequently, on the overall cycle time. The minimisation of scrap provided energy savings and time-to-market reduction.

The initial costs for AM tools still pose some doubts on decision-makers. The challenge of this study is to implement the methodology on a small-scale production and still ensure that benefits are achieved.

The case study selected for this research work is based on a parking sensor housing, which is a plastic part assembled on the vehicle’s front and rear bumpers, therefore, with aesthetics concerns. The part produced with the conventional mould exhibits surface defects that, to be minimised (not eliminated), require a longer packing time to diminish the sink marks.

The economic impact of the use of SLM is relevant despite the low batch size for the case study presented. Energy savings are achieved due to scrap reduction and shorter cycle time.

The systematic methodology proposed for prior evaluation of the advantages of conformal cooling is possible to be applied both on small scale and high production series.

This study aims to examine the archives’ role in Portuguese public policies from 1976 to 2021, based on an analysis of decision-making and support mechanisms, as well as other legal and normative instruments (diplomas) associated with relevant policies and legal actions in government programs, major planning options, sectoral programs and legislation.

The investigation was based on a qualitative methodological framework that follows the document analysis method, consisting of four phases: research; first reading; critical reading and data collection; and analysis and interpretation.

Survey results identified the presence of the archives on government agendas and supported tracing an evolution in the development and application of measures in this area. Additionally, it was possible to relate these measures to socioeconomic and political contexts and to previously identified reference studies. The conclusion is that the place of archives in Portuguese public policies between 1976 and 2021 was still based in historical and heritage dimensions, excluding the aspect of information management, which was not even included within the concept of archives.

This is an original case study of the state of Portuguese archive policies, compared against reference works such as Couture(1998) and more recent national developments as considered in the works of Silva(2019).

Polyurethane (PUR) foam parts are traditionally manufactured using metallic molds, an unsuitable approach for prototyping purposes. Thus, rapid tooling of disposable molds using fused filament fabrication (FFF) with polylactic acid (PLA) and glycol-modified polyethylene terephthalate (PETG) is proposed as an economical, simpler and faster solution compared to traditional metallic molds or three-dimensional (3D) printing with other difficult-to-print thermoplastics, which are prone to shrinkage and delamination (acrylonitrile butadiene styrene, polypropilene-PP) or high-cost due to both material and printing equipment expenses (PEEK, polyamides or polycarbonate-PC). The purpose of this study has been to evaluate the ease of release of PUR foam on these materials in combination with release agents to facilitate the mulding/demoulding process.

PETG, PLA and hardenable polylactic acid (PLA 3D870) have been evaluated as mold materials in combination with aqueous and solvent-based release agents within a full design of experiments by three consecutive molding/demolding cycles.

PLA 3D870 has shown the best demoldability. A mold expressly designed to manufacture a foam cushion has been printed and the prototyping has been successfully achieved. The demolding of the part has been easier using a solvent-based release agent, meanwhile the quality has been better when using a water-based one.

The combination of PLA 3D870 and FFF, along with solvent-free water-based release agents, presents a compelling low-cost and eco-friendly alternative to traditional metallic molds and other 3D printing thermoplastics. This innovative approach serves as a viable option for rapid tooling in PUR foam molding.

This paper aims to study the physical and chemical characteristics of inkjet titanium dioxide inks for cotton fabric digital printing.

Different dispersing agents through the reaction of glycerol monooleate and toluene diisocyanate were prepared and then performed by using three different polyols (succinic anhydride-modified polyethylene glycol PEG 600, EO/PO Polyether Monoamine and p-chloro aniline Polyether Monoamine), to obtain three different dispersing agents for water-based titanium dioxide inkjet inks. The prepared dispersants were characterized using FTIR to monitor the reaction progress. Then the prepared dispersants were formulated in titanium dioxide inkjet inks formulation and characterized by particle size, dynamic surface tension, transmission electron microscopy, viscosity and zeta potential against commercial dispersants. Also, the study was extended to evaluate the printed polyester by using the prepared inks according to washing and crock fastness.

The obtained results showed that p-chloro aniline Polyether Monoamine (J) and succinic anhydride modified polyethylene glycol PEG 600 (H) dispersants provided optimum performance as compared to commercial standards especially, particle size distribution data while EO/PO Polyether Monoamine based on dispersant was against and then failed with the wettability and dispersion stability tests.

These ink formulations could be used for printing on cotton fabric by DTG technique of printing and can be used for other types of fabrics.

The newly prepared ink formulation for digital textile printing based on synthesized polyurethane prepolymers has the potential to be promising in this type of printing inks, to prevent clogging of nozzles on the printhead and to improve the print quality on the textile fiber.

The ability to use laser powder bed fusion (LPBF) to print parts with tailored surface topography could reduce the need for costly post-processing. However, characterizing the as-built surface topography as a function of process parameters is crucial to establishing linkages between process parameters and surface topography and is currently not well understood. The purpose of this study is to measure the effect of different LPBF process parameters on the as-built surface topography of Inconel 718 parts.

Inconel 718 truncheon specimens with different process parameters, including single- and double contour laser pass, laser power, laser scan speed, build orientation and characterize their as-built surface topography using deterministic and areal surface topography parameters are printed. The effect of both individual process parameters, as well as their interactions, on the as-built surface topography are evaluated and linked to the underlying physics, informed by surface topography data.

Deterministic surface topography parameters are more suitable than areal surface topography parameters to characterize the distinct features of the as-built surfaces that result from LPBF. The as-built surface topography is strongly dependent on the built orientation and is dominated by the staircase effect for shallow orientations and partially fused metal powder particles for steep orientations. Laser power and laser scan speed have a combined effect on the as-built surface topography, even when maintaining constant laser energy density.

This work addresses two knowledge gaps. (i) It introduces deterministic instead of areal surface topography parameters to unambiguously characterize the as-built LPBF surfaces. (ii) It provides a methodical study of the as-built surface topography as a function of individual LPBF process parameters and their interaction effects.

The aim of this work was to evaluate the efficiency of subcritical conditions using different water–ethanol mixtures to recover antioxidant compounds from soybean seed coats (SSCs).

SSCs were subjected to high temperature and pressure conditions, using ethanol–water mixtures as extractive solvent, to obtain phenolic and flavonoid compounds with antioxidant activity. A mathematical model, namely one-site desorption kinetic model, was used to describe the extraction kinetics.

Temperature, solvent mass flow rate and solvent composition were studied, and the best extraction conditions were defined by a screening design. The maximum concentration of phenolics was obtained at 220 °C, 50% of ethanol and 2.5 g/min of solvent mass flow rate and a high antioxidant capacity toward different techniques was achieved. The one-site desorption kinetic model showed that before 30 min under optimal conditions, more than 90% of phenolics and flavonoids were recovered, a shorter extraction time than the commonly used at normal pressure and room temperature.

The seed coat is a major by-product of soybean processing, and it only markets as a low value ruminant feed. To date, there are no reports on the extract phenolics from SSCs by means of this methodology. The extraction technique described in this study provides a potential alternative for extraction of bioactive compounds from SSCs. This study contributes to adding value to this industrial waste and, ultimately, to optimize the postharvest production chain of soybean grains.

This study aims to investigate unripe carob pod as a source of antioxidant molecules useful in the eco-friendly synthesis of a gelatin conjugate. This one was involved in the preparation of gummies able to produce remarkable human health benefits.

Eco-friendly strategies (ultrasound-assisted extraction, low temperatures and eco-friendly solvents) were employed in the extraction of active molecules. Antioxidant molecules were involved in the grafting reaction with gelatin chains (ascorbic acid/hydrogen peroxide couple as initiator system). Gelatin conjugate represents a useful material able to prepare gummies with remarkable rheological and antioxidant performances over time.

Experimental results confirmed that the green approach allowed the achievement of extracts with remarkable antioxidant properties due to the presence of phenolic moieties. Gelatin conjugate synthesis preserved these functionalities, usefully exploited in the preparation of gummies with significant structural and biological features.

Compared to the literature data the preparation of the gummies with outstanding biological properties was performed by employing functional gelatin synthesized by an eco-friendly approach.

Material extrusion is one of the most commonly used approaches within the additive manufacturing processes available. Despite its popularity and related technical advancements, process reliability and quality assurance remain only partially solved. In particular, the surface roughness caused by this process is a key concern. To solve this constraint, experimental plans have been exploited to optimize surface roughness in recent years. However, the latter empirical trial and error process is extremely time- and resource consuming. Thus, this study aims to avoid using large experimental programs to optimize surface roughness in material extrusion.

This research provides an in-depth analysis of the effect of several printing parameters: layer height, printing temperature, printing speed and wall thickness. The proposed data-driven predictive modeling approach takes advantage of Machine Learning (ML) models to automatically predict surface roughness based on the data gathered from the literature and the experimental data generated for testing.

Using ten-fold cross-validation of data gathered from the literature, the proposed ML solution attains a 0.93 correlation with a mean absolute percentage error of 13%. When testing with our own data, the correlation diminishes to 0.79 and the mean absolute percentage error reduces to 8%. Thus, the solution for predicting surface roughness in extrusion-based printing offers competitive results regarding the variability of the analyzed factors.

There are limitations in obtaining large volumes of reliable data, and the variability of the material extrusion process is relatively high.

Although ML is not a novel methodology in additive manufacturing, the use of published data from multiple sources has barely been exploited to train predictive models. As available manufacturing data continue to increase on a daily basis, the ability to learn from these large volumes of data is critical in future manufacturing and science. Specifically, the power of ML helps model surface roughness with limited experimental tests.

Whey is a by-product of paneer, cheese and casein industry and considered as a dairy waste. Worldwide, approximately 180–1,900 million tons of whey is produced annually. Whey is classified as a high pollutant due to its organic matter level. Owing to its high chemical oxygen demand and biological oxygen demand, it is a big threat to the environment. Whey contains 4.5%–5.0% lactose, 0.6%–0.7% protein, 0.4%–0.5% lipids, vitamins and minerals. Due to its high nutritional profile, it is a good substrate for the microorganisms for production of natural pigments. The purpose of this paper is to review the utilization of low cost substrate (whey) for production of various types of pigments and their applications in different sectors.

The databases for the search included: Scopus, PubMed, Science Direct, Web of Science, Research gate and Google. The main search was directed towards different types of natural pigments, stability, technologies for enhancing their production and contribution towards circular economy. Approximately 100 research papers were initially screened. A global search was conducted about natural pigments. Research articles, review papers, books, articles in press and book chapters were the type of search for writing this review paper.

Production of natural pigments using whey and their addition in food products not only improves the colour of food but also enhances the antioxidant properties of food products, helping the health benefits by chelating free radicals from the body. The sustainable use of whey for production of natural pigments can improve the bio-based economy of different industries and thereof the national economy.

Efficient utilization of whey can bring a lot more opportunities for production of natural pigments in a sustainable manner. The sustainable approach and circular economy concepts will benefit the dependent industries and health conscious consumers. The potential uses of whey for the production of natural pigments using diverse organisms are highlighted in this paper.

The purpose of this study is to develop a deep learning framework for additive manufacturing (AM), that can detect different defect types without being trained on specific defect data sets and can be applied for real-time process control.

This study develops an explainable artificial intelligence (AI) framework, a zero-bias deep neural network (DNN) model for real-time defect detection during the AM process. In this method, the last dense layer of the DNN is replaced by two consecutive parts, a regular dense layer denoted (L1) for dimensional reduction, and a similarity matching layer (L2) for equal weight and non-biased cosine similarity matching. Grayscale images of 3D printed samples acquired during printing were used as the input to the zero-bias DNN.

This study demonstrates that the approach is capable of successfully detecting multiple types of defects such as cracks, stringing and warping with high accuracy without any prior training on defective data sets, with an accuracy of 99.5%.

Once the model is set up, the computational time for anomaly detection is lower than the speed of image acquisition indicating the potential for real-time process control. It can also be used to minimize manual processing in AI-enabled AM.

To the best of the authors’ knowledge, this is the first study to use zero-bias DNN, an explainable AI approach for defect detection in AM.