Manufacturing Process Research Articles

Decision tree-based approach to extrapolate life cycle inventory data of manufacturing processes

Life cycle assessment (LCA) plays a crucial role in green manufacturing to uncover the critical aspects for alleviating the environmental burdens due to manufacturing processes. However, the scarcity of life cycle inventory (LCI) data for the manufacturing processes is a considerable challenge. This paper proposes a novel approach to extrapolate LCI data of manufacturing processes. Taking advantage of LCI data in the Ecoinvent datasets, decision tree-based supervised machine learning models, namely decision tree, random forest, gradient boosting, and adaptive boosting, have been developed to extrapolate the data of GHG emissions, i.e., carbon dioxide, nitrous oxide, methane, and water vapor. Initially, a correlation analysis was conducted to derive the most influential factors on GHG quantities resulting from manufacturing activities. First, the collected data have been preprocessed and split into train and test sets (70% and 30%, respectively). Second, a five-fold cross-validation method was applied to tune the hyperparameters of the models. Then, the models were re-trained using the best hyperparameters and evaluated using the test set. The results reveal that the Gradient Boosting model has a superior predictive performance for extrapolating the GHG emission data, with average coefficients of determination (R2) on the test set <0.95. Moreover, the model predictions involve relatively low values of the average root mean squared error and an average mean percentage of error on the test set. The correlation and feature importance analyses emphasized that the workpiece material and manufacturing technology have a considerable effect on natural resource consumption, i.e., energy, material, and water inflows into the process. Meanwhile, energy consumption, water usage, and raw aluminum depletion were the most influential factors in GHG emissions. Eventually, a case study to extrapolate the inflows and the outflows for new manufacturing activities has been conducted using the validated models. The proposed GraBoost model provides a computational supplementary approach to estimate and extrapolate the GHG emissions for different manufacturing processes when LCI data are incomplete or don't exist within LCI databases.

Personalized feature extraction for manufacturing process signature characterization and anomaly detection

Manufacturing process signatures reflect the process stability and anomalies that potentially lead to detrimental effects on the manufactured outcomes. Sensing technologies, especially in-situ image sensors, are widely used to capture process signatures for diagnostics and prognostics. This imaging data is crucial evidence for process signature characterization and monitoring. A critical aspect of process signature analysis is identifying the unique patterns in an image that differ from the generic behavior of the manufacturing process in order to detect anomalies. It is equivalent to separating the “unique features” and process-wise (or phase-wise) “shared features” from the same image and recognizing the transient anomaly, i.e., recognizing the outlier “unique features”. In state-of-the-art literature, image-based process signature analysis relies on conventional feature extraction procedures, which limit the “view” of information to each image and cannot decouple the shared and unique features. Consequently, the features extracted are less interpretable, and the anomaly detection method cannot distinguish the abnormality in the current process signature from the process-wise evolution. Targeting this limitation, this study proposes personalized feature extraction (PFE) to decouple process-wise shared features and transient unique features from a sensor image and further develops process signature characterization and anomaly detection strategies. The PFE algorithm is designed for heterogeneous data with shared features. Supervised and unsupervised anomaly detection strategies are developed upon PFE features to remove the shared features from a process signature and examine the unique features for abnormality. The proposed method is demonstrated on two datasets (i) selected data from the 2018 AM Benchmark Test Series from the National Institute of Standards and Technology (NIST), and (ii) thermal measurements in additive manufacturing of a thin-walled structure of Ti–6Al–4V. The results highlight the power of personalized modeling in extracting features from manufacturing imaging data.

The Influence of Additively Manufactured Specimen Thickness on Laser-Induced Ultrasonic Signals

Selective laser melting (SLM) technology, employed in metal additive manufacturing, is widely utilised to fabricate components with precise dimensions and excellent mechanical properties by melting powder layer by layer. Laser ultrasonic testing is anticipated to be an efficient method for in-line monitoring in the additive manufacturing (AM) process. The conversion of specimens from thin to thick during the SLM manufacturing process has an impact on the ultrasonic signals. This paper focuses on the influence of AM specimen thickness changes on ultrasonic signals. In the finite element analysis, the incremental specimen thickness was maintained at 0.2 mm, which was the initial value. As the specimen thickness increased sequentially, ultrasonic signals were obtained from different specimens. The results demonstrate that Lamb waves are generated when the specimen is thin, and the dispersive characteristics weaken as the thickness increases. The dispersive phenomenon diminishes significantly once the specimen thickness exceeds 1 mm. By employing the f-k method, calculated dispersion curves for various specimen thicknesses were obtained. After fitting the derived dispersion curve, a mapping relationship is established with respect to the specimen thickness. Consequently, the fitting coefficient of the dispersion curve can therefore be utilized to characterize the thickness of the additive manufactured thin specimen.

The Use of Thermal Data for Quality Assurance in the Process of Continuous Ultrasonic Welding of Thermoplastic Composite Materials

Continuous ultrasonic welding (CUW) is a manufacturing process used to join thermoplastic composite materials using high-frequency ultrasonic vibrations. Under pressure and vibration, the welding material heats up, melts and the welding partners are joined while they are cooling down. The development of CUW is getting raised attention over the last decade since the use of thermoplastic composites (TCs) in the production industry and especially the aerospace sector demands for economic and fast joining techniques. In order to establish this welding technique in the industry it is necessary to develop methods which enable to do quality assurance on the produced welding seams. In the experiments described in this paper the potential of thermal measurements for the quality assurance of the ultrasonic welding of TCs is investigated. Different thermal systems are evaluated and it is described how the data of the most promising technique is further processed and fed to an artificial intelligence (AI) algorithm. The here trained networks are created to predict lab-shear-strength values and reach mean absolute error scores of below 1 MPa. The described investigations hint that thermography seem to be a valuable resource to make more reliable quality assurance in the process of CUW of TCs and therefore might help to establish the use of this welding technology further in the industry.

Effect of combined additions of Sc, Zr and Ti on hot-cracking resistance and precipitation behaviour in Al-Mg alloy by L-PBF

The request for high-performance aluminum components prompts research into innovative alloys compatible with laser-based additive manufacturing processes, leveraging grain refiners in their composition to mitigate hot-cracking and enhance strength. While the addition of Sc and Zr in Al-Mg alloys has been widely investigated, the high cost and supply risks associated with Sc necessitate reducing its amount and replacing, at least partially, with other inoculants. In this preliminary study, the amount of Sc replaceable with different concentrations of Zr or Ti is evaluated investigating the laser powder bed fusion processability of three powder feedstocks: a pre-alloyed Al-Mg-Zr-Sc powder, a blend of the previous alloy with addition of Zr particles, and a further blend of an alloy depleted of Zr with added Ti particles. The experimental analyses on the laser-processed alloys showed that the standard and the Zr-enriched alloys featured a bimodal microstructure free from cracks with fine equiaxed grains at the edge of the molten pools and coarser grains at their centers. In the alloy variant depleted in Zr with addition of Ti particles a columnar structure was observed and hot-cracks appeared. Thermodynamic simulations of phase formation allowed defining the precipitation kinetics during solidification and direct aging, showing increased precipitation in alloys with higher Zr content, while the presence of Ti resulted in sluggish precipitation. Experimental aging tests demonstrated significant increases in microhardness, with peak values of the modified alloys achieved after 12 h at 375°C.

Characterization of the key aroma compounds in a novel Qingke baijiu of Tibet by GC-MS, GC×GC-MS and GC-O-MS

As a local specialty of Tibet, Qingke baijiu (QB) is made from highland barley, and is referred to as the manufacturing process of light-aroma baijiu. Using highland barley in the Qu-making and the high-temperature stacking process of sauce-aroma baijiu results in a novel Qingke baijiu (NQB). This study analyzed two kinds of NQBs by GC-MS, GC×GC-MS, and GC-O-MS, and compared them with QB, light-aroma, strong-aroma, and sauce-aroma baijiu. A total of 346 volatile compounds and 60 aroma-active compounds were detected in NQB. There were 14 compounds with FD≥27 and OAVs ≥1. Although NQB and QB shared 39 volatile compounds, NQB has more than three times the overall concentration of volatile compounds than QB. In contrast to QB, NQB exhibited a richer flavor profile and a more intense fruity, floral, and acidic aroma. Meanwhile, the distilled grains and sorghum aroma were comparatively diminished.

Research on Establishment of Digital-Twin System for Intelligent Control of Cutting Tools Sintering Process Driven by Data-Model Combination

In the manufacturing process of tungsten cemented carbide cutting tools, the changes in temperature, pressure and vacuum during the sintering process of the cutting tools can have a significant impact on the quality of the cutting tools. This effect is particularly significant in the vacuum sintering stage, so this paper focuses on cutting tools sintering in the vacuum stage. The cutting tools sintering process is carried out in a closed space in a sintering furnace with a complex internal structure. Therefore, it is extremely difficult to accurately predict the heating condition of the cutting tools in the furnace and monitor the sintering quality of the cutting tools. In this study, a digital twin system for the sintering process of cemented carbide cutting tools is proposed, which combines monitoring rolling data analysis and sintering quality prediction model. Firstly, the radiative thermodynamic model of cutting tools sintering temperature was established in this system. The model predicts the time-varying state of the heat distribution at any location in the furnace during the vacuum phase. Subsequently, based on the convolutional neural network, the mapping relationship between the sintering parameters and the grain size in the vacuum stage is constructed, and a dynamic evaluation model of cutting tools sintering quality is proposed. The model enables real-time prediction of sintering quality in the vacuum stage. The system realizes the prediction of the time-varying state of the sintering quality of the cutting tools. The digital twin system realizes the adaptive temperature control of the vacuum stage of cutting tools sintering based on the fuzzy control algorithm. The application of this system has increased the yield rate of cemented carbide cutting tools manufacturing to 98.9%. This breaks through the problem of timing modulation of cutting tools sintering quality in the vacuum stage of cutting tools sintering.

Improving access to gene therapy for rare diseases.

Effective gene therapy approaches have been developed for many rare diseases, including inborn errors of immunity and metabolism, haemoglobinopathies and inherited blindness. Despite successful pre-clinical and clinical results, these gene therapies are not widely available, primarily for non-medical reasons. Lack of commercial interest in therapies for ultra-rare diseases, costs of development and complex manufacturing processes required for advanced therapy medicinal products (ATMPs) are some of the main problems that are restricting access. The complexities and costs of navigating the regulatory environments in different jurisdictions for treatments that affect small numbers of patients is a problem unique to ATMPS for rare and ultra-rare diseases. In this Perspective, we outline some of the challenges and potential solutions that, we hope, will improve access to gene therapy for rare diseases.

Systematic review: How to obtain Fibrinolytic Enzymes from Fungi

Fibrinolytic complications such as stroke and peripheral occlusive disease due to vascular occlusion are significant causes of poor prognosis and death. Given the ability of mushroom fibrinolytic enzymes to dissolve blood clots and prevent side effects, their use as a potential thrombolytic therapy has attracted great interest. Serine protease, metalloprotease, and serine metalloprotease are protease enzymes that can affect the fibrinolytic mechanism by dissolving blood clots and increasing blood flow. This systematic review aimed to disclose all information regarding the production, purification, and characterization of fibrinolytic proteases from fungi. Searches were conducted on online media databases, including Google and journal sites (NCBI, PubMed, and others, using the keywords "(Fibrinolytic Enzymes) OR (Fibrinolytic Proteases) AND (Fungi or Mushrooms)" from 2012-2023. This systematic review observed reviews from the purification and characterization steps, indicating the need for attention to the manufacturing process and enzymatic applications. The production of these enzymes by fungi is associated with developments in pharmaceutical applications.

Shake table tests on a rigid block isolated with high-damping unbonded fiber-reinforced elastomeric isolators

Fiber-reinforced elastomeric isolators (FREIs) represent a relatively recent type of elastomeric device. In contrast to traditional steel-reinforced elastomeric isolators (SREIs), FREIs are constructed with multiple layers of rubber pads and fiber layers instead of steel laminas. This choice is aimed at cost reduction during the manufacturing and installation process. To enhance cost-efficiency, FREIs can also be used in unbonded applications (UFREIs), where the device is placed between the foundation and the superstructure without bonding or fastening. This approach helps to further minimize installation expenses. Furthermore, the lateral rollover deformation, a characteristic feature of this configuration, improves energy dissipation. This study investigates the seismic isolation capabilities of high-damping unbonded FREIs for low-rise masonry buildings. Specifically, unidirectional cyclic and shake table tests of a rigid block isolated with two UFREIs are discussed. The primary goals of this experimental study include evaluating the effectiveness of UFREIs in reducing seismic demands, analyzing the response of UFREIs under unidirectional ground-motion excitation, confirming the ability of UFREIs to withstand major unidirectional excitation without sustaining damage, and exploring the limitations of this isolation system under a range of seismic inputs and low axial loads.

Combined Gas Tungsten Arc Welding and Shielded Metal Arc Welding Processes for Joining Tube to Tubesheet in Shell and Tube Heat Exchangers

Abstract Flawless tube and tubesheet joints are necessary for shell and tube heat exchanger with high leakproof ability and durability. The consequence of mixing of transfer fluids due to the defective welded or expanded tube-to-tubesheet joint results in complete malfunctioning of the heat exchange process. In this case, there is a high demand for quality assessment of the fabricated tube-to-tubesheet joints (TTS) based on the international standards. Additionally, the literature on assessing the mechanical and metallurgical characteristics of tube-to-tubesheet joints using any one welding technique is available; however, articles dealing with the use of multiple welding techniques are scanty. In the current work, the quality assessment of strength welded and light expanded (3%) tube-to-tubesheet joints followed by light expansion was performed whereas for welding, combined gas tungsten arc welding (GTAW) and shielded metal arc welding (SMAW) as root pass and cap pass were adopted in the fabrication stage. Quality and sound tube-to-tubesheet joints were produced using welding and tube expansion process. Linear and circular indications representing the cracks were absent on the developer in the liquid penetration test. The minimum leak path (MLP) was greater than the two-third of tube wall thickness. The results strongly qualify the use of combined gas tungsten inert gas and shielded metal arc welding process for manufacturing of tube-to-tubesheet joints. The methodology and results in the current article are beneficial for researchers and industrialists working close to shell and tube heat exchangers and boilers.

Development of Sustainable 3D Printable Ternary Composite

3D printing technology has the potential to reduce construction waste through its controlled manufacturing process and optimized material consumption, making it a promising sustainable construction approach with reduced environmental impact. However, current 3D printed structures often have a high carbon footprint due to the large amounts of fine-grained primary materials used in mortar composites. Most efforts to develop 3D printed structures are associated with concrete and Portland cement-based materials.This research presents an innovative approach for the reuse of gypsum to develop a material suitable for 3D printing, with the goal to industrialize the usage of waste gypsum in civil engineering. The objective of this study is to develop a sustainable gypsum-cement-pozzolanic (GCP) ternary binder composite, incorporating recycled industrial waste gypsum materials such as construction demolition waste gypsum (CDG), along with Portland cement and secondary pozzolanic materials. The GCP ternary composite combines the desired properties of gypsum, such as fast setting time, with those of Portland cement, which offers high final strength. The addition of a pozzolanic component is essential to ensure the chemical stability of this ternary system.As part of this study, ternary composites were designed and tested for their mechanical properties, durability, and suitability for 3D printing. Development and optimization of the ternary composite was carried out as a loop of procedures (Fig.1). Through these optimization procedures, a range of mixtures with necessary properties for 3D printing was found. To ensure sustainability and to assess the environmental impact of the mixtures, life cycle assessment (LCA) was performed.The LCA of GCP composites showed at least 1.5 times lower embodied energy and carbon dioxide emissions compared to Portland cement-based mortars. This is without considering avoided emissions from the end-of-life stage of gypsum waste, as well as higher technical properties that make GCP composites superior to regular cement composites. Considering these factors, GCP composites exhibit even lower emissions and are considered a viable alternative to Portland cement-based mortars.

Soft Contact Lens Engraving Characterization by Wavefront Holoscopy

Permanent engravings on contact lenses provide information about the manufacturing process and lens positioning when they are placed on the eye. The inspection of their morphological characteristics is important, since they can affect the user’s comfort and deposit adhesion. Therefore, an inverted wavefront holoscope (a lensless microscope based on Gabor’s principle of in-line digital holography) is explored for the characterization of the permanent marks of soft contact lenses. The device, based on an in-line transmission configuration, uses a partially coherent laser source to illuminate the soft contact lens placed in a cuvette filled with a saline solution for lens preservation. Holograms were recorded on a digital sensor and reconstructed by back propagation to the image plane based on the angular spectrum method. In addition, a phase-retrieval algorithm was used to enhance the quality of the recovered images. The instrument was experimentally validated through a calibration process in terms of spatial resolution and thickness estimation, showing values that perfectly agree with those that were theoretically expected. Finally, phase maps of different engravings for three commercial soft contact lenses were successfully reconstructed, validating the inverted wavefront holoscope as a potential instrument for the characterization of the permanent marks of soft contact lenses. To improve the final image quality of reconstructions, the geometry of lenses should be considered to avoid induced aberration effects.

Quality Improvement of Health Plaster Products With Six Sigma Method and QCDSME Analysis

Health plasters are one of a pharmaceutical company's excellent items. There are still defective products in the manufacturing process, including torn paper, joint 1, not cut, perforated edges, joint 2, tucked, long-short, and asymmetrical. Research conducted between June and December 2021 reveals that 192.960 Kg of substandard items were discovered. The contribution of this research is to identify the underlying reasons of the problem and decrease the number of product faults. By using the QCDSME (Quality-Cost-Delivery-Safety-Morale) method analysis in the cost stage, this company is known to experience losses due to defective products of IDR 18,047,232 / year. The results of the analysis using the six sigma method at the analyze stage using the fishbone diagram tool are known that defective products are caused by the absence of size standards in the repair of plate and pressure roll positions, differences in operator responses, the absence of written procedures for handling product defects, and the existence of product roll rolls that are too loose or tight. The improvement proposal made next is to make a size standard for the plate position with a value of 12.5 ± 1 cm and a pressure roll with a value of 22 ± 1 mm with additional tools in the form of a ruler. After taking corrective steps and quality control on product defects, the results showed that the value of product defects decreased from the previous average of 1,119 pcs/day to 205 pcs/day, so that the company's loss value was reduced to IDR 3,306,240/year.

Accurate Measurement of the Internal Temperature of 280 Ah Lithium-Ion Batteries by Means of Pre-Buried Thermocouples

Batteries with an energy storage capacity of 280 Ah play a crucial role in promoting the development of smart grids. However, the inhomogeneity of their internal temperature cannot be accurately measured at different constant charge and discharge power, affecting the efficiency and safety of the battery. This work adopts finite element analysis to determine the typical internal temperature of a single-cell model, which can guide the measuring position of the battery. Before the manufacturing process, a slim pre-buried sensor is utilized to reduce the negative impacts of different constant charge and discharge powers. The maximum internal temperature of the battery is up to 77 °C at a constant charge and discharge power of 896 W. The temperature difference between the two poles and the battery surface is as high as 26.2 °C, which is beyond the safety temperature (55 °C). This phenomenon will result in the degradation of the positive electrode through dQ/dV curves. These measurements of battery internal temperature can improve battery heat control and facilitate the development of energy storage technology.

More is better? The role of strategic data management in a lean manufacturing process

Purpose Lean manufacturing has been pivotal in emphasizing the reduction of cycle times, minimizing manufacturing costs and diminishing inventories. This research endeavors to formulate a lean data management paradigm, through the design and execution of a strategic edge-cloud data governance approach. This study aims to discern anomalous or unforeseen patterns within data sets, enabling an efficacious examination of product shortcomings within manufacturing processes, while concurrently minimizing the redundancy associated with the storage, access and processing of nonvalue-adding data. Design/methodology/approach Adopting a lean data management framework within both edge and cloud computing contexts, this study ensures the preservation of significant time series sequences, while ascertaining the optimal quantity of normal time series data to retain. The efficacy of detected anomalous patterns, both at the edge and in the cloud, is assessed. A comparative analysis between traditional data management practices and the strategic edge-cloud data governance approach facilitates an exploration into the equilibrium between anomaly detection and space conservation in cloud environments for aggregated data analysis. Findings Evaluation of the proposed framework through a real-world inspection case study revealed its capability to navigate alternative strategies for harmonizing anomaly detection with data storage efficiency in cloud-based analysis. Contrary to the conventional belief that retaining comprehensive data in the cloud maximizes anomaly detection rates, our findings suggest that a strategic edge-cloud data governance model, which retains a specific subset of normal data, can achieve comparable or superior accuracy with less normal data relative to traditional methods. This approach further demonstrates enhanced space efficiency and mitigates various forms of waste, including temporal delays, storage of noncontributory normal data, costs associated with the analysis of such data and excess data transmission. Originality/value By treating inspected normal data as nonvalue-added, this study probes the intricacies of maintaining an optimal balance of such data in light of anomaly detection performance from aggregated data sets. Our proposed methodology augments existing research by integrating a strategic edge-cloud data governance model within a lean data analytical framework, thereby ensuring the retention of adequate data for effective anomaly detection.

A data integration framework of additive manufacturing based on FAIR principles

Abstract Laser-powder bed fusion (L-PBF) is a popular additive manufacturing (AM) process with rich data sets coming from both in situ and ex situ sources. Data derived from multiple measurement modalities in an AM process capture unique features but often have different encoding methods; the challenge of data registration is not directly intuitive. In this work, we address the challenge of data registration between multiple modalities. Large data spaces must be organized in a machine-compatible method to maximize scientific output. FAIR (findable, accessible, interoperable, and reusable) principles are required to overcome challenges associated with data at various scales. FAIRified data enables a standardized format allowing for opportunities to generate automated extraction methods and scalability. We establish a framework that captures and integrates data from a L-PBF study such as radiography and high-speed camera video, linking these data sets cohesively allowing for future exploration. Graphical abstract

Microsphere-assisted hyperspectral imaging: super-resolution, non-destructive metrology for semiconductor devices

As semiconductor devices shrink and their manufacturing processes advance, accurately measuring in-cell critical dimensions (CD) becomes increasingly crucial. Traditional test element group (TEG) measurements are becoming inadequate for representing the fine, repetitive patterns in cell blocks. Conventional non-destructive metrology technologies like optical critical dimension (OCD) are limited due to their large spot diameter of approximately 25 μm, which impedes their efficacy for detailed in-cell structural analysis. Consequently, there is a pressing need for small-spot and non-destructive metrology methods. To address this limitation, we demonstrate a microsphere-assisted hyperspectral imaging (MAHSI) system, specifically designed for small spot optical metrology with super-resolution. Utilizing microsphere-assisted super-resolution imaging, this system achieves an optical resolution of 66 nm within a field of view of 5.6 μm × 5.6 μm. This approach effectively breaks the diffraction limit, significantly enhancing the magnification of the system. The MAHSI system incorporating hyperspectral imaging with a wavelength range of 400–790 nm, enables the capture of the reflection spectrum at each camera pixel. The achieved pixel resolution, which is equivalent to the measuring spot size, is 14.4 nm/pixel and the magnification is 450X. The MAHSI system enables measurement of local uniformity in critical areas like corners and edges of DRAM cell blocks, areas previously challenging to inspect with conventional OCD methods. To our knowledge, this approach represents the first global implementation of microsphere-assisted hyperspectral imaging to address the metrology challenges in complex 3D structures of semiconductor devices.

Application of the FMECA method on the electro-hydraulic system for drilling machine type NKH45

This article focuses on the implementation of a preventive maintenance strategy, responding to a major challenge: the search for optimization in minimizing downtime of the radial drilling machine (NKH45). Crucially positioned at the heart of the mechanical manufacturing process, this machine holds significant influence on overall production performance and availability. In consideration of the strategic importance of this equipment within the framework of the mechanical manufacturing activity, an advanced methodology, in particular FMECA (Failure Mode Effects and Criticality Analysis), is implemented. FMECA represents one of the most recommended approaches in this context, based on reliability, allowing the meticulous identification of the main causes of functional failures of equipment. FMECA is a risk prevention method. It is used to improve the reliability of a product, a process or even a means of production. It makes it possible to assess the criticality of potential failures in a system. According to the recommendations of the maintenance department of the company specializing in the construction of agricultural equipment, this study will focus on a subsystem of the electro-hydraulic part of the NKH45 radial drilling machine, we use the FMEA Analysis method (Failure Mode Effects and Criticality Analysis). Thanks to this approach, it is possible to identify the main reasons for equipment breakdowns that affect production and to classify these breakdowns in order to develop an optimal maintenance strategy aimed at: reducing the number of breakdowns, preventing breakdowns, improving the preventive maintenance, reduce downtime, improve corrective maintenance, therefore the ultimate objective of this approach is to design an optimal maintenance action plan in order to significantly minimize downtime, which slows down production flow.

Enhancing sustainability in polymer 3D printing via fusion filament fabrication through integration of by-products in powder form: mechanical and thermal characterization

AbstractFFF (fused filament fabrication) is a type of 3D printing that utilizes filament for part creation. This study proposes using by-products or waste to replace part of the plastic in FFF filament, reducing environmental impact. The aim is to maintain a simple manufacturing process involving extrusion on a single-screw desktop machine followed by printing. The plastic matrix comprises polylactic acid (PLA) and polyethylene glycol (PETG), with added powdered by-products: seashells, car glass and mill scale (metal). Additives will be incorporated at 10% and 20% by weight, with two grain sizes: up to 0.09 mm and up to 0.018 mm. Mechanical tests (tensile, flexural and hardness) and thermal characterization tests will be conducted. Findings suggest adding 10%w powder of any variety to PETG increases tensile strength up to 48%, with metal powder (mill scale) showing the highest enhancement, even at 20%w, resulting in a 41% increase. Conversely, adding powder to PLA worsens mechanical properties without stiffening the material; instead, the elastic modulus decreases. Metal grain size has minimal impact, with grain sizes lower than 0.09 mm optimal for PLA. Thermal conductivity in polymers blended with powder additives is lower than in virgin polymers, likely due to air void formation, supported by density and microscopic evaluations. This research underscores the potential of utilizing waste materials with a simple FFF filament production to enhance sustainability in 3D printing practices.