Manufacturing Process Research Articles

Autoencoder-based inverse design and surrogate-based optimization of an integrated wet granulation manufacturing process

In pharmaceutical manufacturing, integrating model-based design and optimization can be beneficial for accelerating process development. This study explores the utilization of Machine Learning (ML) techniques as a surrogate model for the optimization of a three-unit wet-granulation based flowsheet model for solid dosage form manufacturing . First, a reduced representation of a wet granulation flowsheet model is developed, incorporating a granulation and milling process, along with a novel dissolution model that accounts for the effect of particle size, porosity, and microstructure on dissolution rate. Two optimization approaches are compared, including an autoencoder-based inverse design and a surrogate-based forward optimization. Both methods address the bi-objective problem of maximizing dissolution time and product yield by identifying the optimal granulation and mill process parameters. For this case study, both approaches were effective and incurred a similar computational cost, averaging under 4 s. However, the autoencoder approach offers an advantage through dimensionality reduction, a feature not available in surrogate-based optimization. Dimensional reduction is particularly beneficial for complex process designs with numerous inputs and outputs. The lower dimensional representation helps improve process understanding through enhanced visualization of the process design space and facilitates feasibility studies involving multiple constraints. The autoencoder-based inverse design introduced in this work showcases an implementation of AI and ML in pharmaceutical process development, demonstrating the potential to enhance process efficiency and product quality in complex manufacturing scenarios.

Additive manufacturing process in the production of ceramics used in armor systems: a review

Additive manufacturing process in the production of ceramics used in armor systems: a review

Grafting of maleic anhydride onto waste acrylonitrile butadiene styrene (ABS) and its application for compatibilization of ABS blends

AbstractIncorporating recycled plastics into the manufacturing process is an essential way to establish a sustainable plastics economy. However, current processes are limited by the degradation of mechanical properties, high cost, and inconsistent product quality compared with their virgin counterparts. Here, we present an upcycling strategy for waste plastics where waste acrylonitrile‐butadiene‐styrene (reABS) is first grafted with maleic anhydride (MAH), then utilized to compatibilize nylon 6(PA6)/ABS and PA6/ABS/CaCO3 blends. The reABS‐g‐MAH exhibits improved Young's modulus and tensile strength, but lower strain at break and impact strength compared with reABS. As an additive, reABS‐g‐MAH effectively compatibilizes PA6/ABS and PA6/ABS/CaCO3 blends, indicated by greatly decreased PA6 domain size, homogeneous dispersion of fillers, and narrowed glass transition temperature regions between PA6 and ABS. Adding only 5 wt% reABS‐g‐MAH increases the Young's modulus, strain at break, and impact strength of PA6/ABS blends by 26%, 180%, and 110%, respectively, compared with uncompatibilized samples. Similarly, for PA6/ABS/CaCO3 blends, the strain at break and impact strength increase by 370% and 150%, respectively, while the Young's modulus and tensile strength show increases of 5% and 10%. The results show that this strategy of using polar groups to functionalize waste plastics as compatibilizers may open numerous opportunities for upcycling waste plastics.

An integrated simulation and experimental study of calendering process in water-based manufacturing of lithium-ion battery graphite electrode

Lithium-ion batteries produced from water-based manufacturing processes are favored for their environmental and economic advantages, while currently sacrificing some technical performance. This paper reports an integrated study on the calendering process of water-based manufacturing of lithium-ion battery graphite electrode, aiming to improve electrochemical performance of the manufactured lithium-ion batteries. The interactive mechanism between the calendering process and the porous microstructure of the produced graphite electrode was systematically investigated. It reveals that the graphite electrode with 40 % of porosity produced under 79,509 N/m calendering force achieves an optimal 333 mAh/g of specific capacity and 91.2 % of capacity retention after 100 cycles.

Design and Assessment of an Austenitic Stainless Alloy for Laser Powder Bed Additive Manufacturing

Recent developments in metallic additive manufacturing (AM) processes for the production of high-performance industrial pieces have been hampered by the limited availability of reliably processable or printable alloys. To date, most of the alloys used in AM are commercial grades that have been previously optimized for different manufacturing techniques. This study aims to design new alloys specifically tailored for AM processes, to minimize defects in the final products and to optimize their properties. A computational approach is proposed to design novel and optimized austenitic alloy compositions. This method integrates a suite of predictive tools, including machine learning, calculation of phase diagrams (CALPHAD) and physical models, all piloted by a multi-objective genetic algorithm. Within this framework, several material-dependent criteria are examined and their impact on properties and on the occurrence of defects is identified. To validate our approach, experimental tests are performed on a selected alloy composition: powder is produced by gas atomization and samples are fabricated by laser powder bed fusion. The microstructure and mechanical properties of the alloys are evaluated and its printability is compared with a commercial 316L stainless steel taken as a reference. The optimized alloy performs similarly to 316L in terms of coefficient of thermal expansion, hardness and elongation, but has a 17% lower yield strength and ultimate tensile strength (UTS), indicating that further optimization is required.

Study on surface chemical composition of argon atomized superalloy powder based on TOF-SIMS

Nickel-based powder metallurgy superalloys have been extensively employed in aerospace industry because of their superior performance. However, severe defects can be formed in primary particle boundaries (PPB) during manufacturing process of superalloys, significantly deteriorating their practical performance. This study comprehensively investigates the surface structure, elemental distribution, and chemical state of FGH96 superalloy powder via several advanced surface characterization techniques. The results indicate that the strong binding affinity between metal elements (e.g., Al, Cr, Ti) and O/H elements contributes to the formation of an oxide layer in both surface and interval areas, providing an essential chemical basis for the development of PPB. Furthermore, Ti and Al exhibit the highest binding strength with O, which may form stable oxides. Hopefully, these findings will facilitate the emergence of PPB-free superalloys with improved performance.

Increasing the Interlayer Fracture Toughness of Polymer Fabric Composites Using Local 3D-Reinforcement (Felting)

Introduction. One of the reasons for undesirable delamination of polymer composites with fabric reinforcement is low transverse shear properties. It is known that the reinforcement of polymer fabric composites in the Z direction reduces the sensitivity to delamination and increases the viscosity of interlayer fracture. Various methods of three-dimensional reinforcement of polymer fabric composites are proposed in the literature. However, they complicate the manufacturing process of the structure. The problem is solved by the method of three-dimensional reinforcement proposed in this article — felting. This is a local reinforcement of the composite in the Z direction with minimal production changes. The degree of Z-reinforcement is determined by the felting density, i.e., the number of needle punches per 1 cm2 of the fabric package. The work is aimed at evaluating the effect of felting on the interlayer crack resistance of a composite material.Materials and Methods. The interlayer fracture toughness GIIc was determined on a cross-woven fiberglass with felting of 10 cm-2. The material was impregnated with Etal-370 resin and Etal-45 hardener. Experiments according to ASTM D7905M–14 and GOST 33685–2015 standards were carried out on an Instron 5900R test machine. The stress state at the crack tip was analyzed with regard to the nonlocal strength theory in the ANSYS Workbench program (option “static strength analysis”). The finite element method (FEM) was used.Results. The “load — displacement” curves were considered for the samples. Values GIIc were calculated. The results of ENF tests for felting density of 0 cm–2 and 10 cm–2 were summarized. Control samples and felting samples were compared. In the latter case, GIIс turned out to be ~33% higher. The stress state at the crack tip was calculated under DCB and ENF loading. The dependences of maximum normal and shear stresses, as well as displacements, were visualized in the form of graphs and color charts. To get the calculated “load — displacement” dependences using FEM, the reverse method of obtaining transverse shear constants was used. DCB loading showed that felting provided increasing the rupture strength in the Z direction to ~18%, by 39 to 46 MPa, and in the planes XZ— to ~16%, by 77 to 89 MPa.Discussion and Conclusion. Felting as a method of local three-dimensional reinforcement enhances the interlayer crack resistance of polymer fabric composites. It provides reducing the area of stratifications after local impacts during the operation of structures. Flexible felting technology makes it possible to create zones with an arbitrary impact density, increasing fracture toughness only in the required places of structures. The FEM analysis of the stress state at the crack tip within the framework of the nonlocal strength theory has shown that in strength calculations, the stratification crack can be considered as a stress concentrator.

Exploring the Manifestation of Design for Manufacturing Heuristics in Students' Early-Stage Engineering Design Concepts

Abstract Additive manufacturing (AM) can produce designs in a manner that greatly differs from the methods used in the older, more familiar technologies of traditional manufacturing (TM). Despite this method of manufacturing designs that were seemingly inconceivable in the past, designers often forgo the new design considerations of AM and instead rely on their familiarity with the design considerations more commonly associated with TM, regardless of the manufacturing process they intend to use. This can lead to unnecessary material usage, increased manufacturing time, and can result in designs which are poorly manufactured. To remedy this problem, there is a need to intervene early in the design process to help address any concerns regarding the use of AM design heuristics. This work aims to address this opportunity through a preliminary exploration of the design heuristics that students naturally leverage when creating designs in the context of TM and AM. With the common design heuristics that students are most likely to use in their designs identified, future studies will cement early-stage design support tools that emphasize the significant heuristics found herein. For example, this work found that the design heuristic “incorporating complexity” was the most significant indicator of designs most suited for AM and should therefore be highly encouraged/emphasized when guiding designers in the use of AM. In doing so, it will be possible for early-stage design support tools to maximally improve designs that are intended to be manufactured for AM.

Biomimetic Contact Behavior Inspired Tactile Sensing Array with Programmable Microdomes Pattern by Scalable and Consistent Fabrication.

Flexible sensor arrays have attracted extensive attention in human-computer interaction. However, realizing high-performance sensor units with programmable properties, and expanding them to multi-pixel flexible arrays to maintain high sensing consistency is still struggling. Inspired by the contact behavior of octopus antenna, this paper proposes a programmable multistage dome structure-based flexible sensing array with robust sensing stability and high array consistency. The biomimetic multistage dome structure is pressurized to gradually contact the electrode to achieve high sensitivity and a large pressure range. By adjusting the arrangement of the multistage dome structure, the pressure range and sensitivity can be customized. More importantly, this biomimetic structure can be expanded to a multi-pixel sensor array at the wafer level with high consistency through scalable and high-precision imprinting technologies. In the imprinting process, the conductive layer is conformally embedded into the multistage dome structure to improve the stability (maintain stability over 22000 cycles). In addition, the braced isolation structure is designed to effectively improve the anti-crosstalk performance of the sensor array (crosstalk coefficient: 26.62dB). Benefitting from the programmable structural design and high-precision manufacturing process, the sensor array can be customized and is demonstrated to detect human musculation in medical rehabilitation applications.

Determinants of Carbon Inventory and Systematic Innovation Methods to Analyze the Strategies of Carbon Reduction: An Empirical Study of Green Lean Management in Electroplating an Factory

This study explored the relationship between lean management, carbon inventory, and carbon reduction, along with the main factors affecting factory carbon inventory and carbon reduction, and proposed a set of carbon emission reduction strategies based on green value stream mapping. An electroplating factory and its production line in Taiwan was selected. Fourteen carbon inventory records, three work circle meetings, and green value flowcharts were utilized to collect and analyze data. Furthermore, this study applied the DMAIC framework and TRIZ to solve the issue of carbon emissions. The key factor affecting factory carbon inventory and carbon reduction was found to be electricity waste, which could be reduced through energy-saving methods. After analyzing the green value stream mapping, effective carbon emission reduction can be implemented in the manufacturing process. The company was found to gradually progress towards carbon reduction after investigating seven major green wastes. This study confirms that lean management can help organizations achieve their carbon emission reduction goals and is valuable in improving the organization’s environmental performance and competitiveness.

Application of scalable sensor-assisted multi-scale computational methods in the simulation of micro mechanical behavior of composite materials

Micro mechanics involves the examination of the mechanical behavior of heterogeneous materials, taking into account inhomogeneities such as voids, fractures, and inclusions, and building on mathematical models developed. Composites bring engineering design barriers despite their strengths. Composite manufacturing and processing need specialized equipment, strategies, and labor, ensuring they are challenging and expensive. Mechanical behavior deals with how a composite material performs if faced with mechanical effects and action. Scalable sensor-assisted multi-scale computational Methods (SS-MSCM) are used to investigate topics ranging from the molecular basis of soot production in combustion to how molecule-level flaws influence macroscopic mechanical qualities. Carbon fiber reinforced polymers (CFRP) are generated by mixing graphene fiber with a resin, like vinyl ester and epoxy, to render a composite material with superior performance to the component ingredients. Hence, SS-MSCM-CFRP has Improved mechanical qualities achieved by incorporating nano-reinforcements, including carbon nanofibers and graphene nanoplates, into the CFRP matrix: enhanced flexural and compressive strengths, energy absorption upon impact, toughness to fracture, and interlaminar bonding. Composite materials feature excellent mechanical qualities like high strength and stiffness, fatigue resistance, and durability. It is possible to insert scalable sensors throughout the manufacturing process, which enables real-time monitoring of structural health, strain, and other factors. Scalable sensor-assisted multi-scale computational methods offer enhanced accuracy, real-time monitoring, and cost-effectiveness by integrating sensor data with computational models, improving predictions and failure mechanism insights. However, they face limitations like sensor dependency, computational complexity, data integration challenges, and high implementation costs, leading to potential discrepancies between simulation and experimental results. Important qualities include corrosion resistance, thermal conductivity, and electrical conductivity. As the composite materials develop to satisfy the established mechanical stress and temperature conditions, they offer high durability and strength.

Machine learning approach for predicting the machining characteristics of additive manufactured ABS material

An Additive manufacturing process offers advantage for the production of intricate designs with increased efficiency and reduced material waste. In this research, an experimental and analysis work has been conducted to identify the significant 3-D printed input parameters effecting the output response characteristics as kerf width. The correlation between experimental and predicted values has been established through the application of Artificial Neural Network (ANN), Support Vector Machine Regression (SVMR), and Response Surface Methodology (RSM) techniques. The validation of predicted models conducted with the help of mean prediction error, maximum error and minimum error. The performance of ANN model was analyzed by feedforward-propagation method using Levenberg- Marquardt algorithm. The maximum prediction error investigated by ANN, SVM, and RSM were noted as 4.28, 5.18, and 5.84 respectively. The optical micrographs have been investigated to understand the hole and crater formation phenomenon.

Experimental investigation on the surface topographical improvement of selective laser melted SS316L using abrasive flow finishing

Selective laser melting (SLM) offers a precise fabrication of intricate geometries that are not achievable with conventional manufacturing processes. However, surface imperfections like adhered powder particles, waviness, and other anomalies impede the intended functional application and demand additional post-processing steps. Abrasive flow finishing (AFF) process is extensively used for enhancing the surface topography of SLM-built parts by reciprocating a viscoelastic polymer abrasive medium. The cost of commercial abrasive media is expensive, needs costlier and heavy equipment, has little tolerability, and releases hazardous gases during its preparation. In this work, a biopolymer-based abrasive media is developed in-house and employed to examine the surface topographical changes of SLM-built SS316L flat features. Compared to commercial abrasive media, the cost of the developed abrasive media is much lower, does not require expensive equipment, and is eco-friendly. The effect of extrusion pressure, number of finishing cycles, and size of the abrasive particles on the percentage change in areal surface roughness (%ΔSa) and waviness (%ΔWa) was studied. From the analysis of variance, irrespective of %ΔSa and %ΔWa, it was found that the number of cycles was the most contributing process parameter, followed by extrusion pressure and abrasive particle size. The optimised process parameters resulted in a maximum %ΔSa and %ΔWa of about 90.1 % and 66.25 %, respectively, from the initial as-built areal surface roughness of 6.6 ± 0.2 μm and areal surface waviness of 7.7 ± 0.4 μm.

Recycling Materials for Sustainable DNA Origami Manufacturing.

DNA origami nanotechnology has great potential in multiple fields including biomedical, biophysical, and nanofabrication applications. However, current production pipelines lead to single-use devices incorporating a small fraction of initial reactants, resulting in a wasteful manufacturing process. Here, we introduce two complementary approaches to overcome these limitations by recycling the strand components of DNA origami nanostructures (DONs). We demonstrate reprogramming entire DONs into new devices, reusing scaffold strands. We validate this approach by reprogramming DONs with complex geometries into each other, using their distinct geometries to verify successful scaffold recycling. We reprogram one DON into a dynamic structure and show both pristine and recycled structures display similar properties. Second, we demonstrate the recovery of excess staple strands postassembly and fold DONs with these recycled strands, showing these structures exhibit the expected geometry and dynamic properties. Finally, we demonstrate the combination of both approaches, successfully fabricating DONs solely from recycled DNA components.

Enhanced sensing properties of optical ammonia sensor based on electrospun fibers containing Eosin-Y and silver nanoparticles

This study presents a highly sensitive fluorescence intensity-based gas sensor specifically designed to detect ammonia. Eosin-Y fluorescent dye and silver nanoparticles are incorporated into` cellulose acetate to fabricate fibers membrane using the electrospinning technique, which serves as the foundation for the optical ammonia sensors. Afterward, this sensor is illuminated by a 405 nm LED for monitoring purposes, resulting in changes to both intensity and wavelength. The sensitivity of the optical ammonia sensor is evaluated by comparing the fluorescence intensity recorded for pure nitrogen with that for 1000 ppm ammonia, expressed as the ratio I0/I1000 ppm. The sensor's ability to detect ammonia is evaluated based on the differences in fluorescence intensities between nitrogen and ammonia environments. The proposed optical ammonia gas sensor utilizes Eosin-Y molecules as indicator compounds. Notably, the sensor, constructed from an electrospun fiber membrane, demonstrates an exceptional response of 50.9 at 1000 ppm in an ammonia gas environment at room temperature, representing the highest response achieved to date in an ammonia gas sensor utilizing Eosin-Y molecules. Experimental findings highlight the enhanced sensitivity of the proposed Eosin-Y-containing electrospun fibers compared to traditional Eosin-Y-based dyes for optical ammonia sensing. The development of an optical ammonia sensor utilizing electrospun fibers embedded with fluorescent dye presents a promising opportunity, offering practical applicability due to its cost-effectiveness and straightforward manufacturing process. Finally, the innovative optical ammonia sensor approach can be applied across various fields, including environmental, industrial, and medical applications.

Mechanical Properties and Microstructure of Laser Powder Bed Fusion‐Processed 18Ni300 Maraging Steel According to Direct Aging Treatment Conditions

The nonequilibrium microstructures produced by laser‐based additive manufacturing can enhance the mechanical properties of metallic alloys. However, the optimal post‐treatment conditions will change according to the initial microstructure induced by the manufacturing process employed. Therefore, this study investigates the optimal direct aging conditions for the post‐treatment of laser powder bed fusion‐processed 18Ni300 maraging steel by changing the aging temperature and time. The aging time required to achieve maximum tensile strength decreased as the aging temperature increased, and an increase in the aging temperature accelerated the evolution of nanoscale precipitates. The results indicate that the optimal direct aging conditions of 520 °C for 2 h achieved the largest tensile strength (≈2100 MPa) and precipitate area fraction. Notably, the reduced aging time at this temperature compared with that under conventional aging conditions (8 h at 460 °C or 490 °C) minimizes the thickening of the (Mo,Ti)‐rich segregation, which otherwise contributes to austenite reversion and strength reduction owing to overaging. Thus, the optimal direct aging conditions for additively manufactured maraging steel components are different from those for conventionally produced maraging steel components. This understanding of the microstructural changes induced by direct aging according to the initial non‐equilibrium state provides vital information for enhancing the performance of additively manufactured metallic alloys.

Thermal Conductivity of 3D Printed Metal using Extrusion-based Metal Additive Manufacturing Process

Abstract In this study, the thermal conductivity of 3D-printed 316L stainless steel parts using the bound metal deposition (BMD) method, an extrusion-based 3D printing technology, were examined experimentally and validated numerically using finite element analysis (FEA). Various critical printing parameters were examined, including infill density, skin overlap percentage, and print sequence to study their effect on the printed thermal conductivity. A heat conduction experiment was performed on the 3D-printed samples of 316L stainless steel followed by a FEA. The results from this investigation revealed that an increase in 3D printing infill density correlated with a rise in effective thermal conductivity. Conversely, a substantial decrease in thermal conductivity was observed as porosity increased. For instance, at a porosity level of 16.5%, the thermal conductivity experienced a notable 33% reduction compared to the base material. The skin overlap percentage, which governs how much the outer shell of adjacent layers overlaps, was found to impact heat transfer across the overall part surface. A higher overlap percentage was associated with improved thermal conductivity, although it could affect the surface finish of the part. Furthermore, the study explored the print sequence, focusing on whether the outer wall or infill was printed first. Printing the outer wall first resulted in higher thermal conductivity values than that obtained from printing the infill first. Therefore, it is crucial to carefully consider these factors during the BMD 3D printing process to achieve the desired thermal conductivity properties.

Thermomechanical Assessment of Recovered PA12 Powders with Basalt Filler for Automotive Components

Additive manufacturing processes allow for precise and efficient production, but it is estimated that one-third of the materials used results in waste. Further improvement in a sustainable perspective could come from the ability to manage these scraps and from the exploration of different routes for recovery and reuse. The Selective Laser Sintering process is particularly sensitive to this issue due to the waste ratio which can reach a very high quantity of not-sintered virgin powders. In this research study, recovered PA12 powders, preliminarily characterized through thermal and mechanical analysis, were mixed with 15% basalt powder to improve their aspect and thermomechanical resistance. The influence of basalt powder (BP) on mechanical properties as well as on the thermal stability of polyamide12 (PA12) powder composites was investigated. A study conducted on mechanical properties showed that polymeric composites’ stiffness and hardness were influenced by adding filler, thus improving mechanical parameters. On the other hand, the application of thermogravimetric analysis allowed us to determine the composite’s thermal stability. The objective is to obtain a recovered fully biobased material that could be used to substitute the petroleum-derived polymeric ones currently employed in the production of interiors and shells in the automotive sector.

Rapid investment casting: a techno-economic analysis for evaluating VAT photopolymerisation processes

Adopting additive manufacturing in investment casting, known as rapid investment casting, can significantly reduce lead time and cost. Despite many publications on these technologies, no quantitative techno-economic analyses refer to rapid investment casting. The paper provides helpful insights and a decision-making approach for replacing conventional production technologies (i.e. wax injection) with additive manufacturing technologies. The study is based on a techno-economic analysis framework. It allows assessing key performance indicators (e.g. crossover points and payback periods) for stakeholders to decide the suitable VAT photopolymerisation processes (i.e. stereolithography, digital light processing, and material jetting) according to specific production scenarios (i.e. load factors). Analytical cost models were developed in the frame of this work to assess the time and cost of the overall manufacturing process of the resin model for investment casting, considering the post-processing stages for each of the technologies investigated. The cost models were used to assess the benefits introduced by additive manufacturing in rapid investment casting. A sensitivity analysis evaluated the impact of energy, material, and labour costs for both key performance indicators (crossover points and payback periods). The techno-economic analysis yielded the following results: (i) crossover points in terms of production costs vary, ranging from thousands of units for small components in the jewellery or fashion industries to a few dozen units for larger parts in the mechanical industry, and (ii) crossover points in terms of time are lower than those related to cost. Additionally, digital light processing is regarded as the most promising technology, offering the best crossover points and payback periods across various scenarios, mainly when 3D printers are used at a low utilisation rate.

Deep reinforcement learning for solving car resequencing with selectivity banks in automotive assembly shops

In the automobile manufacturing process, the complexity of the production environment and the different objectives of the upstream and downstream shops necessitate the resequencing of the car sequences using buffers. This paper addresses a car resequencing problem in automotive assembly shops by utilising selectivity banks to reshuffle the given upstream sequences to minimise the number of violations of sequencing rules in the downstream sequence. A new resequencing approach combining heuristics and deep reinforcement learning is proposed to solve the problem. Heuristics solve the automobile storage problem, while a deep reinforcement learning method based on proximal policy optimisation addresses the automobile release problem. Three state matrices, agent actions, and the reward function are designed based on the characteristics of the objective and selectivity banks. Additionally, a novel network structure combining convolutional neural networks and long short-term memory networks is proposed to train the agent. The results demonstrate that the proposed method outperforms two efficient deep reinforcement learning algorithms and two heuristics and obtains better solutions in handling dynamic events.