Performance Of Manufacturing Process Research Articles

Optimization of Chitosan-Carboxymethyl Chitosan Membrane Modification with PVA to Increase Creatinine and Urea Permeation Efficiency

Optimization of Chitosan-Carboxymethyl Chitosan Membrane Modification with PVA to Increase Creatinine and Urea Permeation Efficiency

Porous carbon-coated silicon composites for high performance lithium-ion batterie anode

With the rapid development of silicon-based lithium-ion battery anode, the commercialization process highlights the importance of low-cost and short-flowproduction processes. The porous carbon/silicon composites (C/Si) are prepared by one-step calcination using zinc citrate and nano-silicon as the primary raw materials at a temperature of 950 °C. In this composite, silicon nanoparticles are coated with a porous carbon shell with an average pore diameter of 4.49 nm, which improves the structural stability and electrochemical performance of the anode. Density-functional theory calculations show that the porous carbon enhances the electrical conductivity, facilitates the interlayer migration of Li-ion, and strengthens the gain effect of stress on conductivity. Additionally, stress further reduces the diffusion barriers of Li-ion. The porous C/Si anode demonstrates outstanding specific capacity and rate capability, maintaining a stable specific capacity of 1170 mAh/g at a current density of 0.2 A g−1 after 100 cycles and retaining a reversible specific capacity of 944 mAh/g even at a charging current density of 2 A g−1. The carbon/silicon anode materials prepared using this strategy present a streamlined manufacturing process and exceptional performance, as well as avoiding the post-washing step, providing a pathway toward the scalable application of silicon-based anodes.

Evaluation of hard petroleum asphalt's low-temperature performance and index optimisation based on the grey correlation model

Hard petroleum asphalt (HPA) has good application prospects because of its simple manufacturing process and excellent performance. However, according to previous studies, the performance of HPA at low temperatures cannot be well characterised by commonly used methods. The use of HPA in cold climates is severely constrained. In order to comprehensively evaluate the low-temperature performance of the material, this study used four different types of HPA as the research object, and used the bending beam rheological test ( BBR) to determine the basic rheological properties of the material. On this basis, after fitting and analysing the low-temperature creep compliance curve using the Burgers viscoelastic model, the asphalt was assigned a low-temperature viscoelastic assessment index. The glass transition temperature and sensitivity of HPA were investigated by differential scanning calorimetry (DSC). At the same time, the HPA assessment index at low temperatures was determined systematically by using grey correlation theory.

On developing a green and intelligent manufacturing system

This paper aims to present an autonomous intelligent system that improves the performance of conventional manufacturing processes to make them friendly to the environment. This improvement is made considering objectives that involve a reduction of supplies, use of tools, and low energy consumption; but maintaining desirable characteristics of machined parts in quality, mechanical properties, and dimensions. We present an architecture that integrates modeling with neural networks, validation using metrical statistics, and multi-objective optimization based on MOEA/D which takes experimental or historical data of the process and generates a set of parameters to satisfy all three criteria of sustainability, efficiency, and quality. Two cases for welding and machining process are presented to illustrate the use of the proposed approach which results in evidence of the feasibility of having green and intelligent manufacturing systems. In the welding process, the user can select a low amperage but increase lightly the cycles of operation to reduce energy consumption but satisfy the quality objective. In machining, the user can select a solution to avoid the use of cooling maintaining quality and efficiency in acceptable levels of operation.

Effects of Preparation Conditions of Precursor Powders on Sinterability of Green Bodies and Performance of Protonic Ceramic Electrochemical Cells

As one of promising technologies for hydrogen production and valuable chemical production, protonic ceramic electrochemical cells (PCECs) work at relatively low temperatures (400-600oC), due to high ionic conductivity and low activation energy. However, some undesired structural morphologies and defects presenting in the electrolyte layer during fabrication of PCECs, such as relatively small grain sizes, pinholes, and poor electrolyte/electrode interfaces, presenting detrimental effects on electrochemical performance and durability. PCECs are conventionally fabricated from proton conducting precursor powders, which are usually synthesized by solid state reaction (SSR) method, especially for large quantity fabrication. Thus, the properties and quality of electrolyte precursor powders are crucial for cell performance. Herein, we investigate the influence of fabrication conditions for electrolyte precursors on both manufacturing process and consequential electrochemical performance of PCECs. The present study sheds a light on the discrepancies on the PCEC performance reported in the literature and paves a way for scale-up of PCEC technology.

The transition from batch to continuous manufacturing for tablet manufacturing performance comparison and control system review

The pharmaceutical industry is motivated to improve tablet productivity in order to adapt to the expanding drug market. One innovative way is the transition of the traditional batch tablet manufacturing process to a continuous process. This paper aims to review the performance of continuous tablet manufacturing processes. From the comparison of continuous to traditional batch processes, the continuous tablet manufacturing process demonstrates improvements in production flexibility, robustness, and ultimately process profitability. The continuous process is proven to mix drug particles with more evenly distributed size and less segregation resulting in tablets with better quality compared to the traditional batch process. The improvement on the continuous tablet manufacturing process requires robust control system for process automation to counteract process noise and disturbances in process feed. A well-tuned Proportional Integral Derivative (PID) control is sufficient for the process to produce a desirable product. Developing a new algorithm for model predictive control (MPC) and enhancing the control system with MPC-PID control can further improve the system performance. Overall, the transition from batch to continuous tablet manufacturing is supported by pharmaceutical companies as well as organizations like Food and Drug Administration (FDA).

Technological solutions and adaptive processing tools to mitigate the impact of seasonal variations in milk composition on Cheddar cheese production—A review

Variability in milk composition and physicochemical properties impact both manufacturing process performance and the end‐use functionality of cheese. Such variability may be attributed to seasonal calving patterns, production systems, breed, weather patterns, and feed type and significantly impacts concentrations of macro‐ and microconstituents and ultimately cheese composition and quality. This article reviews technological approaches (e.g., milk standardisation protocols and calcium addition), and predictive strategies (e.g., predictive models for coagulation and curd cutting time, in‐line sensors), used to mitigate the effects of seasonal changes in milk composition and their impact on process efficacy and functionality in Cheddar cheese manufacturing.

The usage of Root Cause Analysis (RCA) in Industry 4.0 conditions

Purpose: The purpose of this publication is to present the usage of Root Cause Analysis (RCA) approach in Industry 4.0 conditions. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: The integration of Root Cause Analysis (RCA) within Industry 4.0 emerges as a strategic imperative, offering a systematic and holistic approach to problem-solving in the dynamic landscape of advanced technologies and interconnected systems. RCA serves as a powerful tool for dissecting multifaceted issues arising from the convergence of physical and digital systems, crucial in understanding and mitigating problems linked to smart technologies, automation, and data-driven processes. In the context of Industry 4.0's complexities, RCA facilitates a comprehensive examination of events, enabling organizations to identify the fundamental causes of disruptions or inefficiencies. The systematic investigative approach of RCA proves indispensable for navigating the intricate web of factors influencing the performance of smart manufacturing processes, cyber-physical systems, and the Internet of Things (IoT). As industries prioritize real-time data collection and analysis, RCA becomes instrumental in deciphering patterns and correlations within the data deluge, guiding informed decision-making and proactive problem resolution. Overall, RCA's integration into Industry 4.0 signifies a strategic method for fostering resilience, continuous improvement, and sustainable success within this dynamic and technologically-driven landscape. Originality/Value: Detailed analysis of all subjects related to the problems connected with the usage of Root Cause Analysis in Industry 4.0 conditions. Keywords: Industry 4.0; Quality 4.0, quality management; quality methods, RCA, Root Cause Analysis. Category of the paper: literature review.

Advances in the research of carbon electrodes for perovskite solar cells.

Perovskite solar cells (PSCs) were first proposed in 2009. They have the advantages of low cost, a simple manufacturing process and excellent photoelectric performance. PSC electrodes are mainly made from precious metals such as gold and silver. Still, the cost of precious metals is high and they react with the other components of the PSCs, resulting in the poor stability of the photovoltaic device. Using carbon as an electrode material can both reduce the cost and significantly improve the stability of the photovoltaic device. However, the poor interface contact between the carbon electrode and perovskite and carbon electrode resistance results in poor photovoltaic device photoelectric performance. Finding a way to successfully utilize carbon as an alternative electrode material is a key step toward moving PSCs from the laboratory to industrialization. This paper reviews the application of carbon black, graphite, graphene, carbon nanotubes (CNTs) and composite carbon electrode in PSCs, focusing on progress in the research of doping, structure, interface modification and the production process.

Experimental validation of numerical model for thermomechanical performance of material extrusion additive manufacturing process: Effect of infill design & density

The optimum selection of process parameters, materials, and product design is essential to achieve the desired response of 3D-printed structures, especially in functional components. The current practices of the experimental optimization process require significant resources, which can be limited through numerical modeling and simulation techniques. In this study, a thermomechanical numerical model is used to predict the performance of the additive manufacturing (AM) process, i.e., fused filament fabrication (FFF). 3D printing (3DP) process simulations were performed for tensile testing coupons using carbon fiber-reinforced polyamide-6 (PA6-CF) material. The numerical model predicted the effect of infill patterns and densities on the deflections and distortions during the FFF process. The numerical model predictions were validated via experiments performed under similar conditions. The results conclude that the numerical model can adequately predict the process-induced deflections and distortions during the FFF process. Generally, higher dimensional control was observed for rectangular infill patterns and increased infill density. However, the numerical model overestimates the shrinkage as the stress-relaxation effect is not considered in the numerical model and underestimates the warpages as perfect build plate adhesion is assumed.

Automated assembly of hybrid dynamic models for CHO cell culture processes

The emergent realisation of Industry 4.0 principles across biomanufacturing, through recent endeavours, will markedly enhance the development and manufacture of modern therapeutics. Through implementation of digital process models, a greater understanding of the intricate relationship between product quality attributes and manufacturing process performance may be established. While contributing towards accelerated process development, representative process models enable advanced optimisation of process parameters, thus having a tangible impact on the assurance of product quality and manufacturing robustness. Hybrid approaches, which couple mechanistic interpretability with statistical data-fitting, are posed to broaden the value and utility of digital models. To augment the advancement in modelling techniques and high-throughput technology, there is a growing requirement for automated approaches towards data processing and model assembly. In this study, a novel strategy is proposed, which leverages saturation and sigmoidal relationships, along with an underlying material balance framework, for the automated assembly of hybrid dynamic models of cell growth. The proposed hybrid model is compared against an equivalent mechanistic model based on Monod expressions. While both models achieve a reasonable fit against experimental data, the hybrid model demonstrates superior predictive performance. Development of automated hybrid models, as demonstrated in this study, may greatly accelerate process digitalisation across biopharmaceutical manufacture.

High-power distributed feedback lasers with high-order surface curved gratings.

A novel, to the best of our knowledge, broad-area distributed feedback laser with high-order surface curved gratings has been fabricated based on standard near ultraviolet lithography. The characteristics of increasing output power and mode selection are achieved simultaneously by using a broad-area ridge as well as an unstable cavity structure composed of curved gratings and a high-reflectivity coated rear facet. Suppression of high-order lateral modes is realized by setting current injection/non-injection regions and asymmetric waveguides. This DFB laser emitting around 1070 nm has achieved a spectral width of 0.138 nm and a maximum output power of 915 mW kink-free optical power. The threshold current and side-mode suppression ratio of the device are 370 mA and 33 dB, respectively. The simple manufacturing process and stable performance give this high-power laser broad application prospects in the fields of light detection and ranging, laser pumps, optical disk access, etc.

A two-step machining and active learning approach for right-first-time robotic countersinking through in-process error compensation and prediction of depth of cuts

• Two-step process method for online compensation and prediction of machining errors • Data-driven models require large training datasets for accurate predictions • Training data is very expensive to obtain for high-value manufacturing processes • Active learning is used to identify the most informative data for model training • Right-first-time robotic machining and reduced inspection costs are achieved Robotic machining processes are characterised by errors arising from the limitations of the industrial robots. These robot-related errors can compromise the overall manufacturing process performance, resulting in final products with dimensions different from the nominal specifications. To avoid accumulation of errors through several manufacturing stages, a quality inspection step is usually performed after the cutting operation. This work presents an innovative two-step manufacturing method for achieving right-first-time characteristics in robotic machining operations through in-process inspection and compensation of the systematic errors, whilst collecting suitable training data for building predictive models. The key idea behind the proposed method is based on the observation that under certain conditions, the robotic machining errors remain largely consistent, and therefore by splitting the process into two similar steps and having an inspection step in between, a prediction and then compensation of the systematic errors would be possible. A Gaussian Process Regression (GPR) framework is applied for the creation of robust process models that predict the post-process inspection result from in-process signal features, with the associated confidence intervals. An active learning algorithm that makes online decisions on the inspection task based on the current confidence of the models, is also proposed. The two-step machining method and the active learning approach were both tested on a robotic countersinking process experiment. The results showed that the in-process inspection and error compensation of the proposed two-step machining method was able to achieve final countersink depths very close to the desired target, confirming the potential for right-first-time robotic machining. In addition, the active learning results highlighted the ability of the algorithm to reduce the number of required post-process inspections, thus saving both time and costs, whilst also identifying novel data relevant for the model training.

Manufacturing high-performance formcoke from pulverized coal using potato residue-based biomass binders

Formcoke is a more favorable and cleaner fuel than coal because of its high carbon content, high calorific value, and low ash content. The identification and sourcing of high-performance, cost-effective, and green binders for formcoke production from pulverized coal are required but remain a big challenge. Herein, potato residue was composited with magnesium chloride as a bio-based composite binder for the production of high-performance formcoke. The compression strength of the obtained formcoke was determined as 1967 N. The calorific value of the formcoke was in the same range as lumpy coal. Furthermore, the formcoke exhibited higher thermal stability and required more activation energy in the combustion process than briquette coal and pulverized coal. In addition, the pollutant emissions of the formcoke such as carbon dioxide, sulfur dioxide, hydrocarbons, and other pollutants of the combustion process were significantly less than those of pulverized coal. This research uses potato residue as the primary raw material to prepare a bio-based composite binder for the production of high-performance formcoke with the advantages of a simple manufacturing process, abundant availability, low price, and excellent performance. It provides a new application of potato residue and a high-performance binder preparation method for the production of high-performance formcoke.

Scalable Manufacturing Process and Multifunctional Performance of Cotton Fibre-Reinforced Poly(Lactic Acid) (PLA) Bio-Composites Coated by Graphene Oxide.

Natural fibre biopolymer composites with both fibres and matrix being derived from biomaterials are increasingly used in demanding applications, such as sensing, packaging, building, and transport, and require good electrical, thermal, and flame retardant properties. Herein, an investigation of the effectiveness of functionalising nonwoven cotton/poly(lactic acid) (PLA) fibre mats with graphene oxide nanosheets has been reported by using a facile dip-coating method followed by thermal reduction for enhancing the electric, thermal, and abrasion-resistance properties. The manufacturing processes for preparing biocomposites and introducing functionality are readily scalable. Experimental results reveal that with the addition of less than 0.5 wt% graphene nanoplatelets, the biocomposites showed significant improvements in abrasion resistance, electrical conductivity, thermal conductivity, and diffusivity. Furthermore, the composite shows excellent piezo-resistivity to act as strain sensors with a gauge factor of 2.59 at strains up to 1%.

A review on process parameters, microstructure and mechanical properties of additively manufactured AlSi10Mg alloy

Additive Manufacturing is a layer over layer material depositing process to fabricate 3D shape objects. In this process laser completely melts one powder layer and fuses it with another. Metal additive manufacturing process like selective laser melting can produce intricate shapes which are not possible to produce by conventional methods. This capability has enabled designers to come up with innovative part designs which add value to either overall manufacturing process or product performance, also fulfilling the demands for reducing the cost and manufacturing time. The material selection, process parameters and post process are the significant stages in additive manufacturing to achieve the desired properties of output product. The present paper provides comprehensive review in a systematic manner on selective laser melting printing parameters, microstructure, heat treatment and mechanical properties of additively manufactured AlSi10Mg aluminium alloy and based on the review; few arenas for future research are highlighted.

Integrated MEMS Toroidal Transformer With Ni-Zn Ferrite Core for Power Supply on Chip

A miniature integrable transformer is an obstacle to the miniaturization of packaged power supply on chip (PwrSoC). This letter introduces a through-silicon via (TSV) high-aspect-ratio toroidal transformer manufactured using microelectro-mechanical systems and electroplating. The primary coil and secondary coil of the transformer are interleaved on the toroidal core cavity, which is entirely wrapped in silicon, and a 1.2 mm high toroidal Ni-Zn ferrite core is formed. A 15:15 turn transformer is fabricated with a compact coil size of Φ4.1 × 2 mm with an aspect ratio (ratio of TSV depth to diameter) of 20. Measurements show that it has an inductance of 105 to 115 nH in a high frequency range of 1 to 80 MHz and a coupling coefficient of ∼0.7 in the range of 1 MHz to >15 MHz. Compared to that of the transformer without the Ni-Zn ferrite core, the coupling coefficient of the toroidal transformer increases by 40% with an inductance increase of 20%. At a frequency of 14 MHz, the transformer has a maximum quality factor of 16.7. The small size, IC compatible manufacturing process and excellent performance at high-frequency of the transformer proposed, meet the further integrated and miniaturized needs of PwrSoC.

Experimental Validation of Numerical Model for Thermomechanical Performance of Material Extrusion Additive Manufacturing Process: Effect of Process Parameters.

The material extrusion additive manufacturing (MEAM) process for polymers seems straightforward. However, several controlled and uncontrolled factors affect the 3D printed product quality, e.g., MEAM process parameters, thermomechanical properties of the material, and part design. Therefore, it is crucial to understand these interlinked factors of part geometry, material properties, and 3D printing (3DP) process parameters to optimize 3D printed product quality. The numerical models and simulation tools can predict the thermomechanical performance of the MEAM process under given input parameters (material, design, and process variables) and reduce the research and development costs significantly. However, the numerical models and tools need further exploration and validation of simulation predictions for their adaptability and reliability. Therefore, in this study, numerical simulations were performed to observe the impact of process parameters on the part quality of MEAM 3D printed components. The two crucial process parameters (i.e., extrusion temperature and layer resolution) were varied while keeping the other process parameters, part geometry (tensile testing coupon), and material properties (acrylonitrile butadiene styrene (ABS)) constant. These two process parameters were sequentially optimized for optimum part quality, first by varying the extrusion temperature and secondly by changing the printing resolution using the optimum printing temperature. The 3DP process quality was evaluated in terms of dimensional accuracy, distortions, and residual stresses. Finally, the specimens were 3D printed under similar process conditions to validate the numerical model predictions.

Integrated process model for the prediction of biopharmaceutical manufacturing chromatography and adjustment steps

A fundamental process understanding of an entire downstream process is essential for achieving and maintaining the high-quality standards demanded for biopharmaceutical drugs. A holistic process model based on mechanistic insights could support process development by identifying dependencies between process parameters and critical quality attributes across unit operations to design a holistic control strategy. In this study, state-of-the-art mechanistic models were calibrated and validated as digital representations of a biopharmaceutical manufacturing process. The polishing ion exchange chromatography steps (Q Sepharose FF, Poros 50 HS) were described by a transport-dispersive model combined with a colloidal particle adsorption model. The elution behavior of four size variants was analyzed and included in the model. Titration curves of pH adjustments were simulated using a mean-field approach considering interactions between the protein of interest and other ions in solution. By including adjustment steps the important process control inputs ionic strength, dilution, and pH were integrated. The final process model was capable to predict online and offline data at manufacturing scale. Process variations at manufacturing scale of 94 runs were adequately reproduced by the model. Furthermore, the process robustness against a 20% input variation of concentration, size variant and ion composition, volume, and pH could be confirmed with the model. The presented model demonstrates the potential of the integrated approach for predicting manufacturing process performance across scales and operating units.

Thin Films Characterization and Study of N749-Black Dye for Photovoltaic Applications

This paper reports on the fabrication and photovoltaic characteristics of a heterojunction solar cell based on an organic small molecular semiconductor, N-749 black dye (N749-BD). To investigate the photovoltaic characteristics of N749-BD, an ITO/PEDOT:PSS/N749-BD/Ag device is prepared by spin casting a 100 ± 5-nm thin film of N749-BD on the poly(3,4, ethylene dioxythiophene):polystyrene sulfonate (PEDOT:PSS) film, which acts as buffer/hole transport layer (HTL) and indium tin oxide (ITO) is employed as a transparent conducting substrate. Under standard testing conditions (STC), i.e., 25 °C, 1.5 AM global and 100 mW/cm2 irradiation, the photovoltaic parameters of the device, such as fill factor (FF) and power conversion efficiency (PCE), are found to be 0.65 and 3.8% ± 0.5%, respectively. Current-voltage (I–V) characteristics of the device are also studied in dark conditions to measure reverse saturation current (I0), series resistance at the interface, rectification ratio (RR), barrier height (ϕb) and ideality factor (n). Optical bandgaps (Eg) of N749-BD thin film are found by applying Tauc’s plot on its ultraviolet-visible (UV-Vis) spectrum, which are measured to be 1.68, 2.67, 3.52 and 4.16 eV. External quantum efficiency (EQE) measurements of the fabricated device are studied, which demonstrate large value of EQE ≈ 12.89%, with peak intensity at 626 nm. Bond dynamics and compositional analysis of N749-BD is carried out via Fourier transformed infrared (FTIR) spectroscopy. Morphology of the thin film of N749-BD on quartz glass are investigated via scanning electron microscopy (SEM) with in-situ energy dispersive X-ray (EDX) spectroscopy which exhibits random distribution of N749-BD grains across the surface with nearly uniform grain sizes and shapes. The larger values of FF, PCE and EQE of ITO/PEDOT:PSS/N749-BD/Ag device suggests the potential of N749-BD to be utilized in low cost, simple manufacturing process and high performance of solar cells.