Industrial Manufacturing Processes Research Articles

Solidification Microstructure and Segregation in the Medium-carbon Steel Cast with a Laboratory-scale Local-chilled Mold

In the industrial steel manufacturing process, such as continuous casting and ingot casting, macrosegregation occurs due to the effect of bridging and contraction flow during the middle and end periods of solidification. Since the macrosegregation results in a nonuniform structure and eventually cracks, there has been a demand for technological development that does not cause macrosegregation in the casting process. In this study, model experiments using the medium-carbon steel cast with a laboratory-scale local-chilled mold at different superheating have been carried out to investigate the relationship between solidified structure and macrosegregation occurred by local bridging during casting. The morphology of shrinkage porosities and dendrite structures was observed. The concentrations of the alloying elements were analyzed for macrosegregation by an electron probe micro-analyzer. Chill plates successfully formed the columnar dendrite bridging area and the columnar dendrite shell in the sample with high superheating. During solidification, the negative pressure of the region below the bridging increased, and the concentrated contraction flow flowed into the bottom of the large shrinkage porosity. Finally, V segregation was formed in the bridging area, large shrinkage porosities remained below the bridging area, and point-like or band-like positive macrosegregation occurred in the interdendritic region between columnar dendrites and equiaxed dendrites below the bridging. In comparison, a lower casting temperature increased the grain density and formed shrinkage porosities that were smaller in size but larger in number and more dispersed.

Optimizing the growth conditions of Al mirrors for superconducting nanowire single-photon detectors

We investigate the growth conditions for thin ( nm) sputtered aluminum films. These coatings are needed for various applications, e.g. for advanced manufacturing processes in the aerospace industry or for nanostructures for quantum devices. Obtaining high-quality films, with low roughness, requires precise optimization of the deposition process. To this end, we tune various sputtering parameters such as the deposition rate, temperature and power, which enables 50 nm thin films with a root mean square roughness of less than 1 nm and high reflectivity. Finally, we confirm the high-quality of the deposited films by realizing superconducting single-photon detectors integrated into multi-layer heterostructures consisting of an aluminum mirror and a silicon dioxide dielectric spacer. We achieve an improvement in detection efficiency at 780 nm from 40% to 70% by this integration approach.

Using machine learning for prediction of spray coated perovskite solar cells efficiency: From experimental to theoretical models

Low-cost perovskite solar cells (PSCs) have experienced unprecedented gains in power conversion efficiency (PCE) of up to 25% of lab-scale devices. To be realized in the market, however, PSCs are not only required to be efficient but also scalable in production. While spray coating has viability as an industrial manufacturing process for perovskite photovoltaics scaling, optimizing the spray conditions is often seen as a challenging and time-consuming process due to its complex and multidimensional parameters. Herein, we use a machine learning (ML) approach to capture the relationship between spray parameter settings to the resultant photoconversion efficiency (PCE) of PSCs from experimental collected data points. This data-driven approach has the potential to accurately predict PCE values given the manufacturing parameters, enabling optimization and resulting in an increased experimentally recorded PCE. Furthermore, we also used a Convolutional Neural Network (CNN) to predict defect size distributions in the PSC structures to improve the understanding of defect formation mechanism at given spray parameters. The implications of the results are discussed for optimizing spray manufacturing process of efficient perovskite photovoltaics.

Semantic information processing for interoperability in the Industrial Internet of Things

With the advent of the Internet of Everything (IoE), the concept of fully interconnected systems has become a reality, and the need for seamless communication and interoperability among different industrial systems has become more pressing than ever before. To address the challenges posed by massive data traffic, we demonstrate the potentials of semantic information processing in industrial manufacturing processes and then propose a brief framework of semantic processing and communication system for industrial network. In particular, the scheme is featured with task-orientation and collaborative processing. To illustrate its applicability, we provide examples of time series and images, as typical industrial data sources, for practical tasks, such as lifecycle estimation and surface defect detection. Simulation results show that semantic information processing achieves a more efficient way of information processing and exchanging, compared to conventional methods, which is crucial for handling the demands of future interconnected industrial networks.

Unsupervised Fault Detection of Pharmaceutical Processes Using Long Short-Term Memory Autoencoders

Unsupervised multilayer long short-term memory autoencoder (LSTM-AE) models are proposed for monitoring nonlinear batch processes. The methodology is demonstrated for a simulation-based study of an industrial-scale penicillin process and for an industrial vaccine manufacturing process, using production data. The LSTM-AE model was trained with two different loss functions: minimizing mean square error (MSE) between the input and reconstructed data and maximizing the average fault detection rate (FDR¯) in the training data set. Two algorithms are also proposed for obtaining contribution plots for the diagnosis of faults. For the industrial case study, where the faults are not known a priori, the contribution plots are found to be a valuable tool for identifying possible sources of faults. Furthermore, a semisupervised procedure has been proposed to select the normal process region for training the model. Two metrics are also presented to evaluate the performance of the proposed methodology: one for the simulator case study in which fault knowledge is available and one for the industrial case study in which fault knowledge is not available a priori. The proposed unsupervised algorithms exhibit a clear improvement in accuracy over linear methods or nonlinear techniques that do not explicitly account for dynamic behavior.

Entropy optimization of non-Newtonian nanofluid natural convection in an inclined U-shaped domain with a hot tree-like baffle inside and considering exothermic reaction

BackgroundNon-Newtonian fluids with properties differing from those of Newtonian fluids have been concerned due to their wide range of applications in industry and engineering. Natural convection may play a crucial role in numerous industrial manufacturing processes. In addition, TiO2-water nanofluid exhibits better performance owing to their high thermal conductivity, low cost, and dielectric properties. Further, baffles can be utilized to ameliorate heat transfer in the system concern. The second law inspection may be substantial so as to examine the entropy optimization's role in diverse thermic systems. AimWith regard to such advantages, the present article dissects natural convection and entropy production of non-Newtonian TiO2-water nanofluid inside an inclined U-shaped domain subject to a hot tree-like baffle at bottom wall and exothermic reaction. MethodThe FEM is instrumental for obtaining apposite solution of the non-dimensional governing equations. NoveltyPower-law (Ostwald-de Waele) nanofluid flow subject to hot tree-like baffle and Arrhenius kinetics in an inclined U-shaped domain is regarded as a novelty of the present investigation. FindingsEnhancing Frank-Kamenetski number amplifies streamlines (6.25%), horizontal velocity (5.086%), and vertical velocity (6.73%). Augmented Rayleigh number emaciates Bejan number by 75% while that of cavity angle from 450to 900 amplify significantly by 494.14%.

Optimization of Levenberg Marquardt Algorithm Applied to Nonlinear Systems

As science and technology advance, industrial manufacturing processes get more complicated. Back Propagation Neural Network (BPNN) convergence is comparatively slower for processing nonlinear systems. The nonlinear system used in this study to evaluate the optimization of BPNN based on the LM algorithm proved the algorithm’s efficacy through a MATLAB simulation analysis. This paper examined the application impact of the enhanced approach using the Continuous stirred tank reactor (CSTR) control system as an example. The study’s findings demonstrate that the LM optimization algorithm’s identification error exceeds 10-5. The research’s suggested control approach for reactant concentration CA in CSTR systems provides a better tracking effect and a stronger anti-interference capacity. Compared to the PI control method, the overall control effect is superior. As a result, the optimization model for nonlinear systems has a greatly improved processing accuracy. With some data support for the accuracy study of neural network models and the application of nonlinear systems, the suggested LM-BP optimization algorithm is evidently more appropriate for nonlinear systems.

Thermal investigation of nanofluid-filled hexagonal fillet cavity under the influence of heated and concentered cylinder

The incorporation of nanoparticles is very advantageous to address the challenges of heat transmission in numerous industrial frameworks. Double-diffusive Natural Convection (DDNC) is a key component of large-scale industrial manufacturing processes, including chemical processing, heat exchangers, and cooling systems. Understanding and optimizing DDNC can enhance process efficiency, reduce energy consumption, and improve product quality. The prime motive of current disquisition is to explicate enhancement in thermal attributes of ordinary water by adding MWCNT's saturated in hexagonal enclosure. A circular heated and concentrated cylinder is placed at the center to generate optimize thermal and concentration gradients. Additionally, magnetic field of uniform strength is applied horizontally to the domain. Problem formulation containing thermophysical relation of inducted nanoparticles are well established and solved numerically by finite element method. Outcomes of involved sundry parameters like, Rayleigh, Hartmann, Lewis numbers are displayed through snapshots. Through observation, it has been noted that the velocity field demonstrates a rising trend in the hydrodynamic case (Ha=0) in contrast to the hydromagnetic case (Ha≠0), as indicated by the stream function's magnitude. More specifically, the maximum value of 〖| |ψ|max reaches 9.8 in the absence of a magnetic field, while it decreases to 3.8 when a magnetic field is present.

Robust Surveillance Schemes Based on Proportional Hazard Model for Monitoring Reliability Data

Product reliability is a crucial component of the industrial production process. Several statistical process control techniques have been successfully employed in industrial manufacturing processes to observe changes in reliability-related quality variables. These methods, however, are only applicable to single-stage processes. In reality, manufacturing processes consist of several stages, and the quality variable of the previous stages influences the quality of the present stage. This interdependence between the stages of a multistage process is an important characteristic that must be taken into account in process monitoring. In addition, sometimes datasets contain outliers and consequently, the analysis produces biased results. This study discusses the issue of monitoring reliability data with outliers. To this end, a proportional hazard model has been assumed to model the relationship between the significant quality variables of a two-stage dependent manufacturing process. Robust regression technique known as the M-estimation has been implemented to lessen the effect of outliers present in the dataset corresponding to reliability-related quality characteristics in the second stage of the process assuming Nadarajah and Haghighi distribution. The three monitoring approaches, namely, one lower-sided cumulative sum and two one-sided exponentially weighted moving average control charts have been designed to effectively monitor the two-stage dependent process. Using Monte Carlo simulations, the efficiency of the suggested monitoring schemes has been examined. Finally, two real-world examples of the proposed control approaches are provided in the study.

An improved grand-potential phase-field model of solid-state sintering for many particles

Understanding the microstuctural evolution during the sintering process is of high relevance as it is a key part in many industrial manufacturing processes. Simulations are one avenue to achieve this understanding, especially field-resolved methods such as the phase-field (PF) method. Recent papers have shown several weaknesses in the most common PF model of sintering, which the present paper aims to ameliorate. The observed weaknesses are shortly recounted, followed by presenting model variations aiming to remove these deficiencies. The models are tested in the classical two-particle geometry, with the most promising model being run on large-scale three-dimensional packings to determine representative volume elements. A densification that is strongly dependent on the packing size is observed, which suggests that the model requires further improvement.

Amplification of higher-order Laguerre-Gaussian modes using a dual-pass MOPA system.

Structured light beams that are tailored for purpose have found a myriad of applications, from improved efficiency of laser-based industrial manufacturing processes to enhanced bandwidth in optical communication. While the selection of such modes is readily achievable at low powers (<100 mW) with external shaping devices, creating and controlling structured light at higher powers (>1 W) has proven to be a non-trivial task, particularly if dynamic control is required. Here we demonstrate the power amplification of low-power higher-order Laguerre-Gaussian modes using a novel in-line dual-pass master oscillator power amplifier (MOPA). The amplifier, operating at a wavelength of 1064 nm, consists of a polarization-based interferometer that alleviates parasitic lasing effects. Through our approach we demonstrate a gain factor of up to 17×, corresponding to an overall enhancement of 300% in amplification compared to a single-pass output configuration while preserving the beam quality of the input mode. These findings are confirmed computationally using a three-dimensional split-step model and show excellent agreement with the experimental data.

OPTIMIZATION OF LASER CUTTING PROCESS PARAMETERS ON SS347 USING GRA AND TOPSIS

Laser cutting is a one of the efficient manufacturing processes in industry to cut the hard materials by vaporizing. Stainless steel (SS347) is the most popular material for many applications due its unique characteristics such as efficiency to retain good strength with no inter-granular corrosion even at elevated temperatures. However, the cutting or machining of this material is very difficult. On the other side, the machining cost of laser process is high when compared with other processes. In this work, GRA and TOPSIS techniques are used to study the laser cutting process parameters of SS347. The obtained results were compared with the data mining approach. The input parameters are power, speed, pressure and stand-off distance (SOD) and the output responses of surface roughness, machining time and HAZ are considered. The set of experiments were constructed by using the Taguchi’s L9 method. The predicted closeness value of TOPSIS is greater than the GRA technique and the predominant factor observed is SOD followed by pressure, speed and power. In this work, C4.5-decision tree algorithm is applied to find the most influential parameter. It also represents the low-level knowledge of data set into high level knowledge (If-Then rules form). This investigation reveals that both TOPSIS and data mining suggested the SOD as predominant factor. This result of the optimized process parameters supports the laser assisted manufacturing industries by providing optimized output. Better results were obtained using the optimized set of parameters with the machining time, HAZ and surface roughness being 7.83 s, 0.09 mm and 0.86 [Formula: see text]m, respectively. The results of this work would be very useful for automobiles and aircrafts industries where SS347 is highly employed.

Investigating the potential for porous ceramics as bacterial carrier in self-healing cemented paste backfill

Waste porous ceramic (WPC), which remains in the industrial manufacturing process, is difficult to be disposed of. This study proposes a new idea for resource utilization of WPC, which means that WPC as a bacterial carrier applies in self-healing cemented paste backfill. The authors investigated the effects of WPC pore size, initial crack size, and bacterial concentration on the crack self-healing ratio of the backfill, analyzed the crack self-healing evolution process by photographing, and finally used a series of instruments to characterize the physical phase at the sample fracture and explain the mechanism. The results showed that the crack self-healing ratio of WPC with small pore size (pore size distribution of 1–2 mm) as the bacterial carrier was 13–22% higher than that of WPC with large pore size (pore size distribution of 3–4 mm) at 3–7 days. Also, the strength recovery ratio of the cemented paste backfill can be improved to 10–23% when adding bacteria-WPC. The self-healing ratio can reach 100% in 28 days for initial crack widths less than 1 mm, while the self-healing ratio decreases to 98% for widths near 2 mm. Additionally, the biomineralization ratio of the 1.5 mol/L embedded bacterial solution was 2.77–4.68% higher than that of 1.0 mol/L. WPC can effectively release the embedded bacterial solution once the backfill cracks appear, which proves the feasibility of WPC as a bacterial carrier. This study also lays the experimental and theoretical foundation for porous ceramic management.

Nonlinear Error Correction for Magnetic Encoders

Magnetic encoders are used as angle sensors to control motors for precise positioning in many industrial manufacturing processes. However, nonideal factors such as installation misalignment lead to angle measurement errors. This study analyzed a nonlinear model of the measurement error due to eccentricity of a magnetic encoder and developed a method of compensating for this error. An experimental calibration system consisting of a two-phase stepping motor was used to provide a standard reference angle for calculating the measurement error of the encoder. The fast Fourier transform algorithm was used to demonstrate that the measurement error can be modeled as a multifrequency composite cosine wave with low-frequency components. Compensation of the measurement error was performed using the calculated experimental parameters of each frequency component in the nonlinear error model. The proposed method was experimentally verified, and the accuracy of the 14-bit AS5047D magnetic encoder was improved from 8~9 bits to 11~12 bits.

Automatic detection surface defects based on convolutional neural networks and deflectometry

AbstractSurface defects in industrial refrigerator manufacturing processes can cause significant production losses and compromise product quality. This area is underexplored and currently, visual quality inspection is a subjective process that requires expert intervention, which limits process efficiency and can lead to errors in defect detection. This paper presents a novel approach for automatic surface defect detection using a combination of convolutional neural networks (CNN) and deflectometry. The proposed method takes advantage of the high accuracy and robustness of CNNs in image classification tasks and the sensitivity of deflectometry to detect subtle surface variations. First, a prototype was built to get the images from the refrigerator. Second, using video recordings, we captured surface topographic data using deflectometry, which we then use to generate surface images. Next, we train a CNN to classify the surface images as defective or normal. The proposed method offers a promising solution for automatic detection and quality control of surface defects in refrigerator manufacturing processes. However, this method could also improve the production of vehicles, household appliances in general, and any product that can suffer scratches and dents.

Experimental Studies on the Removal of Chromium from Tannery Wastewater using Chemical Precipitation and Adsorption Techniques

Despite trivalent chromium’s manifold functionalities in industrial manufacturing processes, when it is improperly treated and disposed to the environment, due to external environmental factors it could get converted to hexavalent chromium that has toxic effects on humans and our environment. Hence, there is a stringent need to optimize the current industrial practices of chromium handling, management, and disposal. This paper puts forth the inferences from comparative studies conducted using chemical precipitation and biochar adsorption to ascertain if the current chromium removal practice in the chrome tanning leather industry under study is an optimal method or not. Results indicate that the current industrial practice of achieving 86~90 % removal efficiency using 2000 mg/L of MgO at pH6 can be optimized by carrying out chemical precipitation at pH8 for 96~99% removal efficiency; a combination of NaOH and Ca(OH)2 in varying ratios resulted in 80-96 % removal efficiency at pH7 and ~99% at all the ratios at pH8, but the sludge volumes were higher than twice the volumes generated by MgO precipitation; inactivated biochar in similar proportions to that of chemical precipitants did not yield encouraging results and the maximum removal efficiency was 73.2% at 48000 mg/L dosage. Therefore, if not for complete treatment of tannery wastewater with biochar, residual chromium in the supernatant should be treated with biochar as it could be effective in treating lower concentrations of chromium.

Selective production of hydrogen isotope gases via PdAg-catalyzed dehydrogenation of formic acid in D2O assisted by surface-grafted amine groups

Hydrogen isotope gases are widely used as deuterium sources in industrial manufacturing processes. It would therefore be beneficial to develop a facile, simple means of selective producing molecular deuterium (D2) and hydrogen deuteride (HD), although reasonable heterogeneous catalytic methodologies are still missing. We previously demonstrated that the hydrogen isotope gases was selectively obtained from formic acid (HCOOH, FA) and D2O using PdAg nanoparticles supported on mesoporous silica (SBA-15) on which amine functional groups had been grafted. In the present work, amine-functionalized carbon was used as an efficient support material to provide a catalyst with improved activity. Employing weakly basic phenylamine groups, D2 was predominantly evolved and the reaction rate was doubled compared with that for our previously reported system. A high degree of correlation between D2 selectivity and the basicity of the grafted amine groups was also observed. The applicability of the present catalytic system was highlighted by in situ deuteration of diphenylacetylene in a solution of FA in D2O, during which deuterated products were obtained with high efficiency.

Adaptive convolution confidence sieve learning for surface defect detection under process uncertainty

Surface defect detection plays an important role in the quality management of industrial manufacturing processes. Existing detection methods are developed based on clean and uncontaminated training data, less considering the process uncertainty. This paper proposes an adaptive convolution confidence sieve learning method for surface defect detection under process uncertainty. First, an edge-separated wavelet transform method combines the spatial and transform domain methods to estimate image noise. After that, an adaptive convolution model integrates parallel multiple convolution kernels, which can cover the defect feature variation. Then, a confidence sieve learning scheme is developed to filter the label noise, and the adaptive convolution is fine-tuned on the high-confidence data to achieve better detection accuracy. Finally, experimental studies on three industrial datasets have verified that the edge-separated wavelet transform method can accurately predict image noise. The adaptive convolution has better representation capability, and the confidence sieve learning improves model robustness.

Prototype of Resistance Spot Welding Material Preparation to Improve the Quality of Welding Joints

The most improtant manufacturing process in several industries was resistance spot welding. Because it was easier to operate, it was very effective and efficient in operation, but sometimes there were still connection problems that were still of poor quality, both nuggets and areas that experience electrode pressure. The quality of the resistance welded joint was influenced by several factors, namely: preparation of materials, welding time, welding current, electrode pressure applied at the time of welding and treatment after welding. The purpose of this study is to design a material preparation prototype and analyze the test results of the resistance spot welding material prototype. The research method was carried out by designing a prototype of the preparation of resistance spot welding material and analyzing the strength of the best welded joints for the most qualified joints. Data collection was carried out by varying the roughness of the transverse surface of the joint to be tested by selecting the right welding parameters, tensile strength testing, and surface roughness. The results of the design carried out were made used iron plate as a frame with a thickness of 5 mm and using a thread bolt 670 mm long as a rail drive line and the movement of the rail was driven by a power window motor and can be controlled automatically with the help of arduino, Servo motor, and Bluetooth module. The best welding strength was obtained at the roughness of the C600=0.20µm sandpaper where in the sandpaper experiment it was the smoothest.

Time-aware deterministic bandwidth allocation scheme in TDM-PON for time-sensitive industrial flows

A programmable logic controller deployed in the cloud or edge cloud (cloud PLC) contributes to flexible and scalable industrial manufacturing processes. For the industrial Internet, a time-division multiplexing passive optical network is a promising access technology for connecting cloud PLC and industrial devices. Time-sensitive (TS) industrial applications such as motion control and mobile robotics impose strict delay and jitter requirements, pursuing deterministic transmission capability, that is, guaranteeing that the upper bounds on delay and jitter of the TS industrial flow meet its requirements. However, traditional bandwidth allocation schemes aim to leverage statistical multiplexing to improve bandwidth resource utilization efficiency, so it guarantees only the “average delay/jitter” of the service and cannot meet the deterministic requirements of the TS industrial flow. This paper presents a time-aware deterministic bandwidth allocation (TA-DetBA) scheme to satisfy the deterministic transmission of TS industrial flows. To this purpose, first, we constrain the position of the transmission window (i.e., bandwidth) allocated by optical line termination to the optical network unit according to the arrival characteristics (i.e., flow arrival time and flow cycle) and requirements (i.e., delay and jitter) of the flow. Subsequently, we propose an integer linear programming scheduling model and a greedy-based time slot equalization heuristic scheduling algorithm to improve schedulability and reduce the average delay of best-effort flows. Simulation results show that our proposed TA-DetBA scheme guarantees the deterministic delay and jitter of TS industrial flow. Compared with the fixed bandwidth allocation (FBA) scheme, the average resource utilization efficiency of the TA-DetBA scheme is improved by up to 20.6% higher than FBA.