Industrial Manufacturing Processes Research Articles

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.

Industrial manufacturing process based on smart grid data classification with security using deep learning technique

Industrial manufacturing process based on smart grid data classification with security using deep learning technique

Research on surface defects detection method and system in manufacturing processes based on the fusion of multi-scale features and semantic segmentation for intelligent manufacturing

Multi-scale defect features, blurred edges and inability to locate geometric features have been the three key factors limiting the detection of surface defects on quality control system in the industrial manufacturing process. In this study, a method based on the fusion of multi-scale features and pixel-level semantic segmentation is proposed for the detection of surface defects. The proposed method firstly fuses multi-level feature maps to balance the expressiveness of multi-scale features, then adds a boundary refinement module to enhance the accurate inference of edge fine-grained, and finally adopts an en-decoder architecture to locate geometric features at the pixel-level for each type of defects, realizing intelligent detection of geometric features of end-to-end multi-scale defects on the surface of parts. We conduct experiments using the collected parts datasets to evaluate the effectiveness of our framework. The experimental results show that the proposed model achieves MIoU of 80.1%, the recognition accuracy reaches more than 95 %, and a detection rate of up to 29.64 FPS, demonstrating the advancement and effectiveness of the proposed method with less misclassification and superior generalization performance and has progress and effectiveness in detecting surface defects of multi-scale features. It provides a research idea for the subsequent realization of surface quality inspection in the manufacturing process system.

Arm PSA-Certified IoT Chip Security: A Case Study

With the large scale adoption of Internet of Things (IoT) applications in people's lives and industrial manufacturing processes, IoT security has become an important problem today. IoT security significantly relies on the security of the underlying hardware chip, which often contains critical information, such as encryption key. To understand existing IoT chip security, this study analyzes the security of an IoT security chip that has obtained an Arm Platform Security Architecture (PSA) Level 2 certification. Our analysis shows that the chip leaks part of the encryption key and presents a considerable security risk. Specifically, we use commodity equipment to collect electromagnetic traces of the chip. Using a statistical T-test, we find that the target chip has physical leakage during the AES encryption process. We further use correlation analysis to locate the detailed encryption interval in the collected electromagnetic trace for the Advanced Encryption Standard (AES) encryption operation. On the basis of the intermediate value correlation analysis, we recover half of the 16-byte AES encryption key. We repeat the process for three different tests; in all the tests, we obtain the same result, and we recover around 8 bytes of the 16-byte AES encryption key. Therefore, experimental results indicate that despite the Arm PSA Level 2 certification, the target security chip still suffers from physical leakage. Upper layer application developers should impose strong security mechanisms in addition to those of the chip itself to ensure IoT application security.

A Guiding Framework for Process Parameter Optimisation of Thermal Spraying

Currently, most thermal spray coating service providers expect original equipment manufacturers (OEMs) to suggest the coating recipe, comprising of the right coating equipment, starting process parameters, type of inert gases, and robot program. The microstructure and mechanical properties may not comply in the first few runs. Feedback from a competent material testing service provider forms the initial step to adjust the parameters in the development journey, toward identifying the processing parameters required to obtain an acceptable coating. With the surge of interest in sustainable manufacturing, the time spent on trials in the future will shrink, and a more rigorous process needs to be applied to achieve the “right-first-time manufacturing” approach in thermal spraying. However, a framework for the systematic development of thermal spray parameter optimisation is lacking. This article provides a framework, based on a logical acumen, in selecting the right process parameters, using available data and prior knowledge about the thermal spraying process. To that end, the article summarises the extant developmental journey of thermal spray process parameters, by covering the aspects of equipment choice, robot and spray parameters, and how to minimise iterations, using diagnostic tools to get to the end solution most efficiently. This article, in its current form, will serve as a good guide for early career engineers and scientists or workers, to minimise the time processing window, by avoiding endless iterations to deposit a certain type of coating using thermal spraying. Besides, this work is also aimed at transforming academic research innovations to a robust and repeatable industrial manufacturing process.

Comparative analysis of the explosion characteristics of magnesium dust and magnesium-aluminium alloy dust with two concentrations

ABSTRACT Magnesium (Mg) dust and magnesium–aluminum (Mg–Al) alloy dust are commonly produced and form a dust cloud in industrial manufacturing processes, which considerably increases explosion risk. This study examined three metal dusts: Mg, 48 mass% Mg–Al alloy, and 91 mass% Mg–Al alloy. A 20-L spherical explosion device was used to investigate the effect of different Mg concentrations. The results revealed that for all the samples, the explosion pressure (P ex) first increased and then decreased for a dust cloud concentration of 100–1000 g/m3. With the increase of dust particle size, the P ex of samples decreased. With the increase of ignition delay time, the P ex of samples first increased and then decreased, reaching the peak at 60 ms. Under identical conditions, when Mg concentration increased, the alloys became less stable. Thermogravimetry-differential scanning calorimetry findings revealed that the oxidation process of samples could be divided into three stages. The initial oxidation temperature of the alloys was lower than that of the pure Mg; however, in its primary oxidation stage, the mass gain rate of the Mg dust was more rapid than that of the Mg–Al alloys. In the oxidation of the alloys, elemental Mg reacted with oxygen first.

Process reconfiguration and intensification: An emerging opportunity enabling efficient carbon capture and low-cost blue hydrogen production

Low-carbon hydrogen can play a significant role in decarbonizing the world. Hydrogen is currently mainly produced from fossil sources, requiring additional CO2 capture to decarbonize, which energy intense and costly. In a recent Green Energy & Environment paper, Cheng and Di et al. proposed a novel integration process referred to as SECLRHC to generate high-purity H2 by in-situ separation of H2 and CO without using any additional separation unit. Theoretically, the proposed process can essentially achieve the separation of C and H in gaseous fuel via a reconfigured reaction process, and thus attaining high-purity hydrogen of ∼99%, as well as good carbon and hydrogen utilization rates and economic feasibility. It displays an optimistic prospect that industrial decarbonization is not necessarily expensive, as long as a suitable CCS measure can be integrated into the industrial manufacturing process.

Determination of manufacturing process failures priority under type 2 fuzzy environment: Application of genetic algorithm and Variable neighborhood search

The aim of this research is to propose the two-stage model to select a set of failures that need to be eliminated or reduced which leads to improved reliability and effectiveness of the manufacturing process in the automotive industry. In the first stage, uncertainties under the relative importance of risk factors and costs of manufacturing process downtime due to failure are modeled by type 2 fuzzy sets. The weights vector of risk factors is obtained by analytical hierarchical Process which is extended with type 2 fuzzy sets. Evaluation of risk factors at the level of each identified failure is based on failure mode and effect analysis which is widely used in practice. Determining the set of failures to be eliminated is set as a knapsack problem. The linear fitness function is defined as the ratio of the overall risk priority index and total costs. Maintenance costs incurred due to the realization of failure are limited by the available budget and in this knapsack problem are presented by a linear inequality. The solution to this problem is found by using the Genetic Algorithm and Variable Neighborhood Search. The model is verified with real-life data originating from automotive companies that exist in Serbia. Authors have managed to obtain suitable results on different knapsack problem instances. It is shown that the enhancement of the manufacturing process can be based on the proposed model.

Chemical transformation of cannabidiol into psychotropic cannabinoids under acidic reaction conditions: Identification of transformed products by GC-MS.

Recently, cannabidiol (CBD), one of the major components of the Cannabis species, has been a focus in the cannabis industry due to its various pharmacological effects. Interestingly, CBD can be converted into several psychoactive cannabinoids, such as 9-tetrahydrocannabinol (Δ9-THC) and its structural isomers, under acidic reaction conditions. In this study, chemical transformation of CBD in ethanol solution was conducted with variation in pH at 2.0, 3.5, and 5.0 by addition of 0.1 M hydrochloric acid (HCl). These resulting solutions were derivatized with trimethylsilyl (TMS) reagent and analyzed using GC/MS-scan mode. Time profiles of CBD degradation and transformation of products were examined according to variations in pH and temperature. Several transformed products produced after the acidic reaction of CBD were identified by matching retention times and mass spectra to authentic standards. Regarding the identification of products without authentic standards, the EI-mass spectra of such cannabinoid-OTMS derivatives were interpreted according to structural class, suggesting mass fragmentation pathways. From the GC/MS data, Δ9-THC, CBC, and ethoxy-hexahydrocannabinol (HHC) analogs were shown to be major components, and THC isomers (Δ8- and Δ10-THCs) and 9-hydroxy-HHC were observed as minor components. Using time profile data, the acidity of the reaction solution was an important factor in degradation of CBD. Degradation of CBD and formation of THC rarely occurred at pH 5.0, even at 70 °C with a long process time of 24 h. In contrast, degradation of CBD occurred readily at pH 3.5 and 30 °C over a short process time and was further accelerated by lowering pH, increasing temperature, and lengthening the process time. Based on profile data and identified transformed products, formation pathways from the degradation of CBD under acidic reaction conditions are suggested. Among the transformed products, seven components are known to have psychoactive effects. Thus, industrial CBD manufacturing processes in food and cosmetic products should be carefully controlled. These results will provide important guidelines on the control of manufacturing processes, storage, fermentation processes, and new regulation in industrial applications of CBD.

FDD: a deep learning-based steel defect detectors.

Surface defects are a common issue that affects product quality in the industrial manufacturing process. Many companies put a lot of effort into developing automated inspection systems to handle this issue. In this work, we propose a novel deep learning-based surface defect inspection system called the forceful steel defect detector (FDD), especially for steel surface defect detection. Our model adopts the state-of-the-art cascade R-CNN as the baseline architecture and improves it with the deformable convolution and the deformable RoI pooling to adapt to the geometric shape of defects. Besides, our model adopts the guided anchoring region proposal to generate bounding boxes with higher accuracies. Moreover, to enrich the point of view of input images, we propose the random scaling and the ultimate scaling techniques in the training and inference process, respectively. The experimental studies on the Severstal steel dataset, NEU steel dataset, and DAGM dataset demonstrate that our proposed model effectively improved the detection accuracy in terms of the average recall (AR) and the mean average precision (mAP) compared to state-of-the-art defect detection methods. We expect our innovation to accelerate the automation of industrial manufacturing process by increasing the productivity and by sustaining high product qualities.