Industrial Manufacturing Processes Research Articles

A novel pbd gene cluster responsible for pyrrole and pyridine ring cleavage in Rhodococcus ruber A5

Pyridine and pyrrole, which are regarded as recalcitrant chemicals, are released into the environment as a result of industrial manufacturing processes, posing serious hazards to both the environment and human health. However, the pyrrole degradation mechanism and the pyridine-degrading gene in Rhodococcus are unknown. Herein, a highly efficient pyridine and pyrrole degradation strain Rhodococcus ruber A5 was isolated. Strain A5 completely degraded 1000 mg/L pyridine in a mineral salt medium within 24 h. The pyridine degradation of strain A5 was optimized using the BoxBehnken design. The optimum degradation conditions were found to be pH 7.15, temperature 28.06 ℃, and inoculation amount 1290.94 mg/L. The pbd gene clusters involved in pyridine degradation were discovered via proteomic analysis. The initial ring cleavage of pyridine and pyrrole in strain A5 was carried out by the two-component flavin-dependent monooxygenase PbdA/PbdE. The degradation pathways of pyridine and pyrrole were proposed by the identification of metabolites and comparisons of homologous genes. Additionally, homologous pbd gene clusters were found to exist in different bacterial genomes. Our study revealed the ring cleavage mechanisms of pyrrole and pyridine, and strain A5 was identified as a promising resource for pyridine bioremediation.

Study of Different Biomaterials for Reducing Textile Industry Waste Water Pollution

Textile industry is distributed throughout the country. The manufacturing process of textile industry involve the use of various chemicals and after these processing textile effluent is mix in natural water bodies like river, lakes etc, which shows the impact on water quality changes i.e. cause of water pollution. In this research focus on to change the effluent quality using different biomaterials like saw dust, parthenium, Typha, Maize cob. In textile effluent high content of TSS, COD, Chloride and heavy metals are present. By using these biomaterials the contents of TSS, COD, and Chloride decreases. After treatment of biomaterials change the quality of textile effluent than the before treatment and it becomes pollutant free effluent which does not harm the environment.

Impact of technical efficiency and input-driven growth in the Indian food processing sector

PurposeThis study examines the performance of India's food processing sector by estimating its output growth, technical efficiency (TE) and input-driven growth (IDG)Design/methodology/approachThis study used panel data from six food processing manufacturing industries for the period 2000–01 to 2017–18. Technical efficiency and input-driven growth was measured using the parametric half-normal stochastic frontier production function.FindingsThe findings of this study showed that the estimated average technical efficiency is 86.6%, which specifies that the Indian food processing sector is technically inefficient. In addition, the output growth rate is 5.5%, driven by high doses of inputs (5.7%), whereas there is no indication of constant returns to scale. However, the food processing sector has experienced more input-driven expansion than either technological or efficiency changes.Research limitations/implicationsThis study is limited to India's organized manufacturing food processing sector; the aggregate macro data at a three-digit level based on the national industrial classification (NIC) was used. This study provides robust estimates for industrialists and processors, as well as concrete policy formulations on how overdoses of inputs may lead to high exploitation of resources, whereas outputs can be augmented by implementing upgraded and new technologies.Originality/valuePrevious research has estimated the total factor productivity and technical efficiency only in order to analyze the food sector's performance, but none of the studies have evaluated the share of inputs in growth performance and efficiency. Therefore, this study contributes by measuring growth performance and the share of inputs in the growth performance of India's food processing sector.

Prediction of Optimum Veneer Drying Parameters with Artifi cial Neural Networks for Production of Plywood with High Mechanical Properties

Veneer drying is the manufacturing process in the plywood industry that most affects energy consumption and panel properties such as bonding and bending. Therefore, the veneer drying temperature and moisture content should be accurately adjusted. Moreover, the determination of veneer thermal conductivity is as important as these two parameters and the thermal conductivity values should also be specifi ed when forming the drying programs. This study aimed to predict the optimum values of the veneer drying temperatures, moisture content and thermal conductivity, which gave the best mechanical properties, by artifi cial neural network (ANN) analysis. Poplar (Populus deltoidesI-77/51) and spruce (Picea orientalis L.) veneers and urea formaldehyde (UF) resin were used in the production of plywood. The thermal conductivity of veneer and the bonding, bending strength and elasticity modulus of the panels were tested by the relevant standards. The most accurate and reliable prediction models were obtained by analyzing the experimental data with ANN. The optimum veneer drying temperature, moisture content and thermal conductivity values that gave the best values for all three mechanical properties were 149 °C, 6.2 % and 0.02668 W/mK for poplar and 116 °C, 4.4 % and 0.02534 W/mK for spruce.

Modeling and Solution of an Unsteady Flow of a Third Grade Fluid Over an Infinite Parallel Rigid Plate Within a Porous Medium

It is known by many researchers that non-Newtonian fluids are regarded as important and appropriate in technological techniques and several industrial manufacturing processes such as in polymer sheet extrusion from dye, drilling of oil and gas well to mention a few. Due to such reason a model of such type of fluid called the differential type model in which the third grade fluid is categorized. The skin friction, Nusselt number, and Sherwood number through the graphs obtained, were also obtained. The nonlinear partial differential equations were solved using He – Laplace method. He – laplace method is a combination of Homotopy perturbation method with the Laplace transform method which is used in determining linear together with the nonlinear partial differential equations, and the use of He’s polynomials for the nonlinear term. Amongst many results obtained; the velocity, temperature and concentration profiles diminish due to the increase in suction parameter. Higher values of magnetic parameter decreases the fluid velocity. The temperature distribution is enhanced by the increment in radiation parameter. Upsurging values of chemical reaction decline the concentration of the fluid.

Special issue and perspective on the chemistry and physics of carbonaceous particle formation

Carbonaceous particles formed during the partial oxidation of a fuel constitute a primary pollutant (i.e., soot) impacting human health and the environment. The competing soot formation and oxidation pathways associated with fossil fuels and carbon-containing renewable fuels must be understood to support the development of energy-conversion devices that limit health and environmental impacts. Such knowledge is also helpful in optimizing the properties of carbon black and other carbonaceous materials used in major industrial manufacturing processes, such as graphene, nanotubes, and (functional) carbon-coated nanoparticles. The current Special Issue comprises 16 articles that highlight progress relevant to carbonaceous particles.

Enhancement of Product-Inspection Accuracy Using Convolutional Neural Network and Laplacian Filter to Automate Industrial Manufacturing Processes

The automation of the manufacturing process of printed circuit boards (PCBs) requires accurate PCB inspections, which in turn require clear images that accurately represent the product PCBs. However, if low-quality images are captured during the involved image-capturing process, accurate PCB inspections cannot be guaranteed. Therefore, this study proposes a method to effectively detect defective images for PCB inspection. This method involves using a convolutional neural network (CNN) and a Laplacian filter to achieve a higher accuracy of the classification of the obtained images as normal and defective images than that obtained using existing methods, with the results showing an improvement of 11.87%. Notably, the classification accuracy obtained using both a CNN and Laplacian filter is higher than that obtained using only CNNs. Furthermore, applying the proposed method to images of computer components other than PCBs results in a 5.2% increase in classification accuracy compared with only using CNNs.

Modeling Battery Formation: Boosted SEI Growth, Multi-Species Reactions, and Irreversible Expansion

This work proposes a semi-empirical model for the SEI growth process during the early stages of lithium-ion battery formation cycling and aging. By combining a full-cell model which tracks half-cell equilibrium potentials, a zero-dimensional model of SEI growth kinetics, and a semi-empirical description of cell thickness expansion, the resulting model replicated experimental trends measured on a 2.5 Ah pouch cell, including the calculated first-cycle efficiency, measured cell thickness changes, and electrolyte reduction peaks during the first charge dQ/dV signal. This work also introduces an SEI growth boosting formalism that enables a unified description of SEI growth during both cycling and aging. This feature can enable future applications for modeling path-dependent aging over a cell’s life. The model further provides a homogenized representation of multiple SEI reactions enabling the study of both solvent and additive consumption during formation. This work bridges the gap between electrochemical descriptions of SEI growth and applications toward improving industrial battery manufacturing process control where battery formation is an essential but time-consuming final step. We envision that the formation model can be used to predict the impact of formation protocols and electrolyte systems on SEI passivation and resulting battery lifetime.

Study of the effects of laser cutting processes on S235 structural steel

AbstractLaser cutting technology shows advantages in advanced industrial manufacturing processes due to high machining speed and precision to cut complex geometries. Nonetheless, its thermal process is characterized by complex physical mechanisms that alter material properties and the structural response of the cut element subjected to high fatigue loadings. The optimization of laser cutting parameters by cut attempts and the evaluation of the mechanical response by testing is very expensive. A 3D numerical model based on the FE method was developed to investigate the thermal effects due to the laser cutting process on mild steel S235. Due to the complexity of the process, monitored cutting processes and material tests were carried out to calibrate and validate the FE model. More precisely, temperature profiles were observed by thermal camera and thermocouples on specimens cut using different laser cutting parameters. Besides, microstructural observations on the heat affected zone were performed using microscopy and XRD methods. The proposed model was capable to simulate the laser cutting process and its results were in good agreement with the corresponding experiments in terms of temperature distribution, temperature history, and micro‐structures inside the cut specimen.

Hierarchical ensemble deep learning for data-driven lead time prediction

This paper focuses on data-driven prediction of lead times for product orders based on the real-time production state captured at the arrival instants of orders in make-to-order production environments. In particular, we consider a sophisticated manufacturing system where a large number of measurements about the production state are available (e.g. sensor data). In response to this complex prediction challenge, we present a novel ensemble hierarchical deep learning algorithm comprised of three deep neural networks. One of these networks acts as a generalist, while the other two function as specialists for different products. Hierarchical ensemble methods have previously been successfully utilised in addressing various multi-class classification problems. In this paper, we extend this approach to encompass the regression task of lead time prediction. We demonstrate the suitability of our algorithm in two separate case studies. The first case study uses one of the largest manufacturing datasets available, the Bosch production line dataset. The second case study uses synthetic datasets generated from a reliability-based model of a multi-product, make-to-order production system, inspired by the Bosch production line. In both case studies, we demonstrate that our algorithm provides high-accuracy predictions and significantly outperforms selected benchmarks including the single deep neural network. Moreover, we find that prediction accuracy is significantly higher in the synthetic dataset, which suggests that there is complexity (i.e. subtle interactions) in industrial manufacturing processes that are not easily reproduced in artificial models

Flow stress and dynamic recrystallization behavior and modeling of GH4738 superalloy during hot compression

GH4738 superalloy is a key material for gas turbine engines, yet its relatively poor thermoplasticity and narrow temperature range for hot working deteriorate grain structure uniformity and thereafter service performance. Employing hot compression experiments and finite element analysis (FEA), the present study examined the thermoplasticity of the GH4738 alloy and proposed a fast approach to establishing its dynamic recrystallization (DRX) model. The hot working conditions were considered in the 1000–1160 °C temperature range under strain rates of 2–26 s−1 up to a true compressive strain of 0.69. Friction- and temperature-corrected stress-strain curves were obtained, based on which the efficiency of power dissipation and constitutive equations were constructed. It reveals that DRX is the dominant softening mechanism in the GH4738 alloy, where the sensitivity of flow softening positively correlates with lower temperatures and higher strain rates. The appropriate hot working condition for the alloy is temperatures around 1040–1080 °C and a strain rate larger than 8 s−1. The final grain sizes (d) and fractions of DRX (Xdyn) were minimized using the stochastic gradient descent algorithm based on 120 sets of data within the framework of the Avrami equation. It is shown that the formulated model and its integration into the FEA not only yield satisfactory agreement with the experiment but effectively avoid the complexity involved in the traditional method. The study provides key inputs for predicting the flow behavior and grain structure of the GH4738 superalloy, which shall be critical for process development and optimization of industrial manufacturing processes.

Parameterized Modeling of the Energy Demand of Machining Processes as a Basis for Reusable Life Cycle Inventory Datasets

Manufacturing processes have a significant contribution to energy consumption and related greenhouse gas (GHG) emissions in a product’s life cycle. Today, information on GHG emissions is increasingly demanded from companies in a life cycle perspective, based on the methodology of Life Cycle Assessment. Manufacturing companies supply producers of final products and are, therefore, requested to provide data on GHG of their manufacturing processes and resulting products. Obtaining such data for real-world manufacturing processes represents a huge effort. This challenge can be overcome with the use of a parameterized model, the Extended Energy Modeling Approach (EEMA), that has been developed for the machining process, which is a widespread industrial manufacturing process. The model calculates the total energy demand from power key values, which report the average power consumption of the constant and variable units of the machinery equipment, the consumer groups, as well as the different operating states of the equipment. Therefore, EEMA enables the reuse of a single measurement campaign for follow-up investigations of the specific machine tool, thereby significantly improving the efficiency of data acquisition for the calculation of the total energy demand and life-cycle-based GHG emissions. To use EEMA for the compilation of life cycle inventory datasets, methodological requirements were analyzed to derive a procedure for LCA-compliant datasets for machine tools. The key findings of applying the EEMA for the case study of a turning machine show that the constant consumer groups have a significant influence on the total energy demand. The share of the variable consumer groups in the total energy demand increases with increasing machine utilization but is always below 5%.

RANCANG BANGUN PROTOTIPE PREPARASI MATERIAL RESISTANCE SPOT WELDING

The manufacturing process in the automotive industry is in dire need of resistance spot welding machines. Because it was easier to use, highly effective, and effective in functioning. However, there were sometimes still connection problems that were still of poor quality, both nuggets and areas that are subject to electrode pressure. To get a quality welding connection, a machine was needed that was able to carry out good material preparation in the form of sanding, able to set the welding current analogously, able to set the time with an automatic timer, able to apply pressure on the electrode measurably during the welding process. The research aimed to design a prototype of resistance spot welding material preparation that could adequately carry out material preparation and welding using a Bluetooth system connected to a mobile phone. The research methods carried out were as follows: first, designing the machine using Autodesk Inventor software, then the device was made with a focus on material preparation prototypes, then the machine elements were made for the material clamping rail, then the machine elements in the control section using Bluetooth connected to the mobile phone, the machine was tested, then the connection quality testing process was carried out on the tensile testing machine. The results showed that the design results had been made using elbows and iron plates with threaded rails with a length of 650 mm, which became material clamping rail grooves with a drive using window motors controlled by Bluetooth on the Arduino circuit. The test results of welded joints show that the roughness is 0.20 μm which has the highest tensile strength.

The Industrial Digital Energy Twin as a Tool for the Comprehensive Optimization of Industrial Processes

Industrial manufacturing processes have evolved and improved since the disruption of the Industry 4.0 paradigm, while energy has progressively become a strategic resource required to maintain industrial competitiveness while maximizing quality and minimizing environmental impacts. In this context of global changes leading to social and economic impact in the short term and an unprecedented climate crisis, Digital Twins for Energy Efficiency in manufacturing processes provide companies with a tool to address this complex situation. Nevertheless, already existing Digital Twins applied for energy efficiency in a manufacturing process lack a flexible structure that easily replicates the real behavior of consuming machines while integrating it in complex upper-level environments. This paper presents a combined multi-paradigm approach to industrial process modeling developed and applied during the GENERTWIN project. The tool allows users to predict energy consumption and costs and, at the same time, evaluates the behavior of the process under certain productive changes to maximize consumption optimization, production efficiency and process flexibility.

Deep learning for anomaly detection in wire-arc additive manufacturing

Wire-arc additive manufacturing (WAAM) is becoming the most important metal additive manufacturing process in many industries. In this paper, one of the common problems of irregularity in the metal deposition in WAAM has been addressed and solved using machine learning (ML). A deep learning-based convolutional neural network (CNN) was used to classify the two classes of deposited beads, i.e. ‘regular bead’ and ‘irregular bead’. A digital camera was installed with a WAAM setup to obtain the images of beads after deposition. A single layer of deposition was conducted on a substrate using aluminium 5356 alloy filler wire using robotic-controlled gas-metal arc welding (GMAW) setup. The performance of the ML model was validated using classification accuracy and processing time. The developed CNN model was checked with three types of proposed datasets. The dataset containing the training and testing ratio of 60:40 achieved an accuracy of 86.53% and 88.08% with 30 and 60 epochs respectively for testing. The proposed ML model was successful in anomaly detection in the deposited bead of WAAM and hence it helps in improving the quality of deposited layers and mechanical properties of fabricated parts.

Characterisation of the Tensile and Metallurgical Properties of Laser Powder Bed Fusion-Produced Ti-6Al-4V ELI in the Duplex Annealed and Dry Electropolished Conditions

Metal additive manufacturing is becoming a popular manufacturing process in industries requiring geometrically complex components, part consolidation, and reductions in material waste. Metals manufactured via additive manufacturing processes such as laser powder bed fusion typically exhibit process-induced defects, material inhomogeneities, and anisotropy in terms of mechanical properties. Post-processing techniques such as heat treatments and surface finishing have been touted as approaches for improving these materials. Although various post-processing techniques have been proposed, the optimal post-processing route remains an active area of research. This research investigates Ti-6Al-4V ELI produced using laser powder bed fusion and post-processed via different routes. The materials in the stress-relieved and duplex annealed material conditions as well as dry electropolished and machined surface conditions were characterised. The duplex annealed Ti-6Al-4V ELI material showed improvements in ductility but at reduced strength when compared with the material in the stress-relieved condition. The microstructure of the duplex annealed material shows little evidence of process-induced defects and features and consists primarily of elongated and acicular α in a lamellar structure with intergranular β and exhibits uniform microhardness throughout the material. A reduced surface roughness due to surface finishing resulted in an improved reduction in area. This research highlights the effects of post-processing treatments and their ability to improve the properties of laser powder bed fusion-produced Ti-6Al-4V ELI.

Resource utilisation of waste TPU: Reform and modification for improving a bentonite-based thermal insulation material’s performance

Thermoplastic polyurethane (TPU) is widely used in the industrial manufacturing process and is internationally recognised as the most environmentally friendly material. In the process of development, it has led to the structural surplus of the low-end TPU and the difficulty in dealing with the waste TPU. This paper introduces the waste TPU in the field of thermal insulation materials in mines. Based upon such research findings, the compound thermal insulation material is made up of the modified polymer environmental protection materials TPU and bentonite in this paper. By formulating magnesia gel material, bentonite covers the surface of TPU particles evenly. A certain thickness of the stable shell can be formed. Mixing with cement, ceramic particles, glass fibre, and other materials, the thermal conductivity of this insulation material is 0.16 W/(mK)(The temperature condition:RT-100℃) and the compressive strength is nearly 1.8 MPa. By transverse temperature diffusion and studying the failure mechanism at extraordinarily high temperature, the new thermal insulation material is corroborated to have certain engineering application value.

Optimization-based path planning framework for industrial manufacturing processes with complex continuous paths

Optimization-based path planning framework for industrial manufacturing processes with complex continuous paths

An integrated design strategy of zinc phthalocyanine dyes for LCD dye-based color filters

Currently, dyes are alternative colorants to traditional pigments in color filters (CFs) of thin film transistor liquid crystal display (TFT-LCD). However, the inferior thermal stability remains an unaddressed issue despite their promising applications. Herein, a series of novel zinc phthalocyanine (ZnPc) dyes with different substituent effects were designed and synthesized. The substituent effects on its optical properties, photo-thermal stabilities and solubility were systematically investigated. Among the newly prepared ZnPc dyes, 3-B4, which contains multiple chlorine atoms and bulky substituents decorated with two methoxy groups, showed the best performance in thermal stability and solubility tests. Furthermore, the color films fabricated by using 3-B4 also displayed the highest photo-thermal stability under simulated industrial manufacturing processes. It indicated the feasibility of 3-B4 as colorants for dye-based CFs. We hope that our design strategy for ZnPc dyes is meaningful for exploring dye-based CFs.

A self-adaptive high precision gripper for shape variant components: Towards higher reliability and efficiency of a cobotic cell

With continuously shrinking batch sizes and increasing order numbers the handling of different object variants becomes a key parameter for the automatization of assembly processes in Industry 4.0. Many compliant and underactuated grasping devices that exploit hierarchical principles, including whipple trees, tendon-pulley systems, pin arrays, fluid differentials, and air pressure, just to name a few, are subject to research. However, these often bionically inspired systems are mostly used for humanoid applications, such as prosthesis, dexterous hands, and crop harvesting. While they can achieve a significant grasp robustness, their precision is often too low for industrial manufacturing processes. Therefore, this paper proposes a deterministic grasping system with one level of adaptation to grasp a great number of object variants in a cobotic assembly cell. This grasping system not only adapts to different shapes but also compensates initial position and alignment errors and guarantees a predefined centring within the grasp. The technology has been tested intensively in the laboratory and subsequently demonstrated in the cobotic cell, where it decreased the machine breakdown time due to pick and place failures by 90%.