Continuous Production Line Research Articles

Energy efficient production planning and scheduling problem with processing technology selection

Energy efficiency attains increasing attention in production systems due to growing energy costs and public perception of environmentally conscious operations. The paper features a capacitated production planning and scheduling problem (CPPS) with sequence-dependent setups for a continuous production line. Meanwhile, energy consumption costs associated with process techniques selection decisions are incorporated into the CPPS. A fix-and-optimize solution approach is proposed to solve the problem. Extensive computational experiments reveal an advantage of the proposed approach having a stable computational performance in solution quality. The optimal results also show the energy cutting benefits of the integrated decision considering operations and energy costs. Moreover, a case study of tea drink production line is presented to demonstrate the application of the model and the solution approach.

Optimization between Tundish Temperature and Slab Exit Temperature to Eliminate “Strand Stuck‐Up” Phenomenon in Continuous Casting Process of Steel by Implementation of Multi‐Objective Evolutionary and Genetic Algorithm

A data‐driven evolutionary genetic algorithm is successfully implemented to optimize two objectives simultaneously from the data generated at an integrated steel plant in view of an efficient continuous casting production line. The objective of optimization is to minimize tundish temperature and maximize the slab exit temperature during continuous casting of steel. EvoNN (Evolutionary Neural Network) coupled with predator prey genetic algorithm is used explicitly to achieve the optimum trade‐off between tundish temperature and slab exit temperature through training of 100 data set considering five process variables: slab width, mold water pressure, average mold water flow, casting speed, and mold water inlet temperature. Optimum trade off curve generated between these two conflicting objectives called as pareto front to yield a set of mutually non‐dominated solutions that helps provide an effective guideline for decision making during the slab production in continuous casting process. It is observed that after a particular critical tundish temperature (i.e., 1555 °C), further enhancement of tundish temperature does not influence the temperature profile of a slab coming out from the caster machine. By optimizing the slab exit temperature (i.e., 815 °C), the proper solidification of slab is achieved with a targeted metallurgical length.

Design of an one-sided transverse flux induction coil by using a numerical optimization algorithm

The transverse flux heating (TFH) concept offers very high electrical efficiency in combination with unique technological flexibility. Numerous advantages make this method beyond competition to be applied in e.g. continuous strip production and processing lines. However, all potential advantages of TFH can be realized in practice only by optimal design of the inductor shape and optimal control of the heaters, using numerical modelling and optimization techniques. The paper describes a successive approach to optimal design of a single layer induction coil, which will be used for onesided TFH of moving thin steel strip with constant width. The peculiarity of the design refers to the very slow movement speed of the strip and the target temperature, which is set below Curie.

Induction Preheating for the Submerged Arc Welded Steel Tube Production

Tenaris is a leading supplier of tubes and related services for the world’s energy industry that manufactures a complete range of seamless and welded steel tubular products. The welding facilities are located in North and South America, and perform three types of welding processes, namely Electric Resistance Welding (ERW), Spiral Submerged Arc Welding, and Longitudinal Submerged Arc Welding (LSAW). Regarding the ERW process, in recent years we studied both the welding using Comsol [1,2], and the ulterior seam heat treatment using an in-house developed FEM model [3,4]. During the LSAW process, after the plate is conformed through the UOE forming process, the butt joint of the pipe is welded in three phases, first of which aims at presenting the edges and joining them but without filling in the bevels, the second one is to fill in the seam region at the inside of the pipe and the third one to do this operation on the outside. The welds are made by melting with an electric arc the bare metal electrodes. Pressure is not applied on the pipe during welding. Depending on the final product properties sought, a preheating near the edges with a relatively narrow temperature range is required for these three welding stages. In this article we focus on the inductive preheating for the tack welding stage of a continuous production line; it involves an open tubular geometry whose edges are facing and separated approximately 10 mm. There is no need to reheat the full tube, but only a region of ±75 mm from the edges approximately. Given the geometry and the required localized preheating, usual solution would be bar type inductors as those used typically for seam annealing. Alternatively, cylindrical coils can be used instead of inductive bars. 2D and 3D numerical models of different inductor bars and cylindrical coils are used in order to assess both kinds of preheaters and to understand the advantages and disadvantages of each one.

Application of computer simulation experiment and response surface methodology for productivity improvement in a continuous production line: Case study

This study focused solely on a paint manufacturing industry in Iran that will help managers to effectively manage their enterprise. The goal of this paper is to integrate simulation modeling along with response surface methodology (RSM) and design of experiments (DOE) in order to analyze and improve the productivity in a selected continuous paint manufacturing industry. Computer simulation is developed to propose different scenarios as the inputs of DOE. Based on the final results, the optimum productivity was achieved at the point of 93.5, that is relevant to the number of labor (B) = 26 and failure time of lifter (C) = 56.01 min. Moreover, the other two factors, A (service rate of delpak mixer) and D (number of permil) should be located at a low level. Quality and production managers, engineers as well as academicians can implement the results of the current study in other case studies. This approach can be generalized to other manufacturing systems to improve their productivity in a timely and cost-effective manner.

Effect of two-step cooling process on mechanical properties of TRIP steel with high performance

A novel two-step cooling experiment was established to simulate the slow cooling process of continuous annealing production line for transformation-induced plasticity (TRIP) steel. The microstructures and mechanical properties of TRIP steel soaked at 700 °C for different time were investigated by tensile test, scanning electron microscopy, X-ray diffraction, and thermodynamic and kinetic calculation. It is shown that the steel soaked for 15 s exhibits the optimal product of strength and elongation (PSE > 30,000 MPa%) due to the transformation of austenite to proeutectoid ferrite, which delays the bainite transformation and improves the stability of retained austenite. In addition, the mechanical properties of TRIP steel soaked over 30 s are much lower, resulting from the precipitation of cementite, which decreases the stability of retained austenite and weakens the TRIP effect.

Online Surface Defect Identification of Cold Rolled Strips Based on Local Binary Pattern and Extreme Learning Machine

In the production of cold-rolled strip, the strip surface may suffer from various defects which need to be detected and identified using an online inspection system. The system is equipped with high-speed and high-resolution cameras to acquire images from the moving strip surface. Features are then extracted from the images and are used as inputs of a pre-trained classifier to identify the type of defect. New types of defect often appear in production. At this point the pre-trained classifier needs to be quickly retrained and deployed in seconds to meet the requirement of the online identification of all defects in the environment of a continuous production line. Therefore, the method for extracting the image features and the training for the classification model should be automated and fast enough, normally within seconds. This paper presents our findings in investigating the computational and classification performance of various feature extraction methods and classification models for the strip surface defect identification. The methods include Scale Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF) and Local Binary Patterns (LBP). The classifiers we have assessed include Back Propagation (BP) neural network, Support Vector Machine (SVM) and Extreme Learning Machine (ELM). By comparing various combinations of different feature extraction and classification methods, our experiments show that the hybrid method of LBP for feature extraction and ELM for defect classification results in less training and identification time with higher classification accuracy, which satisfied online real-time identification.

Simple Methodology to try out Down Time reduction in SMEs with continuous production lines using Delay Time Analysis

Applicability of delay time analysis as a tool for downtime reduction in industry has been already proved through case studies decades ago. This article attempts to encourage the SMEs to implement the technique for downtime management by providing a startup method of implementation by consultants. The algorithm shall be more suitable for those small /medium scale industries engaging continuous production lines that are on the lookout of reducing downtimes or cost of breakdowns. Sample numerical examples and the graphical solution while arriving at optimization of downtime are shown for use by practitioners.

An inventory decision-making model considering continuous production and detection of electric energy metering device

This paper studies the inventory decision problem of power metering device under the condition of continuous production testing. Using the theory of constraints (TOC) to identify bottlenecks existing in the production test system, and then through the establishment of integer programming model, the research focuses on the initial inventory optimisation decision-making of inspection device and checked device in a week. According to the practical decision-making situations, this paper researches the in-out warehouse and vehicle scheduling constraints such as time window; under the condition of continuous operation, it optimises the total cost of inventory and vehicle scheduling. Then we use Matlab software to analyse the model, and use the Flexsim software to simulate the process of testing. In this paper, the research for inventory control of measuring centre with continuous production line provides the beneficial reference for power grid enterprises.

Calibration transfer of a Raman spectroscopic quantification method for the assessment of liquid detergent compositions from at-line laboratory to in-line industrial scale

Calibration transfer or standardisation aims at creating a uniform spectral response on different spectroscopic instruments or under varying conditions, without requiring a full recalibration for each situation. In the current study, this strategy is applied to construct at-line multivariate calibration models and consequently employ them in-line in a continuous industrial production line, using the same spectrometer.Firstly, quantitative multivariate models are constructed at-line at laboratory scale for predicting the concentration of two main ingredients in hard surface cleaners. By regressing the Raman spectra of a set of small-scale calibration samples against their reference concentration values, partial least squares (PLS) models are developed to quantify the surfactant levels in the liquid detergent compositions under investigation. After evaluating the models performance with a set of independent validation samples, a univariate slope/bias correction is applied in view of transporting these at-line calibration models to an in-line manufacturing set-up. This standardisation technique allows a fast and easy transfer of the PLS regression models, by simply correcting the model predictions on the in-line set-up, without adjusting anything to the original multivariate calibration models.An extensive statistical analysis is performed in order to assess the predictive quality of the transferred regression models. Before and after transfer, the R2 and RMSEP of both models is compared for evaluating if their magnitude is similar. T-tests are then performed to investigate whether the slope and intercept of the transferred regression line are not statistically different from 1 and 0, respectively. Furthermore, it is inspected whether no significant bias can be noted. F-tests are executed as well, for assessing the linearity of the transfer regression line and for investigating the statistical coincidence of the transfer and validation regression line. Finally, a paired t-test is performed to compare the original at-line model to the slope/bias corrected in-line model, using interval hypotheses.It is shown that the calibration models of Surfactant 1 and Surfactant 2 yield satisfactory in-line predictions after slope/bias correction. While Surfactant 1 passes seven out of eight statistical tests, the recommended validation parameters are 100% successful for Surfactant 2. It is hence concluded that the proposed strategy for transferring at-line calibration models to an in-line industrial environment via a univariate slope/bias correction of the predicted values offers a successful standardisation approach.

Thermal-Sprayed Coatings on Bushing and Sleeve-Pipe Surfaces in Continuous Galvanizing Sinking Roller Production Line Applications

This paper describes thermal spray techniques for making hard coatings on bushing and sleeve component surfaces. Specifically, plasma-arc welding was used to produce 5-mm thick Co-Cr alloy welding overlays on the bushing, while a high-velocity oxy-fuel spraying technique and laser re-melting technique were used to produce thinner coatings of Co-Cr-Ni+WC of about 1mm thickness on the sleeve-pipe counterparts. The surface-treated components were then submerged in liquid zinc to study the corrosive behaviour of the surface coating and substrate. Both the scanning electron microscope and energy dispersive spectrometer analyses were used to study the microstructure and phase composition of both coatings and substrates prior to and after corrosion experiments. The results show that the microstructure of the bushing consists of γ-cobalt solid solution as well as the eutectic structure of γ-cobalt and carbides, which have good corrosive resistance against molten zinc. Meanwhile, the microstructure of the sleeve pipe consists of a Co-Cr solid solution with various forms of carbides, which displays the combined properties of toughness with good corrosive resistance to molten zinc.

Development of a CCD-based pyrometer for surface temperature measurement of casting billets

In order to achieve high accuracy and good stability of temperature measurement results, an online vision-based temperature field measurement system for continuous casting billets is developed instead of the conventional single-point radiation pyrometer in this paper. This system is a hybrid temperature measurement system which consists of a monochrome array CCD camera with high resolution and a single spot colorimetric thermometer simultaneously. In this system, a narrow-band spectrum radiation temperature measurement model is established for the optical CCD-based pyrometer system, and the non-uniformity of the temperature field measurement due to the inter-element sensitivity deviations of the CCD-array detector and photometric distortion caused by the vignetting in the optical system is analyzed in detail and compensated. Furthermore, in order to eliminate the temperature fluctuation caused by the stripped iron oxide scale on billets, a temperature field reconstruction approach, which took full advantage of the high resolution characteristic of CCD and the distribution character of the surface temperature field of billets, is introduced in this system. Meanwhile, based on the narrow band spectral thermometry theory, the spot temperature measured by the colorimetric thermometer is used to correct the temperature field measured by the CCD camera on-line so as to reduce the temperature measurement error caused by the inconclusive absolute emissivity of different grades of steel and the interference of industrial dust. Currently, the system has been successfully applied and verified in some continuous casting production lines. Industrial trials indicate that the system could effectively eliminate false temperature variation caused by striped iron oxide scale and provide information about changes of processing parameters in the continuous casting production line in real time.

Milling operations in continuous flow production lines

Within the Collaborative Research Center CRC 666 at the TU Darmstadt bifurcated structures in integral style are produced out of steel sheet band. In this context, the cutting technology is used to prepare the semi‐finished product for subsequent forming and machining operations as well as to even manufacturing induced shape and dimensional inaccuracies. In contrast to conventional cutting processes, the workpiece is constantly moved because of the continuous flow production. Thus, high dynamics of the machining process are required. To meet this demand, special built machine tools and the high speed cutting (HSC) technology are applied. This article shows the achieved results in sheet metal and edge milling.

An IoT-Based Online Monitoring System for Continuous Steel Casting

Monitoring solutions using the Internet of Things (IoT) techniques, can continuously gather sensory data, such as temperature and pressure, and provide abundant information for a monitoring center. Nevertheless, the heterogeneous and massive data bring significant challenges to real-time monitoring and decision making, particularly in time-sensitive industrial environments. This paper presents an online monitoring system based on an IoT system architecture which is composed of four layers: 1) sensing; 2) network; 3) service resource; and 4) application layers. It integrates various data processing techniques including protocol conversion, data filtering, and data conversion. The proposed system has been implemented and demonstrated through a real continuous steel casting production line, and integrated with the TeamCenter platform. Results indicate that the proposed solution well addresses the challenge of heterogeneous data and multiple communication protocols in real-world industrial environments.

Optimal shipyard facility layout planning based on a genetic algorithm and stochastic growth algorithm

ABSTRACTThis paper optimises a shipyard layout to minimise the material handling costs during ship construction with regard to the adjacency and alignment conditions of departments. Alignment is a significant factor in shipyard layout design because it potentially reduces the material handling costs by allowing aligned departments to be on a continuous production line, sharing resources. The optimisation process comprises two stages: topological and geometrical optimisations. First, the optimal topology of departments is determined by using a genetic algorithm. Second, a stochastic growth algorithm is used to allow the departments to gradually grow and have their own geometries which satisfy given requirements in area, shape, adjacency, and alignment. A case study is conducted to demonstrate the two-stage algorithm in shipyard layout design in which the effect of considering alignment constraints on shipyard layout design is presented.

Second order hierarchical sandwich structure made of self-reinforced polymers by means of a continuous folding process

One typical way to obtain higher stiffness and strength to weight ratios within structural applications is to use sandwich structures containing lightweight cellular cores. In this study a novel second order hierarchical sandwich structure and its manufacturing principle are described. The whole hierarchical structure is made of a fully recyclable material – different forms of poly-ethylene terephthalate (PET): PET matrix, reinforced with PET fibres (Self reinforced - SrPET) and PET foam resulting in a recyclable structure. The manufacturing path is developed such that it can be implemented within a continuous production line. Out-of-plane compression test are carried out in order to determine the stiffness and strength properties of the proposed structure. An analytical model is developed for evaluating the out-of-plane stiffness and strength properties and used for investigating the influence of the geometric parameters on the structural performance of the proposed hierarchical sandwich structure.

Operation of Flexible Multiproduct Modular Continuous Polymerization Plants

In this work, the operation of modular flexible continuous polymerization plants is investigated. For a benchmark problem (six products with an overall yearly demand of 20,000 t, two continuous production lines with modules that can be moved between the lines) that is defined by industrial speciality polymer producers, the optimal campaign planning problem is solved, minimizing the cost of cleaning and waste production. The results are compared to the production of the same portfolio of products in a batch plant. The results show that the changeovers between different products and production parameters lead to a loss of production capacity and additional costs. For long production campaigns (demand satisfaction on a monthly basis), these costs and losses are comparatively small, whereas for short demand satisfaction periods (one week) they are significant. On the other hand, longer demand satisfaction periods require a significant amount of product inventory and hence storage facilities. image

Designing a fuzzy Q-learning multi-agent quality control system for a continuous chemical production line – A case study

In continuous chemical production lines, final products are produced through irreversible and continuous reactions in successive departments. So, the quality of the final products depends on the quality of the input materials and departments’ situation. Any out of the control situation along a production line leads to operation time, resource and financial lost and spoiled materials. Undoubtedly, decreasing the controlling communication time between different departments and handling unexpected conditions are critical issues in such production lines. In this paper, a Fuzzy Q-learning Multi-Agent Quality Control System (FQL-MAQCS) is proposed to control a continuous chemical production line. The proposed FQL-MAQCS can control unexpected conditions by applying a multi-agent based system consisting of the quality control executor, process data analyst, central decision maker, departmental decision maker, and knowledge and rule manager agents. In addition, the proposed system is equipped with a self-learning mechanism, whose knowledge is gradually formed based on the results of the learning process and stored in the knowledge base. To this end, the fuzzy Q-learning method and rule production mechanism are used. The proposed FQL-MAQCS is tested in a real case study and the associated results demonstrate the usefulness and capability of the developed quality control system.

Variation source identification of multistage manufacturing processes through discriminant analysis and stream of variation methodology: a case study in automotive industry

Product quality problem is a critical issue for multistage manufacturing processes, especially in continuous production lines whereby quality characteristics are measured at the end of the line. Therefore, it is important to reduce process variation by identifying its sources and eliminating its causes. In this regard, a novel approach, to identify the source of variation in multistage manufacturing processes through integration of the Fisher's linear discriminant analysis and the stream of variation methodology, is proposed. Linear discriminant analysis separates the variation of the quality characteristics through the manufacturing process stages while the stream of variation methodology is used for variation propagation modeling in multistage manufacturing processes. Finally, the future deviation is assigned into the analysis in order to identify the source of variation. With an illustrative case study, it is concluded that the proposed approach improves fault diagnosis of continuous production lines in multistage manufacturing processes.

Crystalline silicon on glass by steady-state solution growth using indium as solvent

In order to grow crystalline silicon on glass at low temperatures for photovoltaic applications, a two-step process has been developed. In the first step, amorphous Si films are crystallized at temperatures around 300 °C by metal-induced crystallization, whereby liquid indium serves as a solvent for silicon. Due to the difference of chemical potentials between the amorphous and the crystalline state, an in-plane movement of the liquid metal droplets on the formerly amorphous Si film is accompanied by precipitation of crystalline Si along the droplets’ traces, a process we call amorphous–liquid–crystalline (ALC) transition. In the second step, the ALC seed layers serve as templates for the growth of crystalline silicon by steady-state solution growth. In contrast to common liquid-phase epitaxy, the supersaturation in front of the seed layer is established by a stationary temperature difference between a silicon source and the substrate. Si crystallites in the range of 20–50 µm with low impurity concentrations are grown by this technique. Essential features of steady-state solution growth are compatible with the float glass process in large-scale industrial glass production, which raises hopes for a successive production of glass and thin Si films for solar cells in a continuous production line.