Levenberg-Marquardt Optimization Algorithm Research Articles

Experimental and Engineering Simulation of Loss Characteristics of Silicon Steel Wafers Based on Levenberg-Marquardt Optimization Algorithm

Abstract The loss characteristics of 20WTG1500 silicon steel wafers are simulated and studied by using the Epstein square circle method. First, the loss-measured data are fitted based on the classical Bertotti loss model. When working, a Levenberg-Marquardt (L-M) algorithm is proposed to calculate the parameters of the loss model accurately. For the problem of significant calculation errors of hysteresis loss and stray loss in the classical loss separation model, a variable parameter closely related to the magnetic induction intensity is constructed by using the fixed parameter values of the classical loss model. Then, the loss improvement model is proposed. The accuracy of the improved model is proved by comparing the fitting results of the enhanced model with the measured data.

What if the Covid-19 Pandemic Never Happened Estimation of the Tourist Arrivals for 2020 Via Levenberg-Marquardt Optimization and K-Star (K ) Machine Learning Algorithms

Based on the probability theory, which is used to model uncertainty and randomness in real-world situations, the study aims to understand the impact of uncertain conditions, such as the Covid-19 pandemic, on the accuracy of the algorithms and the resulting losses to a country's tourism industry. The contribution of this paper to the international body of knowledge is twofold: firstly, it advances theoretical understanding of the use of probability theory in modelling real-world problems; and secondly, it offers a methodological approach for estimating tourist arrivals that accounts for the impact of extreme events. To achieve these aims, the Levenberg-Marquardt optimization was first applied to determine the optimal coefficients of the exponential function for estimating tourist arrivals from 1950 to 2020. Next, the K-Star machine learning algorithm was applied to the dataset with and without Covid-19 cases to estimate tourist arrivals.

A parameter determination method of powder burn model based on Levenberg-Marquardt optimization

ABSTRACT The powder burn model is a mathematical model used to characterize the combustion behavior of energetic materials. In this paper, a parameter determination method for the powder burn model is proposed based on the Levenberg-Marquardt (L-M) optimization algorithm, establishing connections between internal ballistics parameters and powder burn model parameters. Taking a 4/1 single-base propellant as an example, the detailed fitting process is presented, and error analysis is conducted. The fittings of the gas state equation and reaction rate equation of powder burn model converge at the 15th and 22nd iterations, respectively. Both fittings displayed a high level of accuracy, with an R-squared value of 0.999. The comparison with the results of the closed bomb experiments demonstrates excellent agreement between pressure-time curves calculated by the powder burn model and the internal ballistics model, confirming their suitability for describing the combustion behavior of propellants. This method provides a foundation for numerically simulating the internal ballistics combustion behavior of energetic materials.

Numerical investigations of ion slip and hall effects on Cattaneo-Christov heat and mass fluxes in darcy-forchheimer flow of Casson fluid within a porous medium, utilizing non-fourier double diffusion theories through artificial neural networks ANNs

The current research focuses on harnessing the computational power of neural networks, notably the Levenberg-Marquardt backpropagation optimization algorithm (ANN-LMBOA), to simulate the behavior of magnetohydrodynamic (MHD) three-dimensional non-Newtonian Darcy-Forchheimer flow of Casson nanofluid over a stretching surface situated within a porous medium. The fluid flow is subject to the influence of Hall and ion slip forces, contributing to its behavior. Additionally, the Cattaneo–Christov heat flux model is incorporated to represent the temperature balance in the boundary layer accurately. Non-Newtonian fluids improve thermal transmission due to their shear-thinning propensity, increased turbulence, and more excellent convective heat transfer. The thermal conductivity of nanofluids is increased by the inclusion of nanoparticles, enabling more effective heat transmission. Buongiorno's model is used for the nanofluid phenomena that concentrate on thermophoresis and Brownian motion. To tackle the flow problem efficiently, the governing equations are transformed into non-linear differential equations using self-similar variables. The ANN-LMBOA algorithm is then employed to solve these equations, providing a practical and accurate approach to the simulation. To train and validate the ANN-LMBOA model, a dataset is generated using the bvp4c numerical method, which can easily handle highly non-linear coupled differential equations. The dataset is created for different scenarios of flow parameters, facilitating comprehensive training, testing, and authentication of the neural network. The accuracy of the ANN-LMBOA model is assessed through various statistical neural network tools, such as MSE, RP, CF graphs, and EH. The study thoroughly investigates flow model parameters related to momentum, energy, and concentration profiles, which are presented using visual representations to understand the system's behavior and characteristics comprehensively. The effects of various parameters of interest like Casson, ion slip, porosity, thermal relaxation, and magnetic parameters on velocity, temperature, and concentration distribution have been studied numerically via ANN-LMBOA networks. The absolute error of reference and target data is obtained in the range of 10−4 − 10−5which proves the best accuracy performance of ANN-LMBOA networks. By raising the Prandtl and Schmidt numbers, solutal, thermal, and diffusional variables, one can reduce the diffusion of mass and heat.

Optimization of effluents using a neural network in the treatment of industrial wastewater

The growth of the planet's population leads to an increase in the problem of access to fresh water. The main sources of water on Earth are brackish and sea water. In connection with the water crisis, water purification becomes an extremely important process, and its achievement is carried out through desalination and various methods of water treatment. In this context, research into the possibility of using neural networks to improve the operation of sewage treatment plants is necessary. The purpose of the research was to optimize and analyze the efficiency of the work of treatment facilities in the treatment of industrial wastewater. Soft computing methods were used to optimize the proposed models. In this study, the exact results of the application of the neural network were determined using analytical and comparative approaches. Treatment of all wastewater and waste generated in the treatment industry involves a number of processes including air flotation, chemical coagulation, settling and biological treatment using fully mixed activated sludge. Various learning functions have been considered, including forward-propagation artificial neural networks (ANNs) such as multilayer perceptron (MLP), cascaded forward-propagation ANNs, and support vector regression (SVR) models. The learning process includes the use of Levenberg-Marquardt optimization algorithms and sequential minimum. The article also provides graphical images illustrating the different types of pollutants, the costs associated with treatment plants, and the color changes in wastewater observed after the treatment process. The obtained results show a high degree of similarity between the predicted and experimental data, which emphasizes the effectiveness of the backpropagation ANN model for accurate predictions. In addition, the integration of machine learning into the production of detergents can be extremely effective in promoting the efficient and sustainable use of water resources. Overall, the paper provides valuable insights into the use of machine learning to address freshwater scarcity

A numerically efficient output-only system-identification framework for stochastically forced self-sustained oscillators

Self-sustained oscillations are ubiquitous in nature and engineering. In this paper, we propose a novel output-only system-identification framework for identifying the system parameters of a self-sustained oscillator affected by Gaussian white noise. A Langevin model that characterizes the self-sustained oscillator is postulated, and the corresponding Fokker–Planck equation is derived from stochastic averaging. From the drift and diffusion terms of the Fokker–Planck equation, unknown parameters of the system are identified. We develop a numerically efficient algorithm for enhancing the accuracy of parameter identification. In particular, a modified Levenberg–Marquardt optimization algorithm tailored to output-only system identification is introduced. The proposed framework is demonstrated on both numerical and experimental oscillators with varying system parameters that develop into self-sustained oscillations. The results show that the computational cost required for performing the system identification is dramatically reduced by using the proposed framework. Also, system parameters that were difficult to be extracted with the existing method could be efficiently computed with the system identification method developed in this study. Pertaining to the robustness and computational efficiency of the presented framework, this study can contribute to an accurate and fast diagnosis of dynamical systems under stochastic forcing.

Machine learning assisted coupled frequency analysis of skewed multi-phase magnetoelectric composite plates with temperature and moisture dependent properties

In this article, the application of an artificial neural network (ANN)-based machine learning (ML) strategy to predict the coupled frequency of geometrically skewed multiphase magnetoelectric (MME) composite plate exposed to hygrothermal environment is presented. The ANN model is trained using a dataset comprising more than one million simulations conducted using an in-house developed finite element formulation. The underlying multiphysics governing equations are derived using Hamilton’s principle and higher-order shear deformation theory (HSDT). The influence of the hygrothermal environment on the elastic stiffness of MME composites is defined by the empirical constants in the constitutive relations. Four prominent combinations of the empirical constants leading to different elastic stiffness relations have been considered in this study. Alongside, the influence of geometrical skewness on the coupled fundamental frequency is also assessed. For the training of the ANN model, the Levenberg–Marquardt optimization algorithm with 30 neurons along with a tangent sigmoid activation function is used. The trained ANN model is tested on an unseen dataset, different from the training data, and it is shown to accurately predict the natural frequency of MME plate with a maximum error of 2.3%. Further, excluding the training time and considering the computational time alone, the ANN model is found to be 6.3 times faster than the FE simulations. It is anticipated that such ML-based reduced order models can be effective in the design process, especially in complex multiphysics problems, such as the one considered in the work, involving a multitude of geometric, loading and material parameters.

Dry-sliding wear properties of 3D printed PETG/SCF/OMMT nanocomposites: Experimentation and model predictions using artificial neural network

In this article, an attempt has been made to experimentally investigate the synergistic effect of organically modified montmorillonite (OMMT) nanoclay and short carbon fibers (SCFs) on the tribological behaviour of additively manufactured Polyethylene Terephthalate Glycol (PETG) based nanocomposites. The tribo-specimens are 3D printed using fused deposition modelling (FDM). The tribological properties, that is, specific wear rate (SWR) and coefficient of friction (CoF) of various PETG/SCF/OMMT nanocomposites were assessed by performing dry-sliding wear test. In addition, an artificial neural network (ANN) methodology is proposed to accurately predict the wear performance of PETG nanocomposites. The ANN model is trained using the datasets obtained from the experimentation. For the training of the ANN model, the Levenberg–Marquardt optimisation algorithm with 10 neurons along with a tangent sigmoid activation function is utilised. Additional experimentation was performed for arbitrary load and sliding velocity which were not used for training the ANN model and the results were compared to assess the predictive capability of the ANN model on unseen data. The proposed ANN methodology predicted the SWR and CoF with agreeable accuracy. It is believed that by adopting the proposed ANN methodology, the experimentation costs and time can be significantly reduced without compromising on the accuracy of the results.

Pore structure evolution of Qingshankou shale (kerogen type I) during artificial maturation via hydrous and anhydrous pyrolysis: Experimental study and intelligent modeling

In this study, alterations in the pore structure of the Qingshankou shale during hydrous and anhydrous pyrolysis (HP and AHP) over wide ranges of temperature (300–450 °C) were studied to elucidate the role of water. Mineralogy of the original shale sample along with geochemical properties and pore structure after each step of artificial thermal maturation were analyzed using X-ray diffraction (XRD), Rock-Eval pyrolysis, and N2 adsorption, respectively. Complexity of pore structure was assessed via fractal dimension analysis followed by delineating different pore families in the samples' PSD curves. Next, four common artificial neural networks (ANNs) models were implemented along with Bayesian regularization (BR) and Levenberg-Marquardt (LM) optimization algorithms to model the N2 adsorption/desorption volume as thermal maturity progressed. Results showed that all samples represent Type IV hysteresis loop while the amount of N2 adsorption during HP was much higher than that of the AHP revealing the importance of water. Overall, the complexity of AHP samples’ pore structure was less than that of the HP since the count of pores with smaller pore width was reduced more during thermal progression. Deconvolution analysis showed that the original sample has eight pore families (7 in mesopore and 1 in the macropore range), while pore families with larger mean pore width were formed and expanded and pore families with smaller mean pore width were eliminated as thermal maturation progressed in both HP and AHP. Moreover, the average absolute percent relative error (AAPRE) of 3.74% for the entire data inferred that generalized regression neural network (GRNN) model was superior for the estimation of N2 volume adsorbed and desorbed. Finally, sensitivity analysis revealed that the relative pressure and pyrolysis method (AHP or HP) respectively had the strongest linear and non-linear relationships with the N2 adsorption/desorption volume. Collectively, this case confirmed the role that water plays in the evolution of pore structures throughout thermal maturation, which could be even more significant than temperature.

Evaluating energy absorption of sustainable rubber crumb/kenaf composites through artificial neural network strategies for low‐velocity impact loads

AbstractThis study presents an innovative approach utilizing Artificial Neural Network (ANN) strategies to evaluate the energy absorption capabilities of eco‐friendly rubber crumb/kenaf composites subjected to low‐velocity impact loads. The primary objectives of this research were to assess the energy absorption characteristics of these sustainable composites, understand their mechanical behavior under the impact, and provide valuable insights into their potential applications. To achieve these objectives, an experimental methodology was employed. Rubber crumb/kenaf composites with varying compositions were prepared, and low‐velocity impact tests were conducted using a drop‐weight impact testing apparatus to assess their energy absorption behavior and these parameters were used as inputs for training the ANN models. The weight percentage of waste tire rubber particle (WTRP), type of impactor and impact energies are considered as input data, whereas the absorbed energy is treated as the output. Through the advanced ANN strategies, accurate predictions of energy absorption performance were achieved for the rubber crumb/kenaf composites. The Levenberg–Marquardt optimisation algorithm with ten neurons and a tangent sigmoid activation function is used to train the ANN model. The trained ANN model is tested on an unseen dataset, different from the training data. It is shown to accurately predict the energy absorption characteristics of WTRP/KRE composites with a maximum error of 4.54%. The results revealed that the composite's energy absorption capabilities were influenced by the ratio of rubber crumb to kenaf, as well as the impact velocity. Additionally, the ANN models demonstrated excellent predictive capabilities, enabling efficient estimation of energy absorption behavior. The significance of these results lies in the potential applications of eco‐friendly rubber crumb/kenaf composites. By understanding their energy absorption characteristics, these composites can be effectively utilized in various industries. For instance, they could be employed in automotive parts manufacturing to enhance occupant safety during low‐velocity impact events. Furthermore, these composites can find applications in sports equipment, protective gear, and other impact‐prone products, contributing to sustainable and environmentally friendly materials. It is believed that by adopting the proposed ANN methodology, the experimentation costs and time can be significantly reduced without compromising the accuracy of the results. The obtained results provide valuable insights into the mechanical behavior of these sustainable composites and open avenues for their implementation in diverse industries where impact resistance is crucialHighlights Development of sustainable Rubber crumb/kenaf composites Application of advanced ANN based predictive assessment of the impact response Parametric study of impactor shape and height of impact is performed The damage mechanism contributing to energy absorption is micrographically studied

Parameter Determination of the 2S2P1D Model and Havriliak-Negami Model Based on the Genetic Algorithm and Levenberg-Marquardt Optimization Algorithm.

This study utilizes the genetic algorithm (GA) and Levenberg-Marquardt (L-M) algorithm to optimize the parameter acquisition process for two commonly used viscoelastic models: 2S2P1D and Havriliak-Negami (H-N). The effects of the various combinations of the optimization algorithms on the accuracy of the parameter acquisition in these two constitutive equations are investigated. Furthermore, the applicability of the GA among different viscoelastic constitutive models is analyzed and summarized. The results indicate that the GA can ensure a correlation coefficient of 0.99 between the fitting result and the experimental data of the 2S2P1D model parameters, and it is further proved that the fitting accuracy can be achieved through the secondary optimization via the L-M algorithm. Since the H-N model involves fractional power functions, high-precision fitting by directly fitting the parameters to experimental data is challenging. This study proposes an improved semi-analytical method that first fits the Cole-Cole curve of the H-N model, followed by optimizing the parameters of the H-N model using the GA. The correlation coefficient of the fitting result can be improved to over 0.98. This study also reveals a close relationship between the optimization of the H-N model and the discreteness and overlap of experimental data, which may be attributed to the inclusion of fractional power functions in the H-N model.

Design of an Anthracite Creep Model Based on Fractional Order Theory: Experiments and Simulations

Fractional order theory was used to characterize the accelerated creep phase of a nonlinear creep model. To accurately describe each stage of the anthracite creep model, the “gyroscope” unit was introduced by combining the Heaviside function and the creep damage definition. The effect of damage on anthracite creep was determined by designing and completing three-axis graded-separation loading creep tests on anthracite. The test curves were combined to classify anthracite into five stages: transient deformation, pseudo-acceleration, deceleration, isothermal, and acceleration creep. Each stage was combined with suitable components to form a combined fractional-order creep model. The one-dimensional equation of the state of the model was extended to three dimensions. The Levenberg–Marquardt optimization algorithm for fitting origin rheological curves was used to complete the fitting of the basic parameters. Finite differences were performed on the model equation of state, and a secondary development of a combined fractional-order creep model (NEG) was completed based on the built-in Burgers model in FLAC3D. A comparison of the numerical simulation results shows that the combined fractional-order creep model is important for accurately predicting the full creep stage of anthracite.

Study of Temperature Rise Characteristics and Voltammetry Characteristics of ZnO Varistor under 10/350μs Pulse Current

To study the effect of 10/350μs pulse current on the electrical performance of ZnO varistor, a high-current impulse experimental study was conducted in this paper. Firstly, the varistor was subjected to different amplitude impulse discharge experiments, and the temperature change of the varistor after the impulse was recorded. Then, the temperature of the varistor at the moment of 1 second after the pulse impulse was suffered by the Asymptotic function, and the Levenberg-Marquardt optimization algorithm was calculated to compare the temperature rise of the varistor under different current amplitudes. Finally, the upward shift of voltammetry characteristics in the high-current region after the impulse was explained according to the double Schottky barrier model and the linear chain aging theory. The results show that the higher the pulse current amplitude is, the higher the temperature rise of the varistor is at the 1s moment. The highest voltammetry characteristic upshift is caused by the 6.4kA pulse current. The temperature rise and the voltammetry characteristic drift in the high-current region are positively correlated. The results of the study can provide a reference for manufacturers to produce ZnO varistors for direct lightning protection of buildings.

Semi-active fuzzy cooperative control of vehicle suspension with a magnetorheological damper

This study attempts to improve the vibration isolation performance of a vehicle suspension system with a magnetorheological damper (MRD) under complex driving conditions. Structure parameter uncertainty, disturbance of the driving process, and response time delay of MRD are all addressed. Firstly, experiments of MRD were carried out in a damping force testing machine to identify the parameters of the MRD adjustable Sigmoid model by the Levenberg-Marquardt optimization algorithm. Then, the parameter identification is verified by comparing experimental and simulation data. Secondly, the state space equations of the suspension system are derived by Newton’s second law. The transfer function from the bounded disturbance input to the control output is obtained based on H∞ control theory. To make the Infinite norm of the system transfer function less than a certain value, three control strategies are proposed: variable structure control (VSC), disturbance rejection control (DRC), and delay tolerance control (DTC). Thirdly, considering these issues together to weaken the effect of disturbances on vehicle driving conditions, a fuzzy cooperative control (FCC) strategy is proposed based on the linear matrix inequality (LMI) theory. Simulation results demonstrate that FCC semi-active vehicle suspension systems conduct effective vibration isolation performance while responding to multiple external disturbances.

Modeling and Compensation of Positioning Error in Micromanipulation

In order to improve the positioning accuracy of the micromanipulation system, a comprehensive error model is first established to take into account the microscope nonlinear imaging distortion, camera installation error, and the mechanical displacement error of the motorized stage. A novel error compensation method is then proposed with distortion compensation coefficients obtained by the Levenberg–Marquardt optimization algorithm combined with the deduced nonlinear imaging model. The compensation coefficients for camera installation error and mechanical displacement error are derived from the rigid-body translation technique and image stitching algorithm. To validate the error compensation model, single shot and cumulative error tests were designed. The experimental results show that after the error compensation, the displacement errors were controlled within 0.25 μm when moving in a single direction and within 0.02 μm per 1000 μm when moving in multiple directions.

Numerical solution of critical force of n-step telescopic boom with superlift device

The installation of a superlift device on the telescopic boom of a crane is the main method to improve the stress condition of the telescopic boom and to effectively improve the lifting performance of the crane. However, there is a lack of an effective and accurate numerical method to solve the critical force of a crane telescopic boom with a superlift device. Therefore, the telescopic boom with a superlift device is reasonably simplified as a stepped column and double cable model. Based on the longitudinal and transverse bending theory, the differential equations of the deflection of the n-stepped column with double cables model are established, the buckling characteristic equation and its recurrence formula of the n-stepped column with double cables model are derived, and the combined superlift equations are established. The Levenberg–Marquardt optimization algorithm is used to solve the critical force and length coefficient. A large amount of data were compared and analyzed between the numerical solution results and the ANSYS finite element simulation results. The error result analysis proved the correctness of the deduced buckling characteristic equation and its derived formula and the accuracy of the numerical solution. The numerical solution method in this paper can be used to calculate the critical force and length coefficient under any number of boom sections, boom section length, sectional moment of inertia, cable length, and included angle between the two cables of the telescopic boom with a superlift device. The numerical solution method can provide a technical support for the structural design of a telescopic boom with a superlift device in practical projects.

A Machine Learning-Based Method for Modelling a Proprietary SO2 Removal System in the Oil and Gas Sector

The aim of this study is to develop a model for a proprietary SO2 removal technology by using machine learning techniques and, more specifically, by exploiting the potentialities of artificial neural networks (ANNs). This technology is employed at the Eni oil and gas treatment plant in southern Italy. The amine circulating in this unit, that allows for a reduction in the SO2 concentration in the flue gases and to be compliant with the required specifications, is a proprietary solvent; thus, its composition is not publicly available. This has led to the idea of developing a machine learning (ML) algorithm for the unit description, with the objective of becoming independent from the licensor and more flexible in unit modelling. The model was developed in MatLab® by implementing ANNs and the aim was to predict three targets, namely the flow rate of SO2 that goes to the Claus unit, the emissions of SO2, and the flow rate of steam sent to the regenerator reboiler. These represent, respectively, the two physical outputs of the unit and a proxy variable of the amine quality. Three different models were developed, one for each target, that employed the Levenberg–Marquardt optimization algorithm. In addition, the ANN topology was optimized case by case. From the analysis of the results, it emerged that with a purely data-driven technique, the targets can be predicted with good accuracy. Therefore, this model can be employed to better manage the SO2 removal system, since it allows for the definition of an optimal control strategy and the maximization of the plant’s productivity by not exceeding the process constraints.

An artificial neural network visible mathematical model for real-time prediction of multiphase flowing bottom-hole pressure in wellbores

Accurate prediction of multiphase flowing bottom-hole pressure (FBHP) in wellbores is an important factor required for optimal tubing design and production optimization. Existing empirical correlations and mechanistic models provide inaccurate FBHP predictions when applied to real-time field datasets because they were developed with laboratory-dependent parameters. Most machine learning (ML) models for FBHP prediction are developed with real-time field data but presented as black-box models. In addition, these ML models cannot be reproduced by other users because the dataset used for training the machine learning algorithm is not open source. These make using the ML models on new datasets difficult. This study presents an artificial neural network (ANN) visible mathematical model for real-time multiphase FBHP prediction in wellbores. A total of 1001 normalized real-time field data points were first used in developing an ANN black-box model. The data points were randomly divided into three different sets; 70% for training, 15% for validation, and the remaining 15% for testing. Statistical analysis showed that using the Levenberg-Marquardt training optimization algorithm (trainlm), hyperbolic tangent activation function (tansig), and three hidden layers with 20, 15 and 15 neurons in the first, second and third hidden layers respectively achieved the best performance. The trained ANN model was then translated into an ANN visible mathematical model by extracting the tuned weights and biases. Trend analysis shows that the new model produced the expected effects of physical attributes on FBHP. Furthermore, statistical and graphical error analysis results show that the new model outperformed existing empirical correlations, mechanistic models, and an ANN white-box model. Training of the ANN on a larger dataset containing new data points covering a wider range of each input parameter can broaden the applicability domain of the proposed ANN visible mathematical model.

Two-factor Heston model equipped with regime-switching: American option pricing and model calibration by Levenberg–Marquardt optimization algorithm

In this paper, we consider the pricing of American options under a regime-switching double Heston model, such that the interest rate and mean-reversion level parameters in both stochastic volatility models shift in various states. We develop a semi-analytical formula for double Heston partial differential equation by using the equivalent European put option price and standard portfolio-consumption model. Then through the moment-generating function of this particular model, the American put option price is evaluated. We employ the Levenberg–Marquardt optimization method to calibrate the regime-switching double Heston model. Numerical experiments have also been performed to demonstrate the accuracy of the proposed formula and the performance of regime change mechanism on option pricing. Ultimately, through an experimental application, we indicate the proposed model is premier to the double Heston model, which illustrates the importance of considering regime-switching factor.

Design of a mobile 3D imaging system based on 2D LIDAR and calibration with levenberg–marquardt optimization algorithm

LIDAR is an excellent means to obtain the information of buildings, forests, bridges, tunnels and many other big scenes, but the high price of 3D LIDAR currently limits its further application. To meet this challenge a mobile 3D imaging system based on 2D LIDAR is proposed. The system has the characteristics of large imaging range and low cost. The composition and implementation principle of each module of the system are introduced in detail and a calibration method for the 3-axis assembly error is proposed. In this method Levenberg-Marquardt (LM) optimization algorithm is used to obtain the optimal value of the 2D LIDAR 3-axis attitude angle, which is used to compensate for the point cloud distortion caused by the assembly error. The experimental results show that the proposed method can effectively reduce the point cloud distortion caused by assembly error. This system can meet the application demand of big scenes 3D imaging.