Actual Manufacturing Research Articles

Discrete-Event Simulation Integrates an Improved NEH Algorithm for Practical Flowshop Scheduling Problems in the Satellite Industry

The production of multiple types of satellites based on a common manufacturing platform represents a permutation flowshop scheduling problem (PFSP) with complex constraints. This is a highly complex scheduling problem, yet there is still a gap between theoretical research and practical application, particularly in the satellite industry. Therefore, we propose a more practical method that integrates discrete-event simulation modelling and an improved NEH algorithm to solve a more realistic PFSP. The discrete-event simulation-based method includes the following three main components: a flexible PFSP simulation modelling approach, an improved NEH algorithm, and an interaction mechanism between the simulation model and the optimisation algorithm. The proposed method allows automatic and flexible simulation modelling according to the characteristics of the actual satellite manufacturing workshop, which determines the practical nature of the approach proposed in this paper and then achieves excellent scheduling results based on the special interaction mechanism. The computational results demonstrate that this is a 9.18% improvement over the initial NEH algorithm and a 1.40% improvement over the best current improved NEH algorithm.

Deformation Evolution and Perceptual Prediction for Additive Manufacturing of Lightweight Composite Driven by Hybrid Digital Twins

This paper proposes a deformation evolution and perceptual prediction methodology for additive manufacturing of lightweight composite driven by hybrid digital twins (HDT). In order to improve manufacturing quality of irregular lightweight composite through boosting conceptual design in aeronautic and aerospace engineering, the HDT meaning hybridization of physical and digital domains, including deformation and energy efficiency can be built, where the essential parameters can be perceptually predicted in advance, by virtue of the fusion of physical sensors and digital information. The long short term memory (LSTM) can be employed to void vanishing gradient problem and improve predicting precision via Recurrent Neural Networks, thereby laying a foundation for the HDT. The diverse manufacturing requirements of different regions are integrated into the parameters designing phase by attaching region weights confirmed via empiricism and in-service simulation. The effects of slicing strategy and external support structures on manufacturing quality are considered from the perspective of improving dimensional accuracy. The manufacturing efficiency and comprehensive costs are accounted as consideration factors, which are perceptually predicted via LSTM. The designed manufacturing parameters through HDT were virtually examined by evaluating the deformation and equivalent stress distributions of fabricated lightweight component with composite material through AM process simulation. The physical experiments were conducted to verify the HDT-based pre-designing and optimization method of manufacturing parameters via fused deposition modeling (FDM). The energy consumption of actual manufacturing process was measured via digital power meter and applied to evaluate accuracy of perceptual prediction outcomes. The dimensional accuracy and distortion distribution of the manufactured lightweight prototype made with composite material were measured through the coordinate measuring machine (CMM) and 3D optical scanner. The proposed method demonstrates effectiveness in improving manufacturing quality and accurately predicting energy consumption, which have been verified with a three-way solenoid valve element, in which the maximum deformation was reduced by 39.78% and the mean absolute percentage error for perceptual prediction was 3.76%.

Solvent diffusion mechanism of gun propellant revealed through molecular dynamics simulation

Drying is an important operational unit in the preparation of nitrocellulose-based gun propellant, which affects the drying quality and drying rate of the product. The diffusion properties of solvents during the drying process determine the rate of internal solvent removal from the gun propellant, so the diffusion behaviors of ethanol and acetone in the gun propellant were investigated based on molecular dynamics simulations. Three models of single-, double- and triple-base gun propellant components were established, and the interactions between the solvent and the components of gun propellant were investigated by radial distribution function, hydrogen bonding and interaction energy calculations, and the free volumes, diffusion coefficients and the activation energies of the solvents were used to describe the solvent diffusion performances. The results showed that the interaction between ethanol and the components of the gun propellant was mainly hydrogen bonding, and the interaction between acetone and the components was mainly van der Waals forces. When the mass ratio of ethanol and acetone is 1:1, the interaction energy between ethanol and the components is greater due to the presence of strong hydrogen bonding, which resulted in a higher diffusion activation energy of ethanol. However, since the molecular volume of ethanol is higher than that of acetone, it makes the diffusion coefficient of ethanol larger. Compared with single-base gun propellant, after adding nitroglycerin and nitroguanidine, the free volume fraction decreases, the sum of the interaction energies between the solvent and the components in gun propellant increases, and the diffusion activation energy increases. Hence, this present work regarding the study of solvent diffusion mechanism of gun propellant can provide theoretically guiding suggestion to the actual drying process and manufacture of gun propellant.

Abnormal pattern recognition for online inspection in manufacturing process based on multi-scale time series classification

The collection of large volumes of temporal data during the production process is streamlined in a cyber manufacturing environment. The ineluctable abnormal patterns in these time series often serve as indicators of potential manufacturing faults. Consequently, the presence of effective analytical methods becomes essential for monitoring and recognizing these abnormal manufacturing patterns. However, the extensive process data may contain various minor abnormal patterns, typically reflecting changes in production status influenced by multiple anomalous causes. This study introduces an approach for recognizing abnormal manufacturing patterns through multi-scale time series classification (TSC). Long-term process signals undergo slicing using dynamically sized observation windows and subsequent classification at multiple scales employing our proposed TSC model, the distance mode profile-multi-branch dilated convolution network (DMP-MDNet). DMP-MDNet comprises two key modules aimed at bypassing complicated feature engineering and enhancing generalization capability. The first module, DMP, uses similarity measurement to encode scale- and magnitude-invariant temporal properties. Subsequently, the MDNet, equipped with multi-receptive field sizes, effectively leverages multi-granularity data for accurate classification. The effectiveness of our method is demonstrated through the analysis of a real-world body-in-white production dataset and various widely used public TSC datasets, showing promising applicability in actual manufacturing processes.

Fabric Defect Detection in Real World Manufacturing Using Deep Learning

Defect detection is very important for guaranteeing the quality and pricing of fabric. A considerable amount of fabric is discarded as waste because of defects, leading to substantial annual losses. While manual inspection has traditionally been the norm for detection, adopting an automatic defect detection scheme based on a deep learning model offers a timely and efficient solution for assessing fabric quality. In real-time manufacturing scenarios, datasets lack high-quality, precisely positioned images. Moreover, both plain and printed fabrics are being manufactured in industries simultaneously; therefore, a single model should be capable of detecting defects in all kinds of fabric. So training a robust deep learning model that detects defects in fabric datasets generated during production with high accuracy and lower computational costs is required. This study uses an indigenous dataset directly sourced from Chenab Textiles, providing authentic and diverse images representative of actual manufacturing conditions. The dataset is used to train a computationally faster but lighter state-of-the-art network, i.e., YOLOv8. For comparison, YOLOv5 and MobileNetV2-SSD FPN-Lite models are also trained on the same dataset. YOLOv8n achieved the highest performance, with a mAP of 84.8%, precision of 0.818, and recall of 0.839 across seven different defect classes.

Construction of digital twin workshop integrated with edge computing and deep learning

Abstract This study addresses the challenges of real-time data synchronization and big data processing in the construction of digital twin workshops under the background of intelligent manufacturing. A solution that integrates edge computing with deep learning technology is proposed. Through a multi-layered, modular framework structure, this solution achieves real-time synchronous mapping and interaction between physical workshops and virtual workshops, effectively improving production efficiency, reducing maintenance costs, and ensuring product quality. The edge computing layer in the research is responsible for preprocessing heterogeneous data from multiple sources and making rapid response decisions. Simultaneously, deep learning algorithms are employed to conduct in-depth analysis, forecasting, and optimization of the production process. Case studies demonstrate that this solution can significantly improve equipment utilization and optimize workpiece scheduling, validating its application value in actual manufacturing environments.

Effect of contact blast loading on the plastic deformation forming ability of large steel pipes

Plastic deformation forming with metal pipe blanks by contact blast loading inside pipes is an interesting moldless forming technique, also a complex and error-prone process. Some advantages are very characteristic of this forming technique such as no cost of mold, tooling and low energy consumption, no complicated control equipment compared to other forming techniques such as casting, rolling, tube hydrostatic forming, bending – welding. Up to now, the calculation and design of this forming technique mainly use some existing reference empirical formulas, so the experimental results are only suitable in the range of small pipe diameters, and still there are significant deviations for larger pipe diameters. In order to increase the predictability and accuracy of forming process by contact blast loading inside large pipes, this paper presents a study on the influence of the mass of highly explosive material – TNT to the forming ability of large steel pipes from API-5LX-42 mild steel materials by modern 3D numerical simulation – using Abaqus/Cae software. Four output criteria with maximum values are used to evaluate the efficiency of this forming process, including maximum diameter of the blast zone (Dmax£2D0), Von Mises stress (Smax£UTS), Hoop plastic strain component (PE22max£1), and Pipe wall thinning rate (eT-max£60 %). The results of this research on the plastic deformation forming process using numerical simulation can be used for the next experimental step to evaluate the difference between simulation and experiment, as well as use this data in the calculation and design of pipe products with circular or square cross-sections to save both time and money of trial and error before application in actual manufacturing

Effect of flip-chip ball grid array structure on capillary underfill flow

During the capillary underfill (CUF) process in flip-chip packaging, issues like incomplete filling, voids, wire sweeping, and overflow can impact product reliability and the relatively long filling time required. Therefore, finding ways to improve filling efficiency is a critical challenge. Conducting experiments to address these issues can be costly. As a result, the industry often relies on simulation software to simulate the actual manufacturing process, predict potential problems, compare results with experimental data, and analyze ways to improve the process. This approach helps enhance the package yield and further improves production efficiency.This study investigates the capillary underfill (CUF) process through simulation. Firstly, the viscosity and reaction kinetics of the underfill material are measured to establish the necessary data for the simulation. Then, Moldex3D, a mold flow simulation software, is used to create the model, generate the mesh, and perform the process simulation analysis. The effects of material properties and process parameters on the capillary underfill process are considered. The simulation results are compared with experimental data to validate the feasibility of the simulation. Subsequently, different flip-chip package structures and CUF process parameters are analyzed through simulation to explore the impact of these parameters on the flow pattern and filling efficiency of the adhesive material during the filling process.According to the simulation results, the filling speed is faster when the bump pitch and gap height are larger. However, the required filling time still has a considerable relationship with the volume of the filling region. Dispensing from the side with lower bump density can shorten the filling time when the bump distribution is uneven.

Construction of meta-dynamic recrystallization dynamic model of 316L stainless steel based on grain orientation spread during continuous variable rate thermal deformation

Because the deformation parameters (temperature, strain rate) in the actual manufacturing process are non-constant. We investigate meta-dynamic recrystallization behavior under the transient mutation state. First, based on a single-pass thermal deformation experiment and electron backscatter diffraction (EBSD) technique, this study establishes a quantitative calculation method for the volume fraction of meta-dynamic recrystallization under different deformation conditions, and find the GOS threshold basis for judging the occurrence of meta-dynamic recrystallization. Comparing the experimental results with the numerical simulation results, it is found that the MDRX behavior of the material could be accurately described. The meta-dynamic recrystallization evolution law of the material in the thermal deformation insulation stage under the variable strain rate state is investigated by continuous variable rate thermal deformation experiment and numerical simulation. It is observed that an increase of the average strain rate in the continuous variable strain rate state would promote the meta-dynamic recrystallization softening behavior of the material in comparison with the steady state deformation stage, and vice versa.

DG2GAN: improving defect recognition performance with generated defect image sample

This article aims to improve the deep-learning-based surface defect recognition. In actual manufacturing processes, there are issues such as data imbalance, insufficient diversity, and poor quality of augmented data in the collected image data for product defect recognition. A novel defect generation method with multiple loss functions, DG2GAN is presented in this paper. This method employs cycle consistency loss to generate defect images from a large number of defect-free images, overcoming the issue of imbalanced original training data. DJS optimized discriminator loss is introduced in the added discriminator to encourage the generation of diverse defect images. Furthermore, to maintain diversity in generated images while improving image quality, a new DG2 adversarial loss is proposed with the aim of generating high-quality and diverse images. The experiments demonstrated that DG2GAN produces defect images of higher quality and greater diversity compared with other advanced generation methods. Using the DG2GAN method to augment defect data in the CrackForest and MVTec datasets, the defect recognition accuracy increased from 86.9 to 94.6%, and the precision improved from 59.8 to 80.2%. The experimental results show that using the proposed defect generation method can obtain sample images with high quality and diversity and employ this method for data augmentation significantly enhances surface defect recognition technology.

Research on Multi-Objective Flexible Job Shop Scheduling Problem with Setup and Handling Based on an Improved Shuffled Frog Leaping Algorithm

Flexible job shop scheduling problem (FJSP), widely prevalent in many intelligent manufacturing industries, is one of the most classic problems of production scheduling and combinatorial optimization. In actual manufacturing enterprises, the setup of machines and the handling of jobs have an important impact on the scheduling plan. Furthermore, there is a trend for a cluster of machines with similar functionalities to form a work center. Considering the above constraints, a new order-driven multi-equipment work center FJSP model with setup and handling including multiple objectives encompassing the minimization of the makespan, the number of machine shutdowns, and the number of handling batches is established. An improved shuffled frog leading algorithm is designed to solve it through the optimization of the initial solution population, the improvement of evolutionary operations, and the incorporation of Pareto sorting. The algorithm also combines the speed calculation method in the gravity search algorithm to enhance the stability of the solution search. Some standard FJSP data benchmarks have been selected to evaluate the effectiveness of the algorithm, and the experimental results confirm the satisfactory performance of the proposed algorithm. Finally, a problem example is designed to demonstrate the algorithm’s capability to generate an excellent scheduling plan.

A numerical simulation and analysis of chalcogenide BaZrS3-based perovskite solar cells utilizing different hole transport materials

Recently, barium zirconium sulfide (BaZrS3) chalcogenide perovskite (CP) material has attracted much attention in the photovoltaic community because of its excellent light harvesting ability, stability, and nontoxicity. BaZrS3 has shown great potential in becoming the best alternative to hybrid halide perovskites. Herein, we have numerically simulated and optimized the performance of a device with the general architecture FTO/WS2/BaZrS3/HTL/Au, using SCAPS-1D (version 3.3.07) software. In this general device, inorganic CuSCN, Cu2O and organic poly(3-hexylthiophene) (P3HT) were inserted, and performance was evaluated as hole transport layer (HTL) materials. Moreover, the influence of various parameters on the device, such as the effect of changing the absorber defect density, varying the operation temperature, and using different metal back contacts, were examined. The best HTL material was organic P3HT, and it exhibited a power conversion efficiency (PCE) of 13.86 %, with a Fill factor (FF) of 11.97 %, open circuit voltage (Voc) of 6.58 V, and current density (Jsc) of 17.59 mA cm−2. The other tested inorganic CuSCN and Cu2O HTL showed a PCE of 13.83 and 13.70 %, respectively. For all the devices, the optimum density of defects was kept at 1.0 × 1015 cm−2. It was established that the P3HT-based device can operate and be stable between 240–––340 K temperature window, while devices with CuSCN and Cu2O can work between 280 – 400 K temperature range. Thus, the CuSCN- and Cu2O-based devices were more stable because they could form a more compact structure that can withstand relatively higher operation temperatures than organic-based devices with P3HT as HTL. Finally, it was established that relatively cheaper metals such as Pt, Ni, and Pd can be suitable alternatives to expensive gold metal back contact. It is expected that this work would shed additional light on the utilisation of BaZrS3 as a promising absorber material in the actual manufacturing of solar cells for clean energy production.

TRADE POLICY, EXCHANGE RATE DYNAMICS AND MANUFACTURING SECTOR OUTPUT IN NIGERIA

Two macroeconomic policies that have impacted on Nigerias manufacturing industry are trade and exchange rate policies. From 1981 to 2022, this study looked at the connection between Nigerias real industrial production, exchange rate, and trade policy. Real manufacturing GDP served as a stand-in for real manufacturing output, and explanatory variables similar to the exchange rate, per capita income, monetary policy rate, manufacturing capacity utilization rate, import to export ratio, and dummy variables for structural adjustment were employed. The model was analyzed in the research by means of autoregressive distributed lag (ARDL) technique. The outcomes showed that while exports as a ratio of gross domestic product (EXGDP). Significantly boosted real manufacturing output (RMO) both in the near and distant future times. Imports as a ratio of gross domestic product (IMGDP) had a negative impact on RMO. Trade liberalization by DSAP had a significant benefit on actual manufacturing production over the long run, while it had a minimally positive impact on RMO during the near term. The rate of exchange has a short-term favorable influence on RMO but a long-term negative one. To increase manufacturing output, the federal government of Nigeria should align its trade policy with the export promotion industrialization plan. KEYWORDS: Trade Policy, Exchange Rate Dynamics, Manufacturing Output, ARDL, Nigeria.

Lithium-Ion Battery Capacity Prediction Method Based on Improved Extreme Learning Machine

Abstract Currently, research and applications in the field of capacity prediction mainly focus on the use and recycling of batteries, encompassing topics such as SOH estimation, RUL prediction, and echelon use. However, there is scant research and application based on capacity prediction in the battery manufacturing process. Measuring capacity in the grading process is an important step in battery production. The traditional capacity acquisition method consumes considerable time and energy. To address the above issues, this study establishes an improved extreme learning machine (ELM) model for predicting battery capacity in the manufacturing process, which can save approximately 45% of energy and time in the grading process. The study involves the extraction of features from the battery charge–discharge curve that can reflect battery capacity performance and subsequent calculation of the grey correlation between these features and capacity. The feature set comprises features with a high correlation with capacity, which are used as inputs for the ELM model. Kernel functions are used to adjust the ELM model, and Bayesian optimization methods are employed to automatically optimize the hyperparameters to improve the capacity prediction performance of the model. The study uses lithium-ion battery data from an actual manufacturing process to test the predictive effect of the model. The mean absolute percentage error of the capacity prediction results is less than 0.2%, and the root-mean-square error is less than 0.3 Ah.

Impacts of Electrode Resistance on Resonance Characteristics of Film Bulk Acoustic Resonators

In the actual manufacturing process of film bulk acoustic resonators (FBAR), metal leads are necessary to complete the electrical topological connection. However, the electrode resistance will significantly influence the characteristics of the resonator. In this paper, the influence of electrode resistance on resonance characteristics of FBAR is analyzed. To be specific, how different electrode resistances affect the quality factor, impedance value, and electromechanical coupling factor of the FBAR series-parallel resonance frequencies is studied by incorporating the electrode resistances into an overall equivalent electromechanical model (i.e., the Mason model) and the COMSOL finite element simulation method. The results show that the increase of electrode resistance will result in the decrease of the series resonant frequency, the impedance value increases significantly and the quality factor decreases significantly at series resonant, while the parallel resonant frequency does not change, the impedance value slightly increases and the quality factor slightly decreases at parallel resonant, and their electromechanical coupling factor slightly increases.

An improved memetic algorithm for multi-objective resource-constrained flexible job shop inverse scheduling problem: An application for machining workshop

Resource-constrained flexible job shop scheduling problems are commonly encountered in some manufacturing industries, and have been widely studied in recent years. However, traditional resource constrained flexible job shop scheduling problem rarely consider the uncertainties in actual manufacturing systems, which may make the original schedule become suboptimal or even unfeasible. Therefore, a resource constrained flexible job shop inverse scheduling problem (RCFJISP) is proposed in this paper, which aims to cope with uncertain events by simultaneously adjusting the machine, worker and process parameters of the original schedule. A multi-objective optimization model is constructed to minimize the makespan, worker cost, machine energy consumption and deviation index. Furthermore, an improved memetic algorithm (IMA) is developed for solving the proposed problem. In IMA, a novel double-layer encoding mechanism is designed to enhance the capacity in exploring new solution’s domains. Three initialization strategies utilizing original scheduling information are designed to improve the quality of initial solutions. An adaptive mutation strategy and a local search mechanism are designed to enhance exploration and exploitation ability of the algorithm. And a crowding operator is proposed to reflect the diversity of the population effectively. In computational experiments, 28 extended benchmarks are constructed, and the effectiveness of the proposed strategy and algorithm is verified by comparing IMA with its 4 variants and other 4 widely used algorithms. Finally, two inverse scheduling problems of a real-world hydraulic cylinder machining workshop under two uncertain situations are studied. The results demonstrate that IMA can effectively solve the actual inverse scheduling problem. With a slight adjustment to the original scheduling, it can reduce the makespan by 11.5%, the worker cost by 8.1% and the machine energy consumption by 27.9% on average.

Auxetic dihedral Escher tessellations

The auxetic structure demonstrates an unconventional deployable mechanism, expanding in transverse directions while being stretched longitudinally (exhibiting a negative Poisson’s ratio). This characteristic offers advantages in diverse fields such as structural engineering, flexible electronics, and medicine. The rotating (semi-)rigid structure, as a typical auxetic structure, has been introduced into the field of computer-aided design because of its well-defined motion patterns. These structures find application as deployable structures in various endeavors aiming to approximate and rapidly fabricate doubly-curved surfaces, thereby mitigating the challenges associated with their production and transportation. Nevertheless, prior designs relying on basic geometric elements primarily concentrate on exploring the inherent nature of the structure and often lack aesthetic appeal. To address this limitation, we propose a novel design and generation method inspired by dihedral Escher tessellations. By introducing a new metric function, we achieve efficient evaluation of shape deployability as well as filtering of tessellations, followed by a two-step deformation and edge-deployability optimization process to ensure compliance with deployability constraints while preserving semantic meanings. Furthermore, we optimize the shape through physical simulation to guarantee deployability in actual manufacturing and control Poisson’s ratio to a certain extent. Our method yields structures that are both semantically meaningful and aesthetically pleasing, showcasing promising potential for auxetic applications.

Streptomycetes as Microbial Cell Factories for the Biotechnological Production of Melanin.

Melanins are complex, polymeric pigments with interesting properties like UV-light absorbance ability, metal ion chelation capacity, antimicrobial action, redox behaviors, and scavenging properties. Based on these characteristics, melanins might be applied in different industrial fields like food packaging, environmental bioremediation, and bioelectronic fields. The actual melanin manufacturing process is not environmentally friendly as it is based on extraction and purification from cuttlefish. Synthetic melanin is available on the market, but it is more expensive than animal-sourced pigment and it requires long chemical procedures. The biotechnological production of microbial melanin, instead, might be a valid alternative. Streptomycetes synthesize melanins as pigments and as extracellular products. In this review, the melanin biotechnological production processes by different Streptomyces strains have been revised according to papers in the literature. The different fermentation strategies to increase melanin production such as the optimization of growth conditions and medium composition or the use of raw sources as growth substrates are here described. Diverse downstream purification processes are also reported as well as all the different analytical methods used to characterize the melanin produced by Streptomyces strains before its application in different fields.

Study of Mechanical Properties, Microstructure, and Residual Stresses of AISI 304/304L Stainless Steel Submerged Arc Weld for Spent Fuel Dry Storage Systems

The confinement boundaries of spent nuclear fuel (SNF) canisters are typically fusion welded. Welded microstructures, strain hardening, and residual stresses combined with a chemically aggressive, chloride-rich environment led to concerns that the welded canister may be susceptible to chloride-induced stress corrosion cracking (CISCC). A comprehensive understanding of the modification of stainless steel (SS) metallurgical and mechanical properties by fusion welding could accelerate the predictive analysis of CISCC susceptibility. This paper describes a submerged arc welding (SAW) procedure that was developed and qualified on 12.7 mm (0.5 in.) thick AISI 304/304L SS to produce joints in a way similar to actual SNF canister manufacturing. This procedure has the potential to reduce the production cost and weld CISCC susceptibility by using fewer welding passes and lower heat input than current industrial applications. Global and local mechanical behaviors and properties, as well as residual stress distributions on the welded joint, were studied. The results indicate that hardness values in the fusion zone (FZ) and heat-affected zone (HAZ) are slightly higher than that of the base metal. Strain localization was presented in the HAZ before the tensile stress reached its maximum value, and then it shifted to the FZ. The specimen finally broke in the FZ. High tensile residual stresses exhibited in the FZ and the nearby HAZ suggest the highest CISCC-susceptible spots. The maximum tensile residual stresses were along the welding direction, indicating that if cracks occur, they would be perpendicular to the welding direction. This study involved developing and qualifying a SAW procedure for SNF canister production. The new procedure yielded cost savings (SAW working efficiency increased by about 80%), improved mechanical properties, and presented moderate residual stresses. Analysis revealed that the welded joint’s low-stress and high-stress damage assessments may be affected by shifts in the strain localization spot under loading.

Large neighborhood local search method with MIP techniques for large-scale machining scheduling with many constraints

This study addresses the problem of scheduling machining operations in a highly automated manufacturing environment while considering the work styles of workers. In actual manufacturing, many aspects of the operation must be considered, such as constraints related to the works to be machined in the machining schedule and the states of the workers. To derive good solutions for such a large-scale problem with many constraints within a realistic amount of computation time, we develop an optimization technique based on a mixed-integer programming (MIP)-based large neighborhood local search method for the machining scheduling problem. Then, computer experiments on a problem based on actual machining requirements are performed to verify the validity of the proposed method.