Workpiece In Process Research Articles

A Production-Logistics prediction method integrating Spatial-Temporal features in flexible production workshop for buffer allocation problem

To meet the demands of personalized manufacturing, characterized by customized production with varying batch sizes, logistics equipment such as Automated Guided Vehicles (AGVs) play a critical role in the manufacturing process. However, the distribution of multiple batches is influenced by various factors, with buffer zone capacity allocation emerging as a key challenge. Optimizing buffer zone allocation necessitates a thorough consideration of both spatial characteristics (e.g., shop floor layout and workpiece pathways) and temporal characteristics (e.g., the sequence of material distribution) to enhance resource allocation, reduce bottlenecks, and improve efficiency. This research proposes a novel logistics prediction method for flexible production plants, utilizing a graph attention network that integrates spatial–temporal features. The method first applies a multi-head attention mechanism to capture significant temporal information. Then, a graph convolutional network is employed to model the workshop layout topology and workpiece processing paths, thereby extracting the spatial features of logistics. This spatial information is sequentially processed through a gated recurrent unit and the multi-head attention mechanism to capture the dynamic temporal features of logistics. The proposed model is ultimately employed to predict production logistics in a flexible manufacturing workshop. The experimental results of the MA-T-GCN (Multi-head Attention Temporal Graph Convolution Network) model on production logistics prediction demonstrate an improvement over the best-performing baseline methods on standard benchmark metrics under varying experimental conditions.

Research on online deformation monitoring of thin-walled parts driven by digital twin

Thin-walled parts are widely used in aerospace. Because thin-walled parts have thin walls, weak stiffness, and are easy to deform and other characteristics, it is easy to produce deformation in the processing, affecting the quality and accuracy of the workpiece processing. Therefore, accurate and efficient online real-time monitoring of the deformation of thin-walled parts has become a vital issue in process control. The visualization, real-time monitoring, and iterative optimization of digital twin technology can effectively solve the problem of online monitoring of thin-walled parts processing. Therefore, this paper proposes a digital twin driven deformation monitoring method for thin-walled parts, which realizes the effective monitoring of deformation during the processing of thin-walled parts. Firstly, the digital twin architecture for online deformation monitoring of thin-walled parts is established, and the critical technologies of the digital twin for online deformation monitoring of thin-walled parts are proposed. Secondly, the Support Vector Regression (SVR) finite element surrogate model is established to realize the rapid simulation of thin-walled parts cutting. At the same time, a One-Dimensional Convolutional Neural Network (1DCNN) combined with the Self-Attention Mechanism (SAM) deformation prediction model was established based on sensor data. The real-time monitoring and accurate prediction of the deformation of thin-walled components are realized through the parallel guidance of the deep learning and surrogate model. Finally, an experimental analysis was carried out by machining thin-walled plates of aluminum alloy 7075, and the fit of the monitoring models was above 95%, thus verifying the validity of the proposed method.

COMPARATIVE STUDY OF DIFFERENT CUTTING FLUIDS ON TOOL-WORK INTERFACE TEMPERATURE DURING TURNING OPERATION ON 6061 ALUMINUM ALLOY

This study investigated the impact of tool-work interface temperature during the turning process of a cylindrical workpiece made of 6061 aluminum alloy. The experiment utilized four different cutting fluids: palm oil-based cutting fluid (POBCF), neem seed oil-based cutting fluid (NOBCF), orange seed oil-based cutting fluid (OSOBCF), and mineral oil-based cutting fluid (MOBCF) using uncoated carbide cutting tool of grade H13A. Various machining parameters were considered, including spindle speed (180, 250, 355, 500 rpm), feed rate (0.105, 0.116, 1.4, 1.6 mm/rev), and depth of cut (0.5, 1.0, 1.5, 2.0 mm). The experimental design followed the Taguchi specified L16 (43) orthogonal array and was conducted on a Lathe Machine XL 400. To measure the tool-work interface temperature, an infrared thermometer (KM 690) was employed during the aluminum alloy machining process. Subsequently, a mathematical model for the tool-work interface temperature values was developed through regression analysis using Minitab 16. The significance of the chosen machining parameters and their respective levels on the tool-work interface temperature was determined using analysis of variance (ANOVA) and F-test. The findings indicated that machining under orange seed oil-based cutting fluid (OSOBCF) conditions resulted in a 9.02%, 16.4%, and 21.7% lower temperature at the tool-work interface compared to palm oil-based cutting fluid (POBCF), neem seed oil-based cutting fluid (NOBCF), and conventional oil-based cutting fluid (MOBCF), respectively. This suggests a potential for producing higher-quality products under orange seed oil cutting fluid conditions compared to other wet conditions.

Cyber-Physical Scheduling System for Multiobjective Scheduling Optimization of a Suspension Chain Workshop Using the Improved Non-Dominated Sorting Genetic Algorithm II

Cyber-Physical Systems (CPSs) offer significant potential to address the evolving demands of industrial development. In the Industry 4.0 era, a framework integrating sensing, data exchange, numerical analysis, and real-time feedback is essential for meeting modern industrial needs. However, implementing this integration presents challenges across multiple domains, particularly in digital analysis, information sensing, and data exchange during corporate transformation. Companies yet to undergo transformation face distinct challenges, including the risks and trial-and-error costs of adopting new technologies. This study focuses on a heavy-duty workpiece processing factory, with a specific emphasis on the painting process. The complexity of this process frequently results in congestion, which is approached as a multi-objective, multi-constraint optimization problem. This paper proposes the Improved Non-dominated Sorting Genetic Algorithm II (INSGA-II) to address the requirements of multi-objective optimization. The proposed approach uses multi-chromosome structures, listeners, and recursive backtracking initialization to optimize the search for solutions under constraints. This enables the factory to automatically streamline production lines based on workpiece processing sequences, leading to increased efficiency. Additionally, a CPS framework focused on simulation modeling has been designed. First, the INSGA-II algorithm processes order data to generate production schedules. Next, the data transmission formats and input-output interfaces are designed. Then, a simulation model is built using the algorithm’s results. These components collectively form the CPS framework for this study. The proposed method offers an automated digital solution through the algorithm, enabling verification of its feasibility via the simulation model. As a result, it significantly enhances decision-making speed, reliability, and equipment utilization.

Cyclic biomachining of copper: Maximum metal removal rate with minimum depleted solution

Bacterially assisted leaching has been proposed for many innovative applications, such as urban mining or micromachining, but many technical challenges including substrate toxicity and downstream processing of the leach liquor must be solved before its industrial implementation. This study was conducted with the final goal of improving the average specific metal removal rate (SMRR) and reducing the amount of depleted solution generated in the leaching process of copper workpieces, with special attention paid to the detection of microbial activity inhibition.The copper leaching rate is almost entirely dependent upon the Fe3+ concentration with Fe3+/Fe2+ ratios higher than 0.3, after which A. ferrooxidans oxidation action becomes relevant. In both biotic and abiotic experiments, the SMRR peaked after the first hour of treatment, and it decreased sharply after three hours. A consecutive process alternating bioleaching and oxidant regeneration stages was proposed to improve performance, which considerably reduced both the time needed to dissolve a certain amount of metal and waste generation. The decrease in copper removal (25 %) and the increase in the time required for oxidant regeneration after five leaching cycles (longer than 90 h) were attributed to bacterial inhibition in the presence of 10.7 g Cu2+ L−1, which was established as the limiting metal concentration for reusing the biologically active solution.

Comprehensive characterization of spark plasma sintered ZrB2-SiCp-SiCw composite and its corresponding orthogonal cutting simulation

A composite consisting of 12.5 wt% SiC particles and 12.5 wt% SiC whiskers in a ZrB2 matrix (ZrB2-SiCp-SiCw) was produced using spark plasma sintering. The sintering process was carried out at a temperature of 1900 °C and a pressure of 40 MPa for a duration of 7 minutes. X-ray photoelectron spectroscopy analysis confirmed the presence of C-C, C-Si, O-B, O-Zr, O-Si, and Zr-B bonds. Scanning transmission electron microscopy and energy-dispersive X-ray spectroscopy analyses revealed the presence of silicon and carbon in the ZrB2 phase. The nano-indentation hardnesses and elastic moduli of ZrB2 and SiC were measured as 2104.58 and 2268.97 Vickers and 397.58 and 394.27 GPa, respectively, under a load of 100 mN and a loading rate of 0.4 mN/sec. The orthogonal cutting process of an Al6061 workpiece with a ZrB2-SiCp-SiCw cutting tool was simulated using DEFORM 2D software. The response surface method was used to develop a model to evaluate the relationship between cutting parameters (tool tip radius, cutting speed, and rake angle), cutting force, feeding force, and maximum temperature. It was found that cutting and feeding forces decrease with increasing cutting speed and rake angle, while increasing the tool tip radius increases these forces. The tool and workpiece endure a higher maximum temperature as the cutting speed and tool tip radius rise, but the maximum temperature drops as the rake angle increases.

On some methods of determining proper frequencies torsional oscillations in metal-working machines

Forced torsional vibrations that occur in machine tool drives are one of the pressing problems of modern machine tool industry. The reasons for these fluctuations may be imbalances of rotating masses, structural imperfections of bearing units, operating or technological process features (for metal-cutting machines – eccentric clamping of the workpiece, processing of non-round workpieces, etc.). In this work, using the example of a real metal-cutting machine, the task of dynamic analysis of the drive of the main movement of the machine with determination of the natural frequencies of torsional vibrations is realized. The real object is replaced by a dynamic model (DM). When constructing the DM, the inertial and rigidity characteristics are determined from the conditions: – the kinetic energy of the object is equivalent to the kinetic energy of the DM (from this condition the axial moments of inertia of the concentrated masses of the row model are obtained); – the potential energy of elastic deformations of the object is equivalent to the potential energy of deformation of the DM (from this condition the rigidity coefficients of the DM are obtained). The natural frequencies of the row DM were determined in three ways: 1) by solving the frequency equation (exact method) in combination with the method of reducing the number of masses of A.P. Cherevkov; 2) partial systems method (fundamental frequency only); 3) method of residuals (Tolle). The following conclusions were made: 1) the main (smaller) frequency can be found by any of the methods considered (since the results of the three methods coincided); 2) when simplifying the DM using the method of partial systems, the criterion for the possibility of further simplifications over the specified frequency range should be taken into account; the transition from a three-mass DM to a two-mass one if the criterion in this calculation was not met led to an error in determining the value of the fundamental frequency of about 10%; 3) dynamic calculations of machines using DM and methods for determining natural frequencies make it possible, based on known frequencies of disturbing forces, to identify possible near-resonant operating modes and avoid them in advance by making changes to the design of the machine at the design stage

Precision prediction in grinding processes based on displacement signal conversion into recurrent plot

The processing of grinding data and the prediction of accuracy are extremely complex; this paper proposes a novel prediction method to ensure the machining accuracy of computer numerical control (CNC) grinding machines. It consists of two components, namely the filter decomposition recurrence plot (RP) transformation (FDRP) and the deep inverted residual attention network (DIRAN). A pipeline named FDRP has been designed to address the issues in the processing of data and the shortcomings of RP. Firstly, the displacement signals undergo filtering to preserve essential grinding information while effectively removing noise. Secondly, long-time series signals are decomposed and augmented based on the characteristics of the machining process. Lastly, an RP transformation is applied to one-dimensional time series data, resulting in the generation of images that accurately represent the grinding process. Furthermore, this paper proposes a novel machining accuracy prediction model. The DIRAN uses a multi-layer inverse residual network structure combined with attention mechanism to extract the features of two-dimensional RP, and its performance is better than other typical prediction methods. It can be applied to predict the polar angle of the workpiece in industrial processing and reduce the defect rate.

Surface characteristics of precision as-cut NdFeB magnet considering diamond wire wear

NdFeB is the highest output value permanent magnet material at present, which has extremely high magnetic properties and is widely used. In the production process of NdFeB, cutting is an indispensable process, and high precision as-cut surface characteristics can reduce the cost of subsequent processes. In this paper, the single factor sawing experiment of NdFeB was carried out by using the range of processing parameters in industrial production. The effects of workpiece feed speed, saw wire speed, workpiece processing size and diamond saw wire wear on sawing surface morphology, surface material brittleness removal ratio, roughness and waviness were studied. The research shows that the surface characteristics of the workpiece sawed in the stable wear stage of the diamond saw wire are the best. In all service stages of the saw wire, reducing the feed speed and workpiece processing size, and increasing the wire speed will reduce the surface roughness and waviness, reduce the number and size of pits, and reduce the proportion of brittle removal areas. The research results provide an experimental reference for the optimization of diamond wire saw cutting parameters of NdFeB.

ИССЛЕДОВАНИЕ ВЛИЯНИЯ СОСТАВА КОМПОЗИТОВ, СОДЕРЖАЩИХ ИМПАКТНЫЕ АЛМАЗЫ, НА ИХ ЭКСПЛУАТАЦИОННЫЕ ХАРАКТЕРИСТИКИ ПРИ ЛЕЗВИЙНОЙ ОБРАБОТКЕ ЦВЕТНЫХ МЕТАЛЛОВ

The paper presents the results of resistance tests of tool composite materials containing nanostructured impact diamonds, in comparison with composites based on synthetic superhard materials, diamond and cubic boron nitride (cBN), and ВК-8 (VK-8) (WC + 8 % Co) hard alloy, during blade processing of workpieces made of brass ЛС-59 (LS -59) (57–60 % Cu) and aluminum alloy AK-7 (Al 92–94 %, Si 6–8 %). Samples of composite materials for wear resistance studies were obtained by thermobaric sintering in a highpressure apparatus. The performance of composites was assessed by the wear of the cutter back surface, as well as by a specially developed method for studying the performance characteristics of composite superhard materials, based on determining their specific productivity. As a result of the tests, it is found that composites based on impact diamonds have the highest resistance when turning non-ferrous metal alloys. In particular, the wear resistance of a composite material containing impact diamonds with the addition of cBN exceeds the wear resistance of cutters made of composite 02 (belbor) by 1.5–3 times, and that of cutters made of hard alloy VK-8 by 8–10 times. Additions of impact diamond to hard alloy in the amount of 6.25 and 12.5 vol.% increase the relative wear resistance of the hard alloy composite compared to the original VK-8 alloy by 10–15 and 20–25 %, respectively.

Discrimination of wear performance based on surface roughness parameters arithmetic mean height (Sa) and skewness (Ssk)

Enhancing interfacial contact bearing performance typically involves minimizing the roughness parameter Sa (arithmetic mean height). Yet, exceedingly low Sa incur steep increases in processing costs, indicating that Sa-centric optimization strategies may not be universally suitable for interface performance control. Roughness parameters skewness (Ssk) and kurtosis (Sku) exert a pronounced influence on component contact wear performance. Accordingly, investigating the correlation between Ssk, Sku, and wear performance holds significant promise for enhancing wear resistance. A finite element wear calculation model for rough-surface abrasive wear employing elastic-plastic contact mechanics and the Jump-in-Cycle method. Cylindrical roller dry sliding experiments validate the model's reliability. Integrating this model, a method is proposed for wear performance discrimination based on Sa and Ssk at different loads. It enables the identification of critical roughness parameter values for improved surface wear resistance. The calculated wear model aligns with experiment trends, with predicted surface textures mirroring observed wear surface characteristics. Critical Sa and Ssk diverge under distinct loads. The study underscores the importance of moving beyond the exclusive pursuit of exceptionally low Sa and Ssk. It provides a foundation for reducing workpiece processing costs while optimizing wear performance.

Finishing and hardening processing of cycloidal helical surfaces by surface plastic deformation with programmed control of the tool

A mathematical model is developed that makes it possible to prepare a tool control program for finishing and hardening processing of a complex-profile workpiece with a cycloidal helical surface by plastic surface deformation. Keywords:surface plastic deformation, cycloidal helical surface, microhardness, rolling, software control of the tool ilya32zog@gmail.com, aka111996@mail.ru, maxim.albom@yandex.ru, al.goncharow@yandex.ru

Processing of a Part Such as a Composite Bearing Housing on a Milling Machine With a Digital Control Program

In the era of the industrial revolution of ndustry 4.0, the Republic of Kazakhstan is rapidly introducing automated robots and digital control programs in many of its production facilities. In this context, special attention is paid to modern technologies, and AKIRA SEIKI milling machines with FANUC control software stand out as advanced technologies in this process. They are becoming the most popular in the CIS countries due to the economic efficiency and intellectual support of the Taiwanese manufacturer. Practical aspects of constructing CNC milling machines and their practical use are presented in this article. Particular attention is paid to the functionality of milling machines using the example of processing such a part as a composite bearing housing. In addition, the process of writing processing operation codes in the CIMKO computer program is described. The code is provided with brief descriptions to help you understand the compiled workpiece processing operations. With the growing need for efficiency and precision in production, many companies and industrial plants in the Republic of Kazakhstan are considering AKIRA SEIKI milling machines with FANUC control software as a major investment. These machines not only provide high productivity, but also help automate complex technological processes. CIMKO, among other things, allows the development and optimization of machining code, which complements the efficiency and accuracy of the machines.

Energy-saving strategy and method of spindle deceleration during no-load operation of machine tools for energy lean management

Computer Numerical Control (CNC) machine tools undergo periods of no-load motion, such as tool feeding, tool withdrawing, and tool changing, during workpiece processing. These intervals result in energy wastage as the machine tools remain idle without performing cutting operations. To address this issue, we propose a novel energy-saving strategy and method involving spindle deceleration during the no-load operation of machine tools for energy lean management. Specifically, we conducted three tasks: (1) analyzed the energy consumption characteristics of the CNC machine tool during no-load operation; (2) established a theoretical decision-making model for the energy-saving method of spindle deceleration; (3) discussed the energy-saving critical time for energy savings and the energy-saving effect of the deceleration strategy for the machine tool spindle through three machining cases. Furthermore, we conducted a case study using the CK6153i lathe to verify the effectiveness and feasibility of the proposed method. The results demonstrated significant energy savings of 1826.65 J, 2948.85 J, and 7650.05 J, along with reductions in energy waste of 14.99 %, 20.43 %, and 53.01 %, respectively, in the three machining cases through the implementation of the spindle deceleration strategy. We believe that the outcomes of this research can better assist engineers in the energy lean management of machine tools.

Multi-View consistency-based point cloud registration method with low overlap rate

Accurate and efficient workpiece measurement is crucial for workpiece processing and quality monitoring. Non-contact optical measurement methods have gained more attention due to their simplicity, efficiency, and flexibility compared to complicated and inefficient contact measurement methods. Multi-view registration of measurement data is a key issue in workpiece measurement, as it relies on the system’s geometric accuracy and motion stability, presenting challenges such as the insufficient overlap of multi-viewpoint cloud data and cumulative error. To address these challenges, this paper proposes a multi-view planning and registration algorithm with a low overlap rate. The multi-view planning algorithm employs a greedy method to plan the scanning viewpoints of the workpiece to obtain complete point cloud data efficiently. The multi-view registration algorithm extracts features using a multi-scale geometric feature extraction network, matches the features based on the Hungarian algorithm, builds a graph, and optimizes workpiece positions based on the G2O algorithm for multi-view registration, effectively reducing cumulative error. Measurement experiments on blade workpieces confirm the feasibility of the proposed algorithms.

A combined CFD-based simulation and experimental study of particle dispersion in a high-concentration airtight space under unorganized airflow

It is significant to obtain the particle distribution data for workpiece processing and industrial protection strategies. To simulate the particle dispersion in a high-concentration airtight space under unorganized airflow, a CFD-based simulation and experimental were utilized in this study. The proposed method works in three steps. The first step is to use Stokes-MRF-Fan-CFD model to simplify the distribution of high-concentration particles calculation to the dispersion state of the gas components. The second step is to compute the airflow patterns and dispersion of particles. The third step is to prove the feasibility of Stokes-MRF-Fan-CFD model. The results of this model in particle dispersion show ±200 mg·m−3 of absolute error, ±20% of relative error, and 88.5747 grade of simulation accuracy. And the results of particle concentration distribution can be mutually confirmed with the streamline and velocity vector distribution in the airtight space, providing a promising and innovative model for the particle dispersion in a high-concentration airtight space under unorganized airflow.

Исследование влияния элементов режима и шага зубьев фрезы на технологические параметры и температурное поле процесса обработки заготовок тонкостенных деталей

When machining workpieces of thin-walled parts, the temperature field differs from the field, which is formed during massive workpieces treatment. The reason for this is that machining of a thin-walled billet is interfered with its surface, being opposite to the one under machining. It has a significant effect on the temperature field, since the intensity of heat removal from this surface into the environment is significantly less than heat removal into the underlying layers of a massive material blank. In this regard, the problem of finding a rational mode of the machining process of thin-walled workpieces is relevant. The aim of the study is to determine the effects of the elements of the milling mode of thin-walled workpieces and milling cutter teeth space on the technological parameters of the milling process of titanium alloy workpieces and to develop recommendations for choosing this milling mode. For this purpose, a numerical modeling of the technological parameters of the milling process of blanks of massive workpieces and thin-walled parts made of titanium alloy, was performed experiencing various combinations of feed to the milling cutter tooth, cutting speed and milling cutter teeth space. When machining a thin-walled workpiece, due to less intense heat removal from the cutting zone into the workpiece, the temperatures in the areas of chip contact with the front surface of the tooth, the back surface of the tooth with the workpiece and the temperature of the workpiece itself are higher than when in a massive workpiece treatment. The patterns of changes in the parameters of the milling process of thin-walled workpieces depending on the feed, cutting speed and the milling cutter teeth space are established. With a larger tool stepover, the average and maximum temperatures in the areas of chip contact with the front surface of the tooth and the back surface of the tooth with the workpiece are lower in most of the combinations of mode elements used. Equations that establish the effect of feed on the milling cutter tooth, cutting speed and spacing of teeth on the parameters of the machining are obtained. The results of the study will allow choosing a rational milling mode and spacing of teeth in the work on workpieces of thin-walled parts made of titanium alloy.

Study on Plastic Deformation Removal Mechanism and Dislocation Change in Nanogrinding of Single Crystal Silicon Carbide with Random Rough Surface

This investigation centers on exploring the plastic deformation removal mechanism and dislocation dynamics in the nanogrinding of single‐crystal silicon carbide featuring a randomly rough surface. To simulate this, an approach combining the Weierstrass–Mandelbrot fractal surface function with molecular dynamics is employed. Randomly rough surface contours are generated using the Weierstrass–Mandelbrot fractal surface function. By adjusting the fractal dimension, an ideal representation of single‐crystal silicon carbide with randomly rough surfaces is obtained. By combining plastic deformation detection methods, the process of workpiece sliding, plowing, and chip removal is studied, and the plastic deformation removal mechanism of random rough surface grinding is explored. Combining postprocessing methods such as sheer strain and dislocation trajectory, the morphological changes and transformation mechanisms of dislocations are analyzed. Notably, at grinding depth of 33.18 nm, the activation of slip systems induces dislocation formation, enabling plastic deformation. Furthermore, at depths exceeding 144.00‐nm, the emergence of stacking faults on the random rough surface is observed. Throughout the grinding procedure, plastic deformation of debris occurs, leading to the formation of plastic bulges on both sides of the tool, and the debris generated during processing does not impact the defects encountered during secondary grinding of the rough surface.

Calculation of force parameters of workpiece machining process with end mill cutters

The aim is to develop and validate an operational methodology for calculating the force parameters and characteristics of the tool and the process of milling structural materials with end milling cutters. The structural schemes of machining and force models of oblique cutting processes in the modes of axial tool feed and continuous plastic deformation of the processed material were used when developing the method of preliminary calculation of the total axial force working on the cutting edge of end milling cutters. The rotating tool tests were conducted on a Hermle UWF 1202 H 3-axis machining center supplemented with a Kistler piezoelectric dynamometer (model 9272). Authors suggested, developed and tested the preliminary calculation method applied to the force characteristics of the machining process of workpieces by end milling cutters, considering how the energy power of ductile fracture of the machined material affects the process. Contact friction arising on the front and rear surfaces of the cutting tool does not reach the limiting value being subject to the Coulomb – Amonton law, that is, it is estimated by the dependence directly proportional to the normal pressure. After calculations, we defined the materials of the workpiece for milling, that is 45 steel (AISI 1045), and the end two-tooth cutter, uncoated T14K8 alloy, which was used to produce samples. The following milling modes were established: 4 mm boring depth; 50, 100 and 150 m/min cutting speeds; 0.05 and 0.1 mm/rev cutting tool feed. Deviation of the measured values of axial cutting force from the calculated values in the range of changing values of tool feed rate was found to be no more than 11%, and in the range of changing values of cutting speed no more than 15%. The developed calculation and analytical methodology for estimating force parameters of the machining process by end milling cutters provides an increase in the efficiency and reliability of the preliminary prognostic calculation of operating parameters and characteristics of cutting elements of end milling cutters.

Research on sustainable collaborative scheduling problem of multi-stage mixed flow shop for crankshaft components

The crankshaft manufacturing process primarily comprises machining, single jacket, and double jacket stages. These stages collectively produce substantial carbon emissions, which significantly impact the environment. Low-carbon energy development and humanity's future are closely related. To promote the sustainable development of crankshaft manufacturing enterprises and improve the production efficiency of crankshafts, research on sustainable collaborative scheduling problems in multi-stage mixed flow shop for crankshaft components is conducted. In addition, the transportation process of related workpieces in the crankshaft manufacturing process, which generally have a large mass, also produces substantial carbon emissions. This paper constructs a multi-objective integer optimization model based on the manufacturing process characteristics of crankshaft components, with minimizing the maximum manufacturing time and carbon emissions as optimization objectives. Considering the complexity of the problem, a comprehensive algorithm integrating moth-flame optimization and NSGA-III is used to solve the mathematical model. Through case experiments, the integrated algorithm is compared and analysed with four classic multi-objective optimization algorithms: NSGA-III, NSGA-II, MOEA/D, and MOPSO. The experiments demonstrate that the algorithm presented in this paper offers significantly enhanced optimization efficiency in solving the problem under study compared to other algorithms. Moreover, this paper compares multi-stage collaborative scheduling and non-collaborative scheduling in the crankshaft manufacturing process, ultimately demonstrating that collaborative scheduling is more conducive to the sustainable development of manufacturing enterprises. The results indicate that the annual carbon emissions can reduce about 3.6 ton.