Template-Type: ReDIF-Article 1.0
Author-Name: Liyuan Zhang
Author-X-Name-First: Liyuan
Author-X-Name-Last: Zhang
Author-Name: Yong Yang
Author-X-Name-First: Yong
Author-X-Name-Last: Yang
Author-Name: Xuefeng Wang
Author-X-Name-First: Xuefeng
Author-X-Name-Last: Wang
Title: Study on intelligent control method of pharmaceutical production quality based on chromatographic fingerprint
Abstract:
Aiming at the problems of low control accuracy and long control time in traditional intelligent control methods of drug production quality, an intelligent control method of drug production quality based on chromatographic fingerprint was proposed. Extract the chromatographic fingerprint of drug production quality, and use wavelet transform to de-noise the obtained fingerprint. According to the preprocessing results, use the real-time mean method to calculate the mean value of the real-time converted chromatographic fingerprint data. Through the tolerance zone of the fingerprint, convert the drug chromatographic fingerprint data. Based on the data conversion results, grade the drug production quality through fuzzy classification. The interval hesitation fuzzy control chart is constructed to carry out intelligent control of drug production quality. The experimental results show that the proposed method has higher precision and shorter control time for drug production quality intelligent control.
Journal: Int. J. of Manufacturing Technology and Management
Pages: 152-167
Issue: 1/2
Volume: 39
Year: 2025
Keywords: chromatographic fingerprint; drug production quality; intelligent control; real-time mean method; fuzzy control chart.
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Template-Type: ReDIF-Article 1.0
Author-Name: Weilin Zeng
Author-X-Name-First: Weilin
Author-X-Name-Last: Zeng
Author-Name: Weizhao Guo
Author-X-Name-First: Weizhao
Author-X-Name-Last: Guo
Author-Name: Jiang Qiu
Author-X-Name-First: Jiang
Author-X-Name-Last: Qiu
Author-Name: Hong Wen
Author-X-Name-First: Hong
Author-X-Name-Last: Wen
Title: A method for surface wear detection of machined parts based on image processing
Abstract:
In order to reduce the error in the surface wear detection of machined parts, improve the detection accuracy of the failure degree of parts and shorten the detection time, a method of surface wear detection of machined parts based on image processing is designed. Firstly, the wear mechanism is analysed and the mathematical model of wear law is established. Then, the relationship model of surface wear of machined parts is analysed to determine the range of wear data to be detected. Finally, three components are used to determine the grey level image of part surface wear, calculate the pixel value of the maximum grey level image, and then binary processing is carried out. The noise is removed by means of the mean filtering algorithm. Finally, the wear detection of machined parts is realised by calculating the weighted mean. The results show that the wear detection error of the proposed method is low and has credibility.
Journal: Int. J. of Manufacturing Technology and Management
Pages: 137-151
Issue: 1/2
Volume: 39
Year: 2025
Keywords: image processing; machined parts; surface wear; limit value; mean filtering; binarisation.
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Handle: RePEc:ids:ijmtma:v:39:y:2025:i:1/2:p:137-151

Template-Type: ReDIF-Article 1.0
Author-Name: Lingmin Yang
Author-X-Name-First: Lingmin
Author-X-Name-Last: Yang
Title: Multi-product supply chain scheduling method based on hybrid genetic algorithm
Abstract:
In order to solve the shortcomings of traditional methods such as high Hamming loss value and low demand supply rate, a multi-product supply chain scheduling method based on hybrid genetic algorithm was designed. First, build supply time series, and sort the priority of supply chain scheduling according to the priority attribute values of backup plans in each link of the supply chain closed-loop model. Then, taking the shortest scheduling time as the objective function and the location constraints of the revolving warehouse as the constraints, the scheduling results are obtained by combining the genetic algorithm. In order to avoid the genetic algorithm falling into the local optimum, the simulated annealing algorithm is introduced to solve the global optimal solution of the objective function to achieve the coordinated scheduling of the supply chain. The experimental results show that this method can achieve multi-product supply chain scheduling more reasonably and effectively.
Journal: Int. J. of Manufacturing Technology and Management
Pages: 122-136
Issue: 1/2
Volume: 39
Year: 2025
Keywords: genetic algorithm; simulated annealing algorithm; multi-product supply chain; supply chain scheduling; dispatch time; positioning of turnover warehouse.
File-URL: http://www.inderscience.com/link.php?id=144117
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Template-Type: ReDIF-Article 1.0
Author-Name: Kui Wang
Author-X-Name-First: Kui
Author-X-Name-Last: Wang
Author-Name: Fangfang Zhang
Author-X-Name-First: Fangfang
Author-X-Name-Last: Zhang
Author-Name: Jizhi Wang
Author-X-Name-First: Jizhi
Author-X-Name-Last: Wang
Author-Name: Hongmei Zhao
Author-X-Name-First: Hongmei
Author-X-Name-Last: Zhao
Title: Research on the integrated logistics supply chain management model of foreign trade processing products
Abstract:
There are some problems in the integrated logistics supply chain management model of foreign trade processing products, such as high management risk coefficient, large model effectiveness evaluation error, and low supply chain evaluation accuracy. Therefore, this paper designs a method to evaluate the integrated logistics supply chain management mode of foreign trade processing products. Firstly, the characteristics of integrated logistics supply chain management mode for different foreign trade processing products are determined. Then, with the help of the analytic hierarchy process to determine the management model evaluation indicators and weights. Finally, determine the maximum demand of each node in the integrated logistics supply chain, build the model effectiveness evaluation model, and realise the evaluation of the supply chain management model. Experiments show that the proposed method can effectively reduce the risk coefficient and improve the accuracy of the model effectiveness evaluation.
Journal: Int. J. of Manufacturing Technology and Management
Pages: 105-121
Issue: 1/2
Volume: 39
Year: 2025
Keywords: foreign trade processing products; integrated supply chain; management mode; risk coefficient; analytic hierarchy process; judgement matrix.
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Handle: RePEc:ids:ijmtma:v:39:y:2025:i:1/2:p:105-121

Template-Type: ReDIF-Article 1.0
Author-Name: Fangwei Pan
Author-X-Name-First: Fangwei
Author-X-Name-Last: Pan
Title: Research on welding path modification of welding industrial robot based on leapfrog algorithm
Abstract:
Aiming at the problems of industrial robot welding path correction such as large error, low search accuracy and long correction time, a welding path correction method based on leapfrog algorithm is proposed. Firstly, the key components of the industrial welding robot system and its interaction are determined, and its control system and operation principle are analysed. Then calculate the total resistance and voltage change value of spot welding industrial robot, realise the change of position relationship in its running path by means of homogeneous transformation method, and determine the change of technical parameters of the robot under different rotation angles. Finally, the optimal path is searched globally, and the welding path of the welding industrial robot is modified by the idea of differential evolution. The results show that the proposed method can reduce the welding path correction error, and the correction time is short.
Journal: Int. J. of Manufacturing Technology and Management
Pages: 89-104
Issue: 1/2
Volume: 39
Year: 2025
Keywords: leapfrog algorithm; welding industrial robot; welding path; correction method; homogeneous transformation; global search.
File-URL: http://www.inderscience.com/link.php?id=144119
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Handle: RePEc:ids:ijmtma:v:39:y:2025:i:1/2:p:89-104

Template-Type: ReDIF-Article 1.0
Author-Name: Mi Wang
Author-X-Name-First: Mi
Author-X-Name-Last: Wang
Title: Study on cost benefit estimation of enterprise environmental management based on multiple linear regression
Abstract:
There are some problems in the cost benefit estimation of enterprise environmental management, such as poor precision of estimation results, low correlation of estimation indexes and long estimation time. Therefore, a cost benefit estimation method of enterprise environmental management based on multiple linear regression is proposed. First, the enterprise environmental management cost indicator system is constructed, and then the similarity between the indicator data is determined by using association rules and support calculation, and then combined and quantified. Finally, quantitatively describe the indicator data to determine that there is no correlation between them, remove the interference items in the indicator data, build an evaluation model with multiple linear regression, and verify the goodness of fit of the results through the sum of squares decomposition formula to complete the benefit estimation. The results show that the proposed method improves the accuracy of the estimation results, and the estimation speed is fast.
Journal: Int. J. of Manufacturing Technology and Management
Pages: 74-88
Issue: 1/2
Volume: 39
Year: 2025
Keywords: multiple linear regression; enterprise environment; administration cost; benefit estimation; similarity; quantitative description; sum of squares decomposition.
File-URL: http://www.inderscience.com/link.php?id=144120
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Template-Type: ReDIF-Article 1.0
Author-Name: YuXi Zhang
Author-X-Name-First: YuXi
Author-X-Name-Last: Zhang
Title: Service quality evaluation of agricultural cold chain logistics supply chain based on k-means clustering algorithm
Abstract:
In order to improve the reliability of the cold chain logistics supply chain and shorten the response time of the supply chain, a quality evaluation method of agricultural cold chain logistics supply chain based on k-means clustering algorithm was proposed. Firstly, build the quality evaluation system of agricultural cold chain logistics supply chain. Secondly, the root method is selected to check the consistency of the judgment matrix, and the weight vector of the logistics supply chain quality evaluation is calculated. Finally, the k-means clustering algorithm is used to evaluate the supply chain service quality. The experimental results show that the supply chain quality reliability of this method is 0.98, and the service response time is only six minutes; the service satisfaction rate can reach 99.6%.
Journal: Int. J. of Manufacturing Technology and Management
Pages: 59-73
Issue: 1/2
Volume: 39
Year: 2025
Keywords: k-means clustering algorithm; weight vector; analytic hierarchy process; agriculture products; cold chain logistics; service quality assessment.
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Template-Type: ReDIF-Article 1.0
Author-Name: Yi Zhou
Author-X-Name-First: Yi
Author-X-Name-Last: Zhou
Title: Risk assessment method of human resources outsourcing based on risk matrix
Abstract:
In order to reduce the error of risk assessment and improve the correlation of risk indicators, a risk assessment method of human resource outsourcing based on the risk matrix method is proposed. First, determine the risk indicators of human resource outsourcing, use the rough set theory to determine the indiscernibility of the evaluation indicators, and use F-score to calculate the risk evaluation indicators and their information gain. Then, build a risk matrix coordinate chart to identify outsourcing risk factors, calculate the probability of outsourcing risk occurrence using the conditional random field joint probability formula, and build a risk assessment model through the risk matrix method to complete the risk assessment. The experimental results show that the evaluation error of the proposed method is always less than 2%, and the maximum correlation coefficient is close to 1, which verifies the effectiveness of the method.
Journal: Int. J. of Manufacturing Technology and Management
Pages: 44-58
Issue: 1/2
Volume: 39
Year: 2025
Keywords: risk matrix method; human resources; outsourcing risk; f-score; information gain; joint probability of conditional random fields.
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Template-Type: ReDIF-Article 1.0
Author-Name: Fei Li
Author-X-Name-First: Fei
Author-X-Name-Last: Li
Author-Name: Tuo Xin
Author-X-Name-First: Tuo
Author-X-Name-Last: Xin
Title: Real-time location method of electric power material storage based on RFID technology
Abstract:
In order to overcome the problems of time-consuming and low positioning accuracy of traditional warehousing positioning methods, a real-time positioning method for electric power materials warehousing based on RFID technology was proposed. First, the weight centre between the electrical materials to be measured is determined by the trilateral measurement method to complete the distance measurement of the stored electrical materials. Secondly, based on the measured distance, calculate the weight proportion of the electrical material storage location, and set the electrical material storage label with the help of Gaussian filter. Finally, RFID technology is introduced to analyse the collision probability of electronic labels of warehousing materials. With the help of time stamp calculation, the collision of electronic labels is avoided and the warehousing position of materials is completed. The experimental results show that the positioning accuracy of this method is high, and the highest positioning accuracy is 92%.
Journal: Int. J. of Manufacturing Technology and Management
Pages: 31-43
Issue: 1/2
Volume: 39
Year: 2025
Keywords: RFID technology; power material storage; real-time positioning; Gaussian filter; electronic label.
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Template-Type: ReDIF-Article 1.0
Author-Name: Gai Song
Author-X-Name-First: Gai
Author-X-Name-Last: Song
Title: Digital packaging design method of intelligent products based on internet of things technology
Abstract:
Aiming at the problems of large feature extraction error of packaging image and poor packaging image design effect in the process of digital packaging design of intelligent products at this stage, this paper studies the digital packaging design method of intelligent products combined with internet of things technology. First, build a packaging data collection platform based on internet of things technology. Then, the digital packaging features of intelligent products are extracted by Harris corner feature method, combined with the digital filter to pre-treatment the packaging characteristics. Finally, the automatic packaging generation model is constructed by regularisation algorithm, and the model is optimised by maximising information entropy to realise the digital packaging design of intelligent products. The experimental results show that the minimum feature extraction error of the proposed method is 0.2%, and the maximum packaging colour enhancement is 95%, which can effectively reduce the extraction error and have a good digital packaging generation effect.
Journal: Int. J. of Manufacturing Technology and Management
Pages: 19-30
Issue: 1/2
Volume: 39
Year: 2025
Keywords: internet of things technology; intelligent products; digital packaging; move window; Harris corner feature.
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Template-Type: ReDIF-Article 1.0
Author-Name: Pei Li
Author-X-Name-First: Pei
Author-X-Name-Last: Li
Author-Name: Jing Yuan
Author-X-Name-First: Jing
Author-X-Name-Last: Yuan
Author-Name: Yawen Hong
Author-X-Name-First: Yawen
Author-X-Name-Last: Hong
Author-Name: Yashe Lei
Author-X-Name-First: Yashe
Author-X-Name-Last: Lei
Title: Design of real-time monitoring method for production line equipment status based on cloud computing and internet of things technology
Abstract:
In order to solve the problems of low monitoring accuracy and long monitoring time in the traditional real-time monitoring method of production line equipment status, this paper designs the real-time monitoring method of production line equipment status based on cloud computing and internet of things technology. Based on the perception of the internet of things labels, collect production line equipment status data and real-time upload, extract time domain parameters of production line equipment status, build historical memory matrix, establish and train multiple state estimation model, design distributed processing cloud computing platform MapReduce framework, complete real-time monitoring of production line equipment status under this framework. The experimental results show that the real-time monitoring accuracy of production line equipment status by the proposed method is 98%, the monitoring time is within 7.25 s, and it has the application effect of high precision and low time consumption.
Journal: Int. J. of Manufacturing Technology and Management
Pages: 168-181
Issue: 1/2
Volume: 39
Year: 2025
Keywords: cloud computing; internet of things technology; production line equipment; condition monitoring; radio frequency identification; RFID.
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Template-Type: ReDIF-Article 1.0
Author-Name: Xiaoying Bai
Author-X-Name-First: Xiaoying
Author-X-Name-Last: Bai
Author-Name: Yulong Wan
Author-X-Name-First: Yulong
Author-X-Name-Last: Wan
Title: Analysis of the factors influencing the sustainable development of the manufacturing industry under the wave of Industry 4.0
Abstract:
As a pillar industry of the national economy, whether the manufacturing industry can adapt to the environment of Industry 4.0 era and get sustainable development in this wave is a key issue in the manufacturing industry. Through analysis of the background of the Industry 4.0 wave, research on the development status and problems of manufacturing industry, and analysis of the influencing factors of the sustainable development of the manufacturing industry, this paper makes clear the impact of environmental factors, investment factors and resource factors on the sustainable development of the manufacturing industry under the Industry 4.0 wave. On the basis of the research results, we should formulate reasonable countermeasures for the sustainable development of the manufacturing industry to promote the development of the manufacturing industry in the new era.
Journal: Int. J. of Manufacturing Technology and Management
Pages: 1-18
Issue: 1/2
Volume: 39
Year: 2025
Keywords: Industry 4.0; manufacturing industry; sustainable development; environmental protection.
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Template-Type: ReDIF-Article 1.0
Author-Name: Xinghong Jia
Author-X-Name-First: Xinghong
Author-X-Name-Last: Jia
Author-Name: Jun Wang
Author-X-Name-First: Jun
Author-X-Name-Last: Wang
Author-Name: Tian Tian Ma
Author-X-Name-First: Tian Tian
Author-X-Name-Last: Ma
Author-Name: Qiong Wang
Author-X-Name-First: Qiong
Author-X-Name-Last: Wang
Title: Grey improvement model for intelligent supply chain demand forecasting
Abstract:
This study helps to realise the balance between supply and demand of aquatic products and rational allocation of logistics resources. In previous studies, the prediction results of most models are not satisfactory for the cold chain logistics demand of aquatic products characterised by small-lot, low-quality uncertain data. In this paper, the traditional grey model and the grey BP neural network combination model are used to simulate and predict the demand for aquatic products cold chain logistics, and analysed and compared. The results show that compared with the traditional grey model, the grey BP neural network model has a reduced prediction error, an ideal ability to handle nonlinear systems and can take into account many influencing factors. Meanwhile, the robustness and generalisation ability of the model were verified by testing it on the dataset of similar scenarios. The method provides an innovative way for aquatic products cold chain logistics demand forecasting, which helps optimise the aquatic products supply chain in China and promotes the prosperous development of cold chain.
Journal: Int. J. of Manufacturing Technology and Management
Pages: 334-357
Issue: 3/4/5
Volume: 39
Year: 2025
Keywords: demand forecasting; intelligent supply chain; BP neural network model.
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Template-Type: ReDIF-Article 1.0
Author-Name: Márcio Henrique Fronteli
Author-X-Name-First: Márcio Henrique
Author-X-Name-Last: Fronteli
Author-Name: Edson Pacheco Paladini
Author-X-Name-First: Edson Pacheco
Author-X-Name-Last: Paladini
Title: The main contributions of digitalisation to servitisation
Abstract:
Digital servitisation has aroused the interest of many researchers and managers. IoT, big data, and cloud computing are the most prominent technologies. This study sought to investigate the impact of other digital technologies (DTechs) on servitisation. This research is based on an exploratory-qualitative approach covering 146 published articles selected from the Scopus and Web of Science databases. We grouped the digital technologies that impacted servitisation positively. Our results were synthesised into four categories. These procedures allowed for building a framework that shows the contribution potential of digitisation as a success factor of servitisation, as well as competitive opportunities based on the combination of DTs. Digitisation is confirmed as the main driver for the development of digital-servitisation based business models. Our results aim to contribute to the industry in terms of establishing innovation strategies for business models.
Journal: Int. J. of Manufacturing Technology and Management
Pages: 358-389
Issue: 3/4/5
Volume: 39
Year: 2025
Keywords: servitisation; digitisation; digital technologies; DTechs; digital servitisation; DS.
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Handle: RePEc:ids:ijmtma:v:39:y:2025:i:3/4/5:p:358-389

Template-Type: ReDIF-Article 1.0
Author-Name: Yan Guo
Author-X-Name-First: Yan
Author-X-Name-Last: Guo
Title: Evaluation of industrial robot loading and unloading capability based on the combination of subjective and objective under cloud manufacturing
Abstract:
The existing evaluation methods for loading and unloading capacity have the problem of low accuracy. Therefore, an evaluation method based on a combination of subjective and objective factors is proposed. Firstly, in the cloud manufacturing environment, collect data on the loading and unloading capabilities of industrial robots. Secondly, establish an evaluation system, determine the subjective weight of evaluation indices based on the improved analytic hierarchy process, and determine the objective weight of evaluation indices based on the entropy method. Finally, the evaluation indices are graded and combined with the weighted average method to obtain the evaluation results of the loading and unloading capacity of industrial robots. Experimental results have shown that the sensitivity of the evaluation results of this method always remains above 90%, and the highest consistency can reach 95%.
Journal: Int. J. of Manufacturing Technology and Management
Pages: 319-333
Issue: 3/4/5
Volume: 39
Year: 2025
Keywords: subjective and objective; industrial robot; loading and unloading capacity; evaluation system; improved analytic hierarchy process; entropy method.
File-URL: http://www.inderscience.com/link.php?id=145932
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Handle: RePEc:ids:ijmtma:v:39:y:2025:i:3/4/5:p:319-333

Template-Type: ReDIF-Article 1.0
Author-Name: Zhenzhuo Wang
Author-X-Name-First: Zhenzhuo
Author-X-Name-Last: Wang
Author-Name: Yijie Zhu
Author-X-Name-First: Yijie
Author-X-Name-Last: Zhu
Title: Multi-objective optimisation method of cutting parameters of CNC machine tools based on improved genetic algorithm
Abstract:
To improve the energy efficiency level of CNC machine tool processing, reduce production costs and energy consumption, a multi-objective optimisation method for cutting parameters of CNC machine tools based on improved genetic algorithm is proposed. Firstly, a multi-objective optimisation model for cutting parameters of CNC machine tools was constructed with the minimum energy consumption, maximum benefit, and maximum timeliness as objective functions, combined with three constraints of cutting speed, cutting power, and feed rate. Then, the objective function values of the cutting parameters of the CNC machine tool are sorted by non-dominated sorting based on the dominated effect. Finally, an improved Pareto genetic multi-objective optimisation method is designed using the improved niche technique and the vector modulus fitness function as the criterion for outliers, in order to obtain the optimal multi-objective optimisation of the cutting parameters of the CNC machine tool. The experimental results show that the method improves the feed speed, cutting speed and cutting depth, reduces the machining energy consumption, machining cost and machining time, and has good application effect.
Journal: Int. J. of Manufacturing Technology and Management
Pages: 195-215
Issue: 3/4/5
Volume: 39
Year: 2025
Keywords: CNC machine tools; objective function; cutting parameters; constraints; goal optimisation; improved genetic algorithm.
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Handle: RePEc:ids:ijmtma:v:39:y:2025:i:3/4/5:p:195-215

Template-Type: ReDIF-Article 1.0
Author-Name: Bin Cai
Author-X-Name-First: Bin
Author-X-Name-Last: Cai
Title: Abnormal node detection method for industrial internet of things based on dynamic trust evaluation algorithm
Abstract:
In order to accurately detect abnormal nodes, an industrial internet of things (IIoT) abnormal node detection method based on dynamic trust evaluation algorithm is proposed. Dynamic trust evaluation considers direct trust, recommendation trust, and historical behavioural trust. This evaluation establishes the trustworthiness of each node in the IIoT. Features are extracted from nodes within the positioning range based on their correlations. These features help identify abnormal nodes accurately. Weighted tracking tasks analyse the trust level of each node and collected data to identify anomalies. Detection results are compared with actual outcomes. Test results show accurate node trust evaluation and consistent detection of abnormal nodes. Implementing this method enhances IIoT's security and reliability by efficiently identifying and responding to abnormal nodes.
Journal: Int. J. of Manufacturing Technology and Management
Pages: 287-299
Issue: 3/4/5
Volume: 39
Year: 2025
Keywords: dynamic trust evaluation algorithm; industrial internet of things; IIoT; abnormal node detection; node characteristics.
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Handle: RePEc:ids:ijmtma:v:39:y:2025:i:3/4/5:p:287-299

Template-Type: ReDIF-Article 1.0
Author-Name: Yonghong Shao
Author-X-Name-First: Yonghong
Author-X-Name-Last: Shao
Title: Optimisation method for product design based on improved genetic algorithm
Abstract:
To optimise the satisfaction of product design applications and reduce production costs, a product design optimisation method based on improved genetic algorithm is proposed in this study. This method is first based on principal component analysis to determine the product shape image. Then, based on this, a multi-objective optimisation model for product shape is constructed, and the objective function design is completed. Finally, a dissimilarity operator is introduced to improve the genetic algorithm. The improved genetic algorithm is applied to solve the objective function and achieve product shape design optimisation. The experiment was conducted on automobiles as the research object, and the experimental results showed that the application of the proposed method can improve user satisfaction with product shape and appearance design, enhance the strength of the car's external structure, reduce product production costs, and is superior to the comparative method. The application effect is good.
Journal: Int. J. of Manufacturing Technology and Management
Pages: 273-286
Issue: 3/4/5
Volume: 39
Year: 2025
Keywords: genetic algorithm; product design; appearance; multi-objective optimisation design.
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Handle: RePEc:ids:ijmtma:v:39:y:2025:i:3/4/5:p:273-286

Template-Type: ReDIF-Article 1.0
Author-Name: Yanyan Cao
Author-X-Name-First: Yanyan
Author-X-Name-Last: Cao
Title: Financial risk early warning method for modern manufacturing enterprises based on RBF neural network
Abstract:
To detect potential financial risks in advance, reduce the rate of missed and false alarms in risk warning, and improve the accuracy of warning, a modern manufacturing enterprise financial risk warning method based on RBF neural network is proposed. Firstly, network coding methods are used to collect financial data such as total asset turnover rate, current ratio, and net profit margin of modern manufacturing enterprises. Secondly, the K-nearest neighbour method is used to remove outliers from the above data to improve the accuracy of risk warning results. Finally, based on the financial data of modern manufacturing enterprises and the financial risk warning results, a financial risk warning model is constructed using RBF neural network to achieve financial risk warning. The research results indicate that the method has low false positives and false positives rate, and a high F<SUB align="right"><SMALL>1</SMALL></SUB> value, which is beneficial for improving the accuracy and effectiveness of enterprise management decisions.
Journal: Int. J. of Manufacturing Technology and Management
Pages: 183-194
Issue: 3/4/5
Volume: 39
Year: 2025
Keywords: RBF neural network; modern manufacturing enterprises; financial risk warning; network coding; K-nearest neighbour method.
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Handle: RePEc:ids:ijmtma:v:39:y:2025:i:3/4/5:p:183-194

Template-Type: ReDIF-Article 1.0
Author-Name: Yongyi Huang
Author-X-Name-First: Yongyi
Author-X-Name-Last: Huang
Title: A workflow task scheduling algorithm based on parallel layering and priority
Abstract:
This study aims to improve the overall execution efficiency of the system by dividing workflow tasks into multiple levels and executing tasks at different levels simultaneously. Firstly, a parallel layered approach is adopted to partition the execution order of tasks, construct a task dependency graph, and dynamically adjust task granularity based on computational complexity to avoid conflicts and competition between tasks. Secondly, based on the results of workflow task partitioning, calculate task priorities and arrange each task in priority order. Finally, based on the calculation of priority, a greedy algorithm is used for workflow scheduling. The experimental results show that the algorithm can effectively utilise parallel computing resources, optimise the execution order of tasks, reduce task waiting time, and improve the overall load balancing and resource utilisation of the system, thereby enhancing the task scheduling effect and performance of the workflow system.
Journal: Int. J. of Manufacturing Technology and Management
Pages: 260-272
Issue: 3/4/5
Volume: 39
Year: 2025
Keywords: parallel layering; priority; workflow; task scheduling; task dependency graph; greedy algorithm.
File-URL: http://www.inderscience.com/link.php?id=145937
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Handle: RePEc:ids:ijmtma:v:39:y:2025:i:3/4/5:p:260-272

Template-Type: ReDIF-Article 1.0
Author-Name: Yaning Li
Author-X-Name-First: Yaning
Author-X-Name-Last: Li
Author-Name: Yanna Shangguan
Author-X-Name-First: Yanna
Author-X-Name-Last: Shangguan
Author-Name: Chao Gao
Author-X-Name-First: Chao
Author-X-Name-Last: Gao
Title: A colour matching method for industrial product packaging driven by multidimensional image information
Abstract:
In order to overcome the problems of poor matching accuracy and long matching time in traditional industrial product packaging colour matching methods, the paper proposes a colour matching method for industrial product packaging driven by multidimensional image information. Firstly, collect multi-dimensional sensory information from industrial product packaging images; Then, clustering of packaging cognitive information is driven by dimensional image information to extract colour features from packaging images; Finally, the Lagrange function is introduced to calculate colour similarity, and a packaging colour matching model is constructed based on Bayesian analysis. Penalty parameters are introduced for model solving to obtain the final colour matching result. The results show that the accuracy of packaging colour matching under the proposed method can reach 99.32%, and the accuracy of colour matching can reach 99.6%, which verifies the matching effect of the proposed method.
Journal: Int. J. of Manufacturing Technology and Management
Pages: 247-259
Issue: 3/4/5
Volume: 39
Year: 2025
Keywords: driven by multidimensional image information; multidimensional perceptual information; Lagrange function; Bayesian.
File-URL: http://www.inderscience.com/link.php?id=145938
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Handle: RePEc:ids:ijmtma:v:39:y:2025:i:3/4/5:p:247-259

Template-Type: ReDIF-Article 1.0
Author-Name: Huimin Wang
Author-X-Name-First: Huimin
Author-X-Name-Last: Wang
Author-Name: Chao Gao
Author-X-Name-First: Chao
Author-X-Name-Last: Gao
Author-Name: Yaning Li
Author-X-Name-First: Yaning
Author-X-Name-Last: Li
Title: A contrast enhancement method for product packaging images based on adaptive equalisation
Abstract:
In order to solve the problems of low peak signal-to-noise ratio and poor product contrast in traditional product packaging image contrast enhancement, a product packaging image contrast enhancement method based on adaptive equalisation is proposed. Build a product packaging image acquisition architecture, obtain packaging images, and use guided filtering methods to denoise the images. Divide the denoised image into two sub images, perform equalisation on the histograms of the sub images within their respective ranges, merge the two sub histograms after equalisation, and obtain the contrast-enhanced product packaging image. The experimental results show that the maximum peak signal-to-noise ratio of the proposed method is 56.8dB, the maximum contrast value of the product packaging image is 0.46, and the maximum enhancement task time is 0.67s. The image contrast enhancement effect is good.
Journal: Int. J. of Manufacturing Technology and Management
Pages: 232-246
Issue: 3/4/5
Volume: 39
Year: 2025
Keywords: adaptive equalisation; product packaging; contrast enhancement; CMOS image sensor; guided filtering.
File-URL: http://www.inderscience.com/link.php?id=145939
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Handle: RePEc:ids:ijmtma:v:39:y:2025:i:3/4/5:p:232-246

Template-Type: ReDIF-Article 1.0
Author-Name: Yalin Wang
Author-X-Name-First: Yalin
Author-X-Name-Last: Wang
Title: Industrial IoT heterogeneous device access authentication for enterprise production management
Abstract:
To address the low accuracy, lengthy duration, and poor application effects of traditional methods, an industrial IoT heterogeneous device access authentication method for enterprise production management is proposed. This method utilises grey prediction techniques for predicting industrial IoT communication channel resources and constructing a communication channel model for industrial IoT. Based on the constructed model, industrial IoT communication data is obtained and the AdaBoost algorithm is employed to identify the identities of the industrial IoT heterogeneous devices. Leveraging enterprise production management as the foundation for the construction of the heterogeneous device access authentication architecture, this architecture combines the identity recognition results and achieves device access authentication through steps such as key initialisation, key extraction, and mutual authentication. The experimental results demonstrate that this method achieves a maximum authentication accuracy of 97.6%, a maximum authentication time of 84.1 ms, and exhibits good application effects.
Journal: Int. J. of Manufacturing Technology and Management
Pages: 216-231
Issue: 3/4/5
Volume: 39
Year: 2025
Keywords: enterprise production management; industrial IoT; heterogeneous devices; access authentication; communication channel model; key extraction.
File-URL: http://www.inderscience.com/link.php?id=145940
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Handle: RePEc:ids:ijmtma:v:39:y:2025:i:3/4/5:p:216-231

Template-Type: ReDIF-Article 1.0
Author-Name: Junzhi Song
Author-X-Name-First: Junzhi
Author-X-Name-Last: Song
Title: Multi-objective scheduling of industrial intelligent manufacturing workshops based on variable neighbourhood genetic algorithm
Abstract:
Traditional multi-objective scheduling methods in industrial intelligent manufacturing workshops suffer from low efficiency and long scheduling minimisation time. To address this issue, a new multi-objective scheduling method of industrial intelligent manufacturing workshops based on variable neighbourhood genetic algorithm is designed. Industrial intelligent manufacturing workshop multi-objective parameters are selected, including completion time, completion process, machine load, and cost. A multi-objective scheduling function is built using the obtained parameters. The variable neighbourhood genetic algorithm is employed to generate neighbourhood sequences and initial solutions, and genetic operations such as encoding, mutation, and crossover are applied to form a new population, thereby achieving the solution of the objective function and realising optimal scheduling. The test results show that the algorithm proposed in this paper can improve the multi-objective scheduling efficiency of industrial intelligent manufacturing workshops and reduce the minimum scheduling time.
Journal: Int. J. of Manufacturing Technology and Management
Pages: 300-318
Issue: 3/4/5
Volume: 39
Year: 2025
Keywords: intelligent manufacturing workshop; variable neighbourhood genetic algorithm; multi-objective scheduling; parameters; objective function.
File-URL: http://www.inderscience.com/link.php?id=145943
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Handle: RePEc:ids:ijmtma:v:39:y:2025:i:3/4/5:p:300-318

Template-Type: ReDIF-Article 1.0
Author-Name: Dhiviandran Chadaran
Author-X-Name-First: Dhiviandran
Author-X-Name-Last: Chadaran
Author-Name: Ainul Akmar Mokhtar
Author-X-Name-First: Ainul Akmar
Author-X-Name-Last: Mokhtar
Author-Name: Hilmi Hussin
Author-X-Name-First: Hilmi
Author-X-Name-Last: Hussin
Title: Analysing the process and quality evaluation of body-in-white part inspection methodology using augmented reality: an investigation
Abstract:
Automotive body manufacturing runs based on processes and each process requires a cycle time. Producing good quality products within the stipulated cycle time is necessary. Measuring the quality of finished product is also part of the cycle and consumes time. Standard average time has been investigated and discussed for a case study of 30 samples from a similar product. Datum point inspection method using checking fixtures is discussed and measuring points of the product are determined. Finding inconsistencies of process time and rejected product are discussed. Measuring the quality of the finished product is also part of the process. An average of 71.6% of time was used for datum measurement. The advancement of augmented reality in measuring the quality of manufactured products has been investigated and discovered that there is a 20% in reduction of datum measurement process time compared to conventional method. This research ought to be an interactive approach in quality inspection, replacing the need for hardcopy instruction manuals as well as time saving process in contrast to conventional method.
Journal: Int. J. of Manufacturing Technology and Management
Pages: 390-405
Issue: 3/4/5
Volume: 39
Year: 2025
Keywords: augmented reality; process time; datum point; quality; inspection.
File-URL: http://www.inderscience.com/link.php?id=145944
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Handle: RePEc:ids:ijmtma:v:39:y:2025:i:3/4/5:p:390-405

Template-Type: ReDIF-Article 1.0
Author-Name: Choudhury Abul Anam Rashed
Author-X-Name-First: Choudhury Abul Anam
Author-X-Name-Last: Rashed
Author-Name: Mst. Nasima Bagum
Author-X-Name-First: Mst. Nasima
Author-X-Name-Last: Bagum
Author-Name: Syeda Kumrun Nahar
Author-X-Name-First: Syeda Kumrun
Author-X-Name-Last: Nahar
Author-Name: Md. Mehedi Hasan Kibria
Author-X-Name-First: Md. Mehedi Hasan
Author-X-Name-Last: Kibria
Title: Challenges towards manufacturing transformation to Industry 4.0
Abstract:
To cope with the rapid growth of technology, the manufacturing sector needs to incorporate updated technology and innovations. Several obstacles need to be addressed before the implementation of the updated technology. The research objectives are to identify the challenges hindering the transformation towards I4.0 and prioritise the challenges. Twelve challenges are determined by an extensive literature review and consultation with relevant experts. A semi-structured questionnaire-based survey was performed in the manufacturing sector. Based on the obtained data from 125 respondents, statistical analysis was performed using Microsoft Excel and SPSS 25. Based on the results, a conceptual model was developed and tested with PLS-SEM 4.0. The results showed that lack of sufficient capital, high expenditure in implementation, legislative problems, insufficient technological knowledge, lack of proper structure, deficiency of skilled workforce, cheap labour, etc., are the major challenges in implementing cutting-edge technology and innovations in the manufacturing sector.
Journal: Int. J. of Manufacturing Technology and Management
Pages: 423-451
Issue: 3/4/5
Volume: 39
Year: 2025
Keywords: challenges; cutting edge technology; Industry 4.0; I4.0; manufacturing transformation; digital and e-manufacturing.
File-URL: http://www.inderscience.com/link.php?id=145948
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Handle: RePEc:ids:ijmtma:v:39:y:2025:i:3/4/5:p:423-451

Template-Type: ReDIF-Article 1.0
Author-Name: Felipe Alves de Oliveira Perroni
Author-X-Name-First: Felipe Alves de Oliveira
Author-X-Name-Last: Perroni
Author-Name: Ugo Ibusuki
Author-X-Name-First: Ugo
Author-X-Name-Last: Ibusuki
Author-Name: Eduardo de Senzi Zancul
Author-X-Name-First: Eduardo de Senzi
Author-X-Name-Last: Zancul
Author-Name: Klaus Schützer
Author-X-Name-First: Klaus
Author-X-Name-Last: Schützer
Author-Name: Cláudio Nogueira de Meneses
Author-X-Name-First: Cláudio Nogueira de
Author-X-Name-Last: Meneses
Author-Name: Thiago Cannabrava de Sousa
Author-X-Name-First: Thiago Cannabrava de
Author-X-Name-Last: Sousa
Title: Fixture devices monitoring for machining condition optimisation aided by machine learning
Abstract:
This paper focuses on applying recent digitisation technologies for machining process improvement based on fixture device monitoring. Industry 4.0 technologies support smart monitoring of manufacturing processes, enabling semi-autonomous tool process parameters adjustment, reducing human-machine interactions, resulting in more accurate process improvements. The paper aims to present the results of a project development and validation of a machining conditioning monitoring system, combining measures conducted directly in the spindle unit and fixture devices. The machining condition monitoring system, aided by a machine learning algorithm, uses vibration data to determine the tool's maximum wear. The project, a collaborative effort by two universities, was designed for practical application and rigorously tested in a real-world operational environment at an automotive company.
Journal: Int. J. of Manufacturing Technology and Management
Pages: 406-422
Issue: 3/4/5
Volume: 39
Year: 2025
Keywords: device monitoring; condition monitoring system; process optimisation; machine learning.
File-URL: http://www.inderscience.com/link.php?id=145949
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Handle: RePEc:ids:ijmtma:v:39:y:2025:i:3/4/5:p:406-422

Template-Type: ReDIF-Article 1.0
Author-Name: Yousef Amer
Author-X-Name-First: Yousef
Author-X-Name-Last: Amer
Author-Name: Praveen Kelath
Author-X-Name-First: Praveen
Author-X-Name-Last: Kelath
Author-Name: Ashraf Zaghwan
Author-X-Name-First: Ashraf
Author-X-Name-Last: Zaghwan
Title: Optimising aesthetic automotive component manufacturing: comparative analysis of injection pressure in injection compression moulding vs. injection moulding
Abstract:
This study investigates the injection pressure differences between injection moulding (IM) and injection compression moulding (ICM) processes for manufacturing aesthetic automotive interior components. Using Autodesk Moldflow® software, we conducted a simulation-based analysis of ICM injection pressure for various specimens with wall thicknesses ranging from 1 mm to 4 mm and flow lengths from 50 mm to 300 mm, using PC Lexan LS1 material. The results show that the pressure disparity between IM and ICM increases with thicker walls and shorter flow lengths, ranging from 30% to 51%. Correlation analysis indicates a strong positive relationship (0.999) between IM and ICM, which is supported by regression analysis. Physical trials validate the simulation outcomes. Future research should address parameters such as material selection and process constraints. The implementation of ICM could enhance manufacturing efficiency for aesthetic automotive components, offering cost reductions and new business opportunities.
Journal: Int. J. of Manufacturing Technology and Management
Pages: 452-482
Issue: 3/4/5
Volume: 39
Year: 2025
Keywords: injection compression moulding; ICM; simulation; injection pressure; Moldflow®; Radome badges; automotive; injection moulding.
File-URL: http://www.inderscience.com/link.php?id=145950
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Handle: RePEc:ids:ijmtma:v:39:y:2025:i:3/4/5:p:452-482