Discrete Manufacturing Industry Research Articles

Sustenance Strategies for Lean Manufacturing Implementation in Malaysian Manufacturing Industries

This study aims to investigate the relationship between sustaining factors and lean manufacturing implementation. This survey-based study was a cross-sectional study and the samples were drawn by using cluster sampling procedure from medium and large manufacturing companies based on the Federation of Manufacturers Malaysia (FMM) with the final number of 151 respondents. In total, four hypotheses were developed and tested statically using PLS-SEM through SmartPLS software. The result provided evidence that lean culture, lean leadership, and lean knowledge management have a positive relationship on lean manufacturing implementation. However, lean supplier management does not have a positive relationship on lean manufacturing implementation. The survey was responded by middle and top-level management from the discrete manufacturing industries. Although there is growing interest in empirical shreds of evidence in favor of sustaining lean, this study provides a comprehensive view of sustaining factors for lean manufacturing implementation. Hence, this study contributes to expanding the boundary of the existing literature and contributes to the body of knowledge while providing insights to practitioners in tailoring strategies to sustain lean manufacturing implementation and leverage their performance.

A novel implicit decision variable classification approach for high-dimensional robust multi-objective optimization in order scheduling

AbstractThis paper efficiently addresses the high-dimensional robust order scheduling problem. A novel algorithm named dynamic cooperative coevolution based on an implicit decision variable classification approach (DCC/IDVCA) is developed to search for robust order schedules. To significantly reduce the computational resources required for solving the high-dimensional robust order scheduling problem, we propose decomposing the original decision variables through implicit classification methods. First, a novel estimation method is introduced to evaluate the weighted contribution of variables to robustness. This method utilizes historical information, including the variation of the overall mean effective fitness and the frequency of variables being classified into highly robustness-related subcomponents in previous cycles, for evaluating their weighted contribution to robustness. Then, based on the corresponding weighted robustness contributions, the original variables are classified into highly and weakly robustness-related variables. Finally, these two types of variables are decomposed into highly and weakly robustness-related subgroups within a dynamic cooperative coevolution framework and optimized separately. In the experimental section, the proposed algorithm is applied to two practical order scheduling problems in discrete manufacturing industry. The experimental results demonstrate that the proposed algorithm achieves competitive outcomes compared to state-of-the-art high-dimensional robust multi-objective optimization algorithms.

Modeling of Digital Twin Workshop in Planning via a Graph Neural Network: The Case of an Ocean Engineering Manufacturing Intelligent Workshop

In the era of Industry 4.0 to 5.0, the manufacturing industry is dedicated to improving its production efficiency, control capability and competitiveness with intelligent enhancement. As a typical discrete manufacturing industry, it is difficult for ocean engineering (OE) manufacturers to accurately control the entire production process, and the establishment of an integrated system supported by digital twin (DT) technology is a better solution. This paper proposes a comprehensive set of system architectures for the DT workshop. It focuses on planning, which is the main line of control, to establish a model based on graph neural networks (GNNs) and suggests five decision-support approaches associated with the model from a practical application perspective. The utilization of complete twin data for prediction and visual simulation effectively eliminates the problem of unexpected factors interfering with scheduling in enterprise production planning and achieves the goals of rapid processing and just-in-time completion. The planning model is based on the attention mechanism, which characterizes the disjunctive graph, extracts the input GNN, and outputs the scheduling decision by constructing the multi-attention network of operations and machines to deal with the complicated “operation–machine” combination relationship. The proposed method has been verified in the case of structural assembly and welding workshops, has validity and reliability, and is superior to the traditional priority scheduling rules and heuristics in terms of precision rate and rapidity. Furthermore, the DT system completes the production line application, and its proven reliability supports its full-scale application in future smart factories.

An industrial heterogeneous data based quality management KPI visualization system for product quality control

Purpose Quality management systems are commonly applied to meet the increasingly stringent requirements for product quality in discrete manufacturing industries. However, traditional experience-driven quality management methods are incapable of handling heterogeneous data from multiple sources, leading to information islands. This study aims to present a quality management key performance indicator visualization (QM-KPIVIS) system to enable integrated quality control and ultimately ensure product quality. Design/methodology/approach Based on multiple heterogeneous data, an integrated approach is proposed to quantify explicitly the relationship between Internet of Things data and product quality. Specifically, this study identifies the tracing path of quality problems based on multiple heterogeneous quality information tree. In addition, a hierarchical analysis approach is adopted to calculate the key performance indicators of quality influencing factors in the quality control process. Findings Proposed QM-KPIVIS system consists of data visualization, quality problem processing, quality optimization and user rights management modules, which perform in a well-coordinated manner. An empirical study was also conducted to validate the effectiveness of proposed system. Originality/value To the best of the authors’ knowledge, this study is the first attempt to use industrial Internet of Things and multisource heterogeneous data for integrated product quality management. Proposed approach is more user-friendly and intuitive compared to traditional empirically driven quality management methods and has been initially applied in the manufacturing industry.

Research on Key Technologies of Intelligent Perception and Control in Discrete Manufacturing Industry based on IEC61499 Standard

This paper aims at the characteristics of discrete manufacturing, such as changeable process, complex process, multiple varieties and small batch, the mechanism of real-time perception and dynamic optimization of various production factors such as materials, equipment and personnel in the production process is established, the interaction and integration between the physical world and information space in the discrete manufacturing environment is established, and the distributed industrial intelligent perception and control solution is developed based on the industrial Internet IEC61499 standard.

A Novel Production Scheduling Approach Based on Improved Hybrid Genetic Algorithm

Due to the complexity of the production shop in discrete manufacturing industry, the traditional genetic algorithm (GA) cannot solve the production scheduling problem well. In order to enhance the GA-based method to solve the production scheduling problem effectively, the simulated annealing algorithm (SAA) is used to develop an improved hybrid genetic algorithm. Firstly, the crossover probability and mutation probability of the genetic operation are adjusted, and the elite replacement operation is adopted for simulated annealing operator. Then, a mutation method is used for the comparison and replacement of the genetic operations to obtain the optimal value of the current state. Lastly, the proposed hybrid genetic algorithm is compared with several scheduling algorithms, and the superiority and efficiency of the proposed method are verified in solving the production scheduling.

Research on the evaluation method of the operation status of digital workshop in discrete manufacturing industry

Research on the evaluation method of the operation status of digital workshop in discrete manufacturing industry

Paths for the Digital Transformation and Intelligent Upgrade of China's Discrete Manufacturing Industry

Paths for the Digital Transformation and Intelligent Upgrade of China's Discrete Manufacturing Industry

Research on machine learning Algorithm optimization based on 0-1 coding

Machine learning algorithm is the core of artificial intelligence, is the fundamental way to make computer intelligent, its application in all fields of artificial intelligence. Aiming at the problems of the existing algorithms in the discrete manufacturing industry, this paper proposes a new 0-1 coding method to optimize the learning algorithm, and finally proposes a learning algorithm of “IG type learning only from the best”.

Patterns for Visual Management in Industry 4.0.

The technologies of Industry 4.0 provide an opportunity to improve the effectiveness of Visual Management in manufacturing. The opportunity of improvement is twofold. From one side, Visual Management theory and practice can inspire the design of new software tools suitable for Industry 4.0; on the other side, the technology of Industry 4.0 can be used to increase the effectiveness of visual software tools. The paper first explores how the theoretical result on Visual Management can be used as a guideline to improve human-computer interaction, then a methodology is proposed for the design of visual patterns for manufacturing. Four visual patterns are presented that contribute to the solution of problems frequently encountered in discrete manufacturing industries; these patterns help to solve planning and control problems thus providing support to various management functions. Positive implications of this research concern people engagement and empowerment as well as improved problem solving, decision-making and management of manufacturing processes.

Principles and Design Strategies for Ultra‐efficient Production Systems in the Process Industry

AbstractA sustainable design of production systems, both within and beyond company boundaries, is essential for the future viability of the process industry. In this context, the concept of ultra‐efficiency has recently been developed, aiming at achieving holistic improvements in the fields of action energy and material efficiency, emission reduction, organization, human and staff, with the goal of establishing a positive impact factory. In this paper, the concept developed mainly for the discrete manufacturing industry is transferred to the process industry. It is put into the scientific sub context and elementary approaches, methods and strategies for factory planning and design of production processes in the process industry are summarized.

Time to be responsive in the process industry: a literature-based analysis of trends of change, solutions and challenges

The current uncertain and volatile business context is challenging firms worldwide, leading to the need to be responsive at a competitive cost. This trend is so substantial that it even affects industries traditionally competing in rather stable contexts, such as the process industry. Although the process industry includes multiple sectors with different technologies and processes, these share several aspects that make the industry as a whole distinctive to the discrete manufacturing industry. Based on a literature review, this study identifies and describes trends leading the process industry to the need for responsiveness, corresponding solutions to accommodate the need, and related challenges hindering the industrialization and diffusion of solutions in this industry. This study shows that trends, such as the uncertainty and volatility of market requirements, are challenging the process industry to develop reconfigurability solutions across multiple production levels. The development of reconfigurability solutions is hindered by modularity, integrability, co-ordination and collaboration challenges.

Research on key technologies of edge computing in discrete manufacturing industry in the era of big data

Relying on the centralized operation feedback mode of cloud computing, the data processing capacity is improved, but the delay and energy consumption of discrete manufacturing industry are increased. A resource scheduling method for discrete manufacturing based on edge computing architecture is proposed. Based on the establishment of big data multi cluster edge cloud framework, the task priority of discrete manufacturing industry is determined. According to the task priority of each production link, the objective plan of resource scheduling and unloading is established to achieve real-time and efficient task resource scheduling. The experimental results show that the stability is higher than 78%, the real-time response rate is fast and the performance is better.

Job Shop Scheduling Based on Digital Twin Technology: A Survey and an Intelligent Platform

The concept of digital twinning has become a hot topic in the manufacturing industry in recent years. The emerging digital twin technology is an intelligent technology that makes full use of multimodels, big data, and interdisciplinary knowledge, which provides some new approaches for the field of the intelligent manufacturing industry. The job shop scheduling problem has been an important research field in the discrete manufacturing industry. Digital twin technology is adopted to solve the problem of job shop scheduling, which provides the possibility for the intelligent development of workshops. Based on digital twin technology and combined with the actual problem of production line scheduling, we propose a new intelligent scheduling platform to solve the shop scheduling problems above. Meanwhile, based on the prediction and diagnosis of multisource dynamic interference in the workshop production process by big data analysis technology, the corresponding interference strategy is formulated in advance by the scheduling cloud platform. The model simulation experiment of intelligent dispatching cloud platform was carried out, and some enterprises in intelligent manufacturing workshop were taken as examples to verify the superiority of the dispatching cloud platform. Finally, we look forward to the future research direction of intelligent manufacturing based on digital twin technology.

Investigating the applicability of modular function deployment in the process industry

Modular function deployment (MFD) is a recognized method for designing modular products within the discrete manufacturing industry as a means of accommodating market demands for higher variety, shorter product life cycles and smaller production batches. Nevertheless, while the process industries face similar market demands, product modularity is only sparsely explored. This paper analyzes the five steps of the MFD methodology for their fit to a process industry context based on general industry characteristics and insights from a case company. It is found that several aspects of the method must change before use in a process industrial context is feasible.

A framework for lean manufacturing implementation in Indian textile industry

Lean Manufacturing is a philosophy of continuous improvement to eliminate waste. This paper is the result of a case study conducted in south India's textile industry. Although the concept of lean manufacturing has shown good results in continuous process industry, compared with discrete manufacturing industry it has not been extensively utilized. Process industry and particularly textile industry has highly inflexible automatic machinery with high volume/low product variety. This complexity of the textile industry renders the introduction of lean manufacturing strategies a challenge; hence, it has been taken up as a challenge to introduce lean techniques in a textile market. To enhance the processes we chose a mix of meaning stream mapping (VSM), 5S, kanban, kaizen, poka-yoke, and visual controls. Potential levels of accomplishment prior to and after lean adoption were demonstrated using radar diagrams. The findings of this study show that a thorough analysis of the operation, set-up and changeover time (CO), the use of color coding for volume-mix recognition, the use of kaizen and the consistency circles that inspire the workers are some of the different keys to an effective lean implementation in a textile industry.

Design and Application Research of a Digitized Intelligent Factory in a Discrete Manufacturing Industry

Design and Application Research of a Digitized Intelligent Factory in a Discrete Manufacturing Industry

Design and Application Research of a Digitized Intelligent Factory in a Discrete Manufacturing Industry

The intelligent flexible system is a powerful means for manufacturing enterprises to achieve high efficiency and high output. The physical structure of the flexible system is constructed in this paper. The three systems; ... | Find, read and cite all the research you need on Tech Science Press

Intelligent manufacturing production line data monitoring system for industrial internet of things

Applying the wireless sensor network of the Industrial Internet of Things and the radio frequency identification technology to the production workshop of the discrete manufacturing industry, the real-time status of the shop floor can be automatically collected, providing a powerful decision-making basis for the upper-level planning management department. This paper proposes a reference architecture and construction path for smart factories by analyzing industrial IoT technology and its application in manufacturing workshops. Combined with the analysis of the status quo and needs of the discrete manufacturing enterprise workshop, this paper designs the overall architecture and theoretical model of the system. In view of the variety of on-site manufacturing data, large amount of data, variable status, heterogeneity, and strong correlation between data, integrated key technologies such as WSN and RFID, the industrial IoTs solution for manufacturing workshops is given. The multi-thread data real-time collection, storage technology and product tracking monitoring of the workshop are studied. Finally, the performance of the system is analyzed from the perspective of real-time and quality. The results show that the system is effective in the monitoring of production line data.

The framework design of smart factory in discrete manufacturing industry based on cyber-physical system

ABSTRACTA cyber-physical system (CPS) is a new trend in smart manufacturing-related research, where the physical system acts as a data access role with sensors and communication systems to collect real-world information and communicate to computation modules (i.e. cyber layer), which further analyse and notify the findings to the corresponding physical systems through multiple feedback loops. This paper develops a conceptual framework for a new paradigm called smart factory based on CPS by applying virtual-real mapping and fusion, digital twin, big data-driven, virtualisation, and edge-to-cloud service technology to the manufacturing system. The definitions, characteristics, architectures and previous case studies on smart factory based on CPS are explained in detail. In the paradigm, the digital twin merges the physical system modelling and cyberspace simulation to provide an intelligence solution simulation analysis ability. This generates a variety of digital designs of production processes, quality, cost, efficiency and environmental impact analysis about smart factory business mode in the preproduction phase based on big data which can be analysed, and provide authoritative solutions for actual physical space. The proposed system is applied and validated in a real case study from the circuit breaker production industry.