Procedia manufacturing最新文献

The application of the elements of the Fourth Industrial Revolution (4IR) through the development of a Flexible Manufacturing System (FMS) for manufacturing operations in the railcar industry will promote flexibility, intelligent coordination of the manufacturing operations, efficient handling and quality control. Some production systems in the railcar industry are non-responsive to changes in real time thereby leading to reduction in productivity, hence, the need for a FMS that will cater for the dynamics of manufacturing operation. This work proposes a FMS, which encompasses the assembly line, lean production, logistics and quality assurance. The system comprises of the Radio Frequency Identification Technology (RFID) for components identification and process control, arrays of sensors and cameras, automated material storage and supply, standard interfaces such as the interface for the internet of things (IoT), the robotic welding system as well as a robust intelligent control system. A framework for the implementation of the FMS was developed while the simulation of the designed system was performed using the Anylogic 8.2.3. software. Based on the results obtained, there was an inverse relationship between the operating cycle time of the conveyor and the conveyor speed per cycle when the conveyor’s performance was simulated at a speed of 3 m/s and 7 m/s. The results showed that the system can suitably perform the sequence of assembly and quality assurance operations during the manufacturing of railcar subassemblies with minimal interruptions and human intervention. This will promote production of component parts with high structural and dimensional integrity with significant reduction in the manufacturing cycle time and cost.

Tool wear prediction is of significance in advanced manufacturing industries, as it aims to ensure the quality of parts, improve machining efficiency and reduce machining costs. Existing tool wear monitoring and prediction methods mainly adopt neural network model with fixed architecture, which rely on the researchers’ experience and cannot guarantee accuracy under different cutting conditions. This paper proposes a tool wear prediction method based on network architecture search. which can learn a suitable network structure under different cutting conditions. Experiments shows sufficient improvement in the accuracy of predicting tool wear compared with existing methods.

This paper focuses on the complex selection process of worker assistance systems for human-centered manufacturing systems. Due to rising complexity of products and processes in the manufacturing sector, as well as changing work environments, the choice for suitable support systems on the shop floor becomes more difficult. The selection of worker assistance systems is yet not well studied and there is a lack in scientifically based methodologies to assist designers of manufacturing systems in this process. In this work we identify a broad variety of influencing attributes for selecting the most appropriate worker assistance systems depending on each individual field of application. These attributes are building the ground for the development of a morphological box to facilitate the selection process of worker assistance systems. The proposed procedure to use the morphological box is finally explained with a practical example.

Active involvement of entrepreneurs in the development and implementation of innovations is associated with the ability to use all external and internal human, material, capital and information resources, and this is associated with innovative maturity. This is determined by the level of organizational culture that uses the entrepreneurial sense, creativity and other abilities to create and implement innovations, including ergonomic innovations. Ergonomic innovations integrate the achievements of many sciences humanizing the work and life environment so that they are friendly to the psychophysical needs of the user. Small and mediumsized enterprises play an extremely important role in the Polish economy (enterprises in the territory of the Republic of Poland), constituting a strong group of 99.8% of all economic entities operating on the market. In this article, qualitative studies of ergonomic innovations have been made on the basis of complex online and paper surveys. The results of the conducted research and analyzes on ergonomic innovations can be assessed both in the substantive and utilitarian level, and the value of the article is its contribution to the development of innovation management.

Various studies confirm the positive outcomes of business simulation games, which can be effectively used in the education of manufacturing and supply chain processes. The use of them in education increases business knowledge, causes better understanding of business processes, improves decision making, problem-solving, and interpersonal communication skills. General business simulation games with manufacturing functions included are described briefly. Twenty-two specialized manufacturing and supply chain simulations are discussed in detail showing their features and decision-making possibilities. The study is useful for educators, trainers, and companies looking for practical learning methods. The discussion of the games gives them an opportunity to better understand available business simulations and be able to choose the appropriate one for their expected learning outcomes.

This study investigates the use of a statistical anomaly detection method to analyse in-situ process monitoring data obtained during the Laser-Powder Bed Fusion of Ti-6Al-4V parts. The printing study was carried out on a Renishaw 500M Laser-Powder Bed Fusion system. A photodiode-based system called InfiniAM was used to monitor the melt-pool emissions along with the operational behaviour of the laser during the build process. The analysis of the in-process data was carried out using an unsupervised machine learning approach called the Search and TRace AnomalY algorithm. The ability to detect defects during the manufacturing of metal alloy parts was demonstrated.

Preventive maintenance interventions are scheduled in industrial systems to prevent machine failures and breakdowns, which are associated with the incurrence of repair, unavailability, and quality-related costs. The execution of such interventions, however, generally represents a penalty to a manufacturing system’s production throughput due to machine interruption requirements. By the use of a digital twin architecture, we develop a decision support system to schedule preventive maintenance interventions with the aim of minimizing production throughout penalties via the exploitation of real-time opportunities such as supply shortages, momentary machine idleness or machine breakdowns. The decision support system has its application demonstrated by a case in a furniture manufacturer in the State of Santa Catarina – Brazil.

Efficient material supply is a key requirement for production and assembly processes, but the related planning process is complex due to different factors influencing internal material provision. Different strategies and techniques for storage, order picking and transportation of components as well as for material flow control exist. In this paper, a holistic approach for planning, analyzing and evaluating internal material provision is introduced, influencing factors are revealed and a brief planning guideline is presented. An evaluation approach is pointed out based on qualitative, quantitative and monetary aspects. Finally, the planning approach is applied in a specific use case analyzing different planning variants such as line stocking, kitting and a hybrid solution for material provision at assembly stations in medical technology industry.

Ultrafine-grained (UFG) microstructure of Cu processed by large strain machining (LSM) is explored in order to create highly refined grain structures to achieve the highest strength while postponing the available nuclei for future recrystallization. The optimum solution is obtained theoretically using the Strength Pareto Evolutionary Algorithm (SPEA) and empirically using LSM. The thermal stability of the optimal solution is verified across the comparable LSM conditions using isothermal annealing curve. We also studied the kinetics of crystallization on the optimal solution using the Johnson–Mehl–Avrami–Kolmogorov (JMAK) theory. The optimal solution encountered leads to the latest time for the point where hardness start decline among a comparable sample conditions and lower the rate constant (1/τ) among LSM conditions.