Procedia manufacturing最新文献

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.

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.

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.

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.

In recent years, manufacturing systems have given rise to manufacturing big data due to the rapid developments in sensor and information technology and that has fueled data-driven research techniques towards addressing the issues in multistage quality control and diagnosis. In this paper, a unified framework with dual Hierarchical Bayesian Networks (HBNs) has been presented for simultaneous online process monitoring and fault diagnosis of a multistage manufacturing system. To achieve this, a novel AMDS (Absolute Mean Deviation of States) control chart has been developed for monitoring the unobserved inputs. The AMDS control chart is built on the AMDS statistic, which is calculated using the inferred states distribution generated utilizing the HBNs of the unobserved inputs. Discrete event simulation results of the two-stage process demonstrate that the methodology can successfully detect process changes and diagnose the root causes of the change. In addition, it can also identify the time at which the fault has occurred and the type (mean shift or variance change) and nature (step faults or slow drifts) of the change. The robustness of the proposed methodology is extensively tested against multiple randomly generated non-linear quadratic process models for two-stage systems.

In the past two decades, the field of additive manufacturing (AM) has seen tremendous growth, especially in the production of functional parts. Unfortunately, improving the dimensional accuracy of these printed parts to the point where they can be used for a broad range of applications has proven challenging. Several methodologies to improve the dimensional accuracy of 3D printed parts have been proposed in the literature. One approach that has seen a considerable amount of work in recent years is product design adjustment based on predictive modeling. Under this approach, predictions of geometric deviations across the surface of a part are used to modify the shape of a part before printing so as to counteract or compensate for the predicted deviations. However, a majority of compensation methods aim at minimizing expected geometric and dimensional error, with a lack of consideration of cost and uncertainty. This study presents a new strategy based on multi-attribute utility theory to account for cost and inherent uncertainty associated with a compensation decision. By establishing manufacturer preferences and prior beliefs about the efficacy of a predictive model, the proposed decision-making strategy for compensation significantly increases the value of a given print to a manufacturer under simulated preferences.

Surface integrity refers to the condition of the workpiece surface modified by a manufacturing process. Surface integrity plays an equally important role in the functional performance and quality of the components as the dimensional accuracy, especially for applications such as load-bearing components and energy absorbers. With the rise in additive manufacturing technologies, the components where surface characteristics play an important role are being manufactured by a combination of additive manufacturing process followed by a finishing process. A secondary finishing process such as machining is essential for components produced by additive manufacturing or powder metallurgy as these processes exhibit poor surface finish, dimensional inaccuracies, and other internal/surface defects such as porosity. High strain rates and cutting temperatures involved in the machining process affect the surface characteristics of the machined component. It is envisaged that the cutting mechanism of porous parts produced by additive manufacturing could be significantly different from that of the continuous material. Therefore, the machining parameters, depending on their interaction with the inherent part porosity, could give rise to different surface characteristics and could be critical in determining the functional performance of the part. This work investigates the effect of micro-machining on the surface integrity and cutting force of additive manufactured Ti6Al4V alloy. Experiments are carried out at different levels of porosity and depth of cut to explore their effects on the cutting force, roughness, micro-hardness, residual stress of the resulting surface. It is seen that the mean cutting force was minimum during machining of porous materials and was maximum during machining of continuous materials. As the depth of cut increased, cutting force increased for continuous and porous materials. Whereas cutting force decreased as the porosity increased and was minimum during machining of most porous material (46 % porous). The surface finish was better for wrought alloy compared to additively manufactured Ti6Al4V alloy. As the porosity increased, the surface finish reduced initially and improved later.