Optimal path planning of a disinfection mobile robot against COVID-19 in a ROS-based research platform

Due to COVID-19 pandemic, there is an increasing demand for mobile robots to substitute human in disinfection tasks. New generations of disinfection robots could be developed to navigate in high-risk, high-touch areas. Public spaces, such as airports, schools, malls, hospitals, workplaces and factories could benefit from robotic disinfection in terms of task accuracy, cost, and execution time. The aim of this work is to integrate and analyse the performance of Particle Swarm Optimization (PSO) algorithm, as global path planner, coupled with Dynamic Window Approach (DWA) for reactive collision avoidance using a ROS-based software prototyping tool. This paper introduces our solution – a SLAM (Simultaneous Localization and Mapping) and optimal path planning-based approach for performing autonomous indoor disinfection work. This ROS-based solution could be easily transferred to different hardware platforms to substitute human to conduct disinfection work in different real contaminated environments.

A deep learning-based worker assistance system for error prevention: Case study in a real-world manual assembly

Modern assembly systems adapt to the requirements of customised and short-lived products. As assembly tasks become increasingly complex and change rapidly, the cognitive load on employees increases. This leads to the use of assistance systems for manual assembly to detect and avoid human errors and thus ensure consistent product quality. Most of these systems promise to improve the production environment but have hardly been studied quantitatively so far. Recent advances in deep learning-based computer vision have also not yet been fully exploited. This study aims to provide architectural, and implementational details of a state-of-the-art assembly assistance system based on an object detection model. The proposed architecture is intended to be representative of modern assistance systems. The error prevention potential is determined in a case study in which test subjects manually assemble a complex explosion-proof tubular lamp. The results show 51 % fewer assembly errors compared to a control group without assistance. Three of the four considered types of error classes have been reduced by at least 42 %. In particular, errors by omission are most likely to be prevented by the system. The reduction in the error rate is observed over the entire period of 30 consecutive product assemblies, comparing assisted and unassisted assembly. Furthermore, the recorded assembly data are found to be valuable regarding traceability and production improvement processes.

The impact of the collaborative workplace on the production system capacity: Simulation modelling vs. real-world application approach

In recent years, there have been more and more collaborative workplaces in different types of manufacturing systems. Although the introduction of collaborative workplaces can be cost-effective, there is still much uncertainty about how such workplaces affect the capacity of the rest of production system. The article presents the importance of introducing collaborative workplaces in manual assembly operations where the production capacities are already limited. With the simulation modelling method, the evaluation of the introduction impact of collaborative workplaces on manual assembly operations that represent bottlenecks in the production process is presented. The research presents two approaches to workplace performance evaluation, both simulation modelling and a real-world collaborative workplace example, as a basis of a detailed time study. The main findings are comparisons of simulation modelling results and a study of a real-world collaborative workplace, with graphically and numerically presented parameters describing the utilization of production capacities, their efficiency and financial justification. The research confirms the expediency of the collaborative workplaces use and emphasise the importance of further research in the field of their technological and sociological impacts.

Impact of Industry 4.0 on decision-making in an operational context

The implementation of Industry 4.0 technologies suggests significant impacts on production systems productivity and decision-making process improvements. However, many manufacturers have difficulty determining to what extent these various technologies can reinforce the autonomy of teams and operational systems. This article addresses this issue by proposing a model describing different types of autonomy and the contribution of 4.0 technologies in the various steps of the decision-making processes. The model was confronted with a set of application cases from the literature. It emerges that new technologies' improvements are significant from a decision-making point of view and may eventually favor implementing new modes of autonomy. Decision-makers can rely on the proposed model to better understand the opportunities linked to the fusion of cybernetic, physical, and social spaces made possible by Industry 4.0.

A multi-criteria decision-making in turning process using the MAIRCA, EAMR, MARCOS and TOPSIS methods: A comparative study

Multi-criteria decision-making is important and it affects the efficiency of a mechanical processing process as well as an operation in general. It is understood as determining the best alternative among many alternatives. In this study, the results of a multi-criteria decision-making study are presented. In which, sixteen experiments on turning process were carried out. The input parameters of the experiments are the cutting speed, the feed speed, and the depth of cut. After conducting the experiments, the surface roughness and the material removal rate (MRR) were determined. To determine which experiment guarantees the minimum surface roughness and maximum MRR simultaneously, four multi-criteria decision-making methods including the MAIRCA, the EAMR, the MARCOS, and the TOPSIS were used. Two methods the Entropy and the MEREC were used to determine the weights for the criteria. The combination of four multi-criteria making decision methods with two determination methods of the weights has created eight ranking solutions for the experiments, which is the novelty of this study. An amazing result was obtained that all eight solutions all determined the same best experiment. From the obtained results, a recommendation was proposed that the multi-criteria making decision methods and the weighting methods using in this study can also be used for multi-criteria making decision in other cases, other processes.

A comparative study of different pull control strategies in multi-product manufacturing systems using discrete event simulation

Pull production control strategies coordinate manufacturing operations based on actual demand. Up to now, relevant publications mostly examine manufacturing systems that produce a single type of a product. In this research, we examine the CONWIP, Base Stock, and CONWIP/Kanban Hybrid pull strategies in multi-product manufacturing systems. In a multi-product manufacturing system, several types of products are manufactured by utilizing the same resources. We develop queueing network models of multi-stage, multi-product manufacturing systems operating under the three aforementioned pull control strategies. Simulation models of the alternative production systems are implemented using an open-source software. A comparative evaluation of CONWIP, Base Stock and CONWIP/Kanban Hybrid in multi-product manufacturing is carried out in a series of simulation experiments with varying demand arrival rates, setup times and control parameters. The control strategies are compared based on average wait time of backordered demand, average finished products inventories, and average length of backorders queues. The Base Stock strategy excels when the manufacturing system is subjected to high demand arrival rates. The CONWIP strategy produced consistently the highest level of finished goods inventories. The CONWIP/Kanban Hybrid strategy is significantly affected by the workload that is imposed on the system.

Molecular-dynamics study of multi-pulsed ultrafast laser interaction with copper

Ultrafast laser has an undeniable advantage in laser processing due to its extremely small pulse width and high peak energy. While the interaction of ultrafast laser and solid materials is an extremely non-equilibrium process in which the material undergoes phase transformation and even ablation in an extremely short time range. This is the coupling of the thermos elastic effect caused by the pressure wave and the superheated melting of the material lattice. To further explore the mechanism of the action of ultrafast laser and metal materials, the two-temperature model coupling with molecular dynamics method was used to simulate the interaction of the copper and laser energy. Firstly, the interaction of single-pulsed laser and copper film was reproduced, and the calculated two-temperature curve and the visualized atomic snapshots were used to investigate the influence of laser parameters on the ablation result. Then, by changing the size of the atomic system, the curve of ablation depth as a function of laser fluence was obtained. In this paper, the interaction of multi-pulsed laser and copper was calculated. Two-temperature curve and temperature contour of copper film after the irradiation of double-pulsed and multi-pulsed laser were obtained. And the factors which can make a difference to the incubation effect were analyzed. By calculating the ablation depth under the action of multi-pulsed laser, the influence of the incubation effect on ablation results was further explored. Finally, a more accurate numerical model of laser machining metal is established and verified by an ultra-short laser processing experiment, which provides a new calculation method and theoretical basis for ultra-fast laser machining of air film holes in aviation turbine blades, and has certain practical guiding significance for laser machining.

Latent class analysis for identification of occupational accident casualty profiles in the selected Polish manufacturing sector

The objective of the analysis is identifying profiles of occupational accident casualties as regards production companies to provide the necessary knowledge to facilitate the preparation and management of a safe work environment. Qualitative data characterizing employees injured in accidents registered in Polish wood processing plants over a period of 10 years were the subject of the research. The latent class analysis (LCA) method was employed in the investigation. This statistical modelling technique, based on the values of selected indicators (observed variables) divides the data set into separate groups, called latent classes, which enable the definition of patterns. A procedure which supports the decision as regards the number of classes was presented. The procedure considers the quality of the LCA model and the distinguishability of the classes. Moreover, a method of assessing the importance of indicators in the patterns description was proposed. Seven latent classes were obtained and illustrated by the heat map, which enabled the profiles identification. They were labelled as follows: very serious, serious, moderate, minor (three latent classes), slight. Some recommendations were made regarding the circumstances of occupational accidents with the most severe consequences for the casualties.

Using augmented reality devices for remote support in manufacturing: A case study and analysis

Industry 4.0 forces increased digitization, production flexibility, improvement of employee competences and integration of employees and IT systems within an enterprise. To this end, state-of-the-art systems and IT solutions, such as the Virtual Reality (VR) and Augmented Reality (AR), are implemented. New systems must be integrated with the existing IT architecture, and their implementation forces the enterprise to provide network access with sufficient bandwidth to fully benefit from the capabilities of new technologies. The paper discusses the practical application of modern AR solutions in the industry, with a special focus on remote support for maintenance operations and training of production employees. Two experiments aimed determining the impact of various environmental conditions on the possibility of using the AR Remote Support are described. Basing on those experiments it is possible to determine the environmental conditions required to use HoloLens 2 AR goggles in two dedicated remote support applications.

A multi-objective selective maintenance optimization method for series-parallel systems using NSGA-III and NSGA-II evolutionary algorithms

Aiming at the problem that the downtime is simply assumed to be constant and the limited resources are not considered in the current selective maintenance of the series-parallel system, a three-objective selective maintenance model for the series-parallel system is established to minimize the maintenance cost, maximize the probability of completing the next task and minimize the downtime. The maintenance decision-making model and personnel allocation model are combined to make decisions on the optimal length of each equipment’s rest period, the equipment to be maintained during the rest period and the maintenance level. For the multi-objective model established, the NSGA-III algorithm is designed to solve the model. Comparing with the NSGA-II algorithm that only considers the first two objectives, it is verified that the designed multi-objective model can effectively reduce the downtime of the system.