On the Efficiency of Laser Alloying of Grey Cast Iron with Tungsten and Silicon Carbides.

Cast iron is widely used in engineering production and in the surface alloying of workpieces, which is exploited to improve the properties of the material. Research on cast iron is still valid and needed for the manufacturing processes throughout the product life cycle. In this study, the gray, cast iron GJL 200 laser processing is described based on surface alloying with WC and SiC particulates. SEM analysis and XRD analysis, as well as microhardness testing and tribological behavior studies, were employed. It was revealed that laser alloying with carbide particulates affects structural, mechanical, and operational properties compared to cast iron in its initial state. Most importantly, the right choice of laser processing conditions can increase the wear resistance of the cast iron base. The wear resistance after WC alloying was 4-24 times higher compared to the initial material, while after SiC alloying, it was 2-18 times lower than that of the initial material.

Inertial Sensor-Based Sport Activity Advisory System Using Machine Learning Algorithms.

The aim of this study was to develop a physical activity advisory system supporting the correct implementation of sport exercises using inertial sensors and machine learning algorithms. Specifically, three mobile sensors (tags), six stationary anchors and a system-controlling server (gateway) were employed for 15 scenarios of the series of subsequent activities, namely squats, pull-ups and dips. The proposed solution consists of two modules: an activity recognition module (ARM) and a repetition-counting module (RCM). The former is responsible for extracting the series of subsequent activities (so-called scenario), and the latter determines the number of repetitions of a given activity in a single series. Data used in this study contained 488 three defined sport activity occurrences. Data processing was conducted to enhance performance, including an overlapping and non-overlapping window, raw and normalized data, a convolutional neural network (CNN) with an additional post-processing block (PPB) and repetition counting. The developed system achieved satisfactory accuracy: CNN + PPB: non-overlapping window and raw data, 0.88; non-overlapping window and normalized data, 0.78; overlapping window and raw data, 0.92; overlapping window and normalized data, 0.87. For repetition counting, the achieved accuracies were 0.93 and 0.97 within an error of ±1 and ±2 repetitions, respectively. The archived results indicate that the proposed system could be a helpful tool to support the correct implementation of sport exercises and could be successfully implemented in further work in the form of web application detecting the user's sport activity.

On the Features of Composite Coating, Based on Nickel Alloy and Aluminum-Iron Bronze, Processed by Direct Metal Deposition.

In recent years, additive manufacturing technologies have become increasingly widespread with the most intensive development being direct metal deposition (DMD), alloys, and ceramic materials on a metal substrate. This study shows the possibilities of the effective formation of coatings, based on heterogeneous metal alloys (Ni-based alloy and Fe-Al bronze) deposited onto 1045 structural steel. Changes in the microhardness, the microstructure, and the tribological properties of the composite coating, depending on the laser spot speed and pitch during DMD processing, have been considered. It was revealed that if the components of the composite coating are chosen correctly, there are possible DMD conditions ensuring reliable and durable connection between them and with the substrate.

On Investigating the Microstructural, Mechanical, and Tribological Properties of Hybrid FeGr1/SiC/Gr Metal Matrix Composites.

In recent years, studies of different properties of hybrid metal matrix composites, as well as very detailed issues, have been published. In this article, ready-made iron, graphite, and silicon carbide powders were used to produce the base material and composites. An analysis of some microstructural and mechanical properties, as well as the tribological behavior of metal matrix composites (MMCs), based on FeGr1 sintered material with the single and hybrid addition of a silicon carbide and graphite was undertaken. During the study, the flexural and compressive strength of MMCs were analyzed and changes of the momentary coefficient of friction, the temperature of friction, as well as wear rates of the MMCs tested were monitored. Based on the results, it was revealed that wear rates decreased 12-fold in comparison to the base material when SiC or SiC + Gr were added. Further research into MMCs with ceramic particle additives is proposed.

A Deep Learning-Based Model for the Automated Assessment of the Activity of a Single Worker.

Nowadays, it is necessary to verify the accuracy of servicing work, undertaken by new employees, within a manufacturing company. A gap in the research has been observed in effective methods to automatically evaluate the work of a newly employed worker. The main purpose of the study is to build a new, deep learning model, in order to automatically assess the activity of the single worker. The proposed approach integrates the methods known as CNN, CNN + SVM, CNN + R-CNN, four new algorithms and a piece of work from a selected company, using this as an own-created dataset, in order to create a solution enabling assessment of the activity of single workers. Data were collected from an operational manufacturing cell without any guided or scripted work. The results reveal that the model developed is able to accurately detect the correctness of the work process. The model's accuracy mostly exceeds current state-of-the-art methods for detecting work activities in manufacturing. The proposed two-stage approach, firstly, assigning the appropriate graphic instruction to a given employee's activity using CNN and then using R-CNN to isolate the object from the reference frames, yields 94.01% and 73.15% accuracy of identification, respectively.

Single Tracks as a Key Factor in Additive Manufacturing Technology-Analysis of Research Trends and Metal Deposition Behavior.

In recent years, general studies on Selective Laser Melting (SLM)/Selective Laser Sintering (SLS)/direct metal deposition (DMD) technologies, as well as studies on detailed issues in this area, have been carried out. However, a research gap is observed in investigations into the features of single tracks in the above-mentioned technologies. On the basis of data published in 2016-2019, an approach was adopted for a preliminary quantitative analysis of the knowledge base and also trends observed in the development of new technologies. This study demonstrates the effectiveness of the data mining technique based on the Bayes algorithm for analyzing trends in processes of additive manufacturing and the practical application of the knowledge received using the Bayes algorithm. After the analyses referred to above were completed, single and double layers of a composite material based on the Ni-based alloy and Fe-Al bronze were analyzed under different processing conditions. The effects of laser spot speeds and pitches on microhardness, microstructure, and interlayers' features were described. So, the innovative approach, namely, the combination of the analysis of the scientific database of the phenomenon under study and the subsequent experimental investigation of its features, is the scientific novelty of the present study.