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.

Nowadays, achieving the objectives of sustainable development (SD) within a manufacturing company, through introducing and integrating sustainability into a development strategy, is a key parameter in gaining a competitive advantage in the market. The objective of this study was to develop a decision-tree based methodology to facilitate SD assessment in a manufacturing company, which consists of five main components: (1) Determination of SD indicators based on literature analysis, (2) Using the Analytic Hierarchy Process (AHP) method which determines the priority of the SD criteria, (3) Collecting data to determine the values of the key objectives SD, (4) Using a decision tree to build scenarios of possible actions to increase the level of SD, (5) Indicating recommended actions for continuous monitoring of progress towards reaching SD objectives. In the proposed approach, the use of the AHP method allowed for indicating the most important SD indicators, which made it possible to limit the number of queries to manufacturers on data from real companies regarding the values of SD indicators. Finally, the methodology was applied and verified within a real manufacturing company in order to assist the Management Board in making projections about future actions regarding an increase in SD level.

Given the use of high-strength steels to achieve lightweight construction goals, conventional shear-cutting processes are reaching their limits. Therefore, so-called high-speed impact cutting (HSIC) is used to achieve the required cut surface qualities. A new machine concept consisting of linear motors and an impact mass is presented to investigate HSIC. It allows all relevant parameters to be flexibly adjusted and measured. The design and construction of the test bench, as well as the mechanism for coupling the impact mass, are described. To validate the theoretically determined process speeds, the cutting process was recorded with high-speed cameras, and HSIC with a mild deep-drawing steel sheet was performed. It was discovered that very good cutting edges could be produced, which showed a significantly lower hardening depth than slowly cut reference samples. In addition, HSIC was numerically modelled in LS-DYNA, and the calculated cutting edges were compared with the real ones. With the help of adaptive meshing, a very good agreement for the cutting edges could be achieved. The results show the great potential of using a linear motor in HSIC.

Nowadays, it is necessary to develop a conceptual framework for analysing the relationship between the implementation of Additive Manufacturing (AM) and Supply Chain Management (SCM). In this context, a gap in the research has been observed in the new approach to designing the importance of AM in SCM. The main contribution of this paper, therefore, is a new framework to formulate the role in adopting AM in SCM. The research methodology is based on detailed literature studies of AM in relation to the SCM process within a manufacturing company, as well on a case study, namely the COWAN GmbH manufacturing company who specialise in producing homewares for motorhome enthusiasts. As highlighted in the state-of-the-art analysis, no work, currently available, supports all the features presented.