A “digital twin” is a dynamic, digital replica of a technical object (e.g., a physical system, device, machine or production process) or a living organism. Using this type of solution has become an integral part of Industry 4.0, offering businesses tangible benefits, in addition to being particularly effective within the context of sustainable production and maintenance. The purpose of this paper is to present the results of research on the development of digital twins of technical objects, which involved data acquisition and their conversion into knowledge, the use of physical models to simulate tasks and processes, and the use of simulation models to improve the physical tasks and processes. In addition, monitoring processes and process parameters allow for the continued improvement of existing processes as regards intelligent eco-designing and planning and monitoring production processes while taking into account sustainable production and maintenance.

While the intensity, complexity, and specificity of robotic exercise may be supported by patient-tailored three-dimensional (3D)-printed solutions, their performance can still be compromised by non-optimal combinations of technological parameters and material features. The main focus of this paper was the computational optimization of the 3D-printing process in terms of features and material selection in order to achieve the maximum tensile force of a hand exoskeleton component, based on artificial neural network (ANN) optimization supported by genetic algorithms (GA). The creation and 3D-printing of the selected component was achieved using Cura 0.1.5 software and 3D-printed using fused filament fabrication (FFF) technology. To optimize the material and process parameters we compared ten selected parameters of the two distinct printing materials (polylactic acid (PLA), PLA+) using ANN supported by GA built and trained in the MATLAB environment. To determine the maximum tensile force of the exoskeleton, samples were tested using an INSTRON 5966 universal testing machine. While the balance between the technical requirements and user safety constraints requires further analysis, the PLA-based 3D-printing parameters have been optimized. Additive manufacturing may support the successful printing of usable/functional exoskeleton components. The network indicated which material should be selected: Namely PLA+. AI-based optimization may play a key role in increasing the performance and safety of the final product and supporting constraint satisfaction in patient-tailored solutions.

Purpose. Female runners are known to be at greater risk from chronic running injuries than age-matched males, although the exact mechanisms are often poorly understood. The aim of the current investigation was to determine if female recreational runners exhibit distinct limb and joint stiffness characteristics in relation to their male counterparts. Methods. Fourteen male and fourteen female runners ran over a force platform at 4.0 m · s -1 . Lower limb kinematics were collected using an eight-camera optoelectric motion capture system operating at 250 Hz. Measures of limb and joint stiffness were calculated as a function of limb length and joint moments divided by the extent of limb and joint excursion. All stiffness and joint moment parameters were normalized to body mass. Sex differences in normalized limb and knee and ankle joint stiffness were examined statistically using independent samples t tests. Results. The results indicate that normalized limb (male = 0.18 ± 0.07, female = 0.37 ± 0.10 kN · kg · m -1 ) and knee stiffness (male = 5.59 ± 2.02, female = 7.34 ± 1.78 Nm · kg · rad -1 ) were significantly greater in female runners. Conclusions. On the basis that normalized knee and limb stiffness were shown to be significantly greater in female runners, the findings from the current investigation may provide further insight into the aetiology of the distinct injury patterns observed between sexes.

Accepted: 27 February 2017The large variability of internal and external factors is a serious problem hampering production management. To meet the standards and at the same time ensure the viability of production it is necessary to quickly respond to problems that arise during production processes, and to adequately correct plans. Measures taken in the management of madetoorder production are frequently single and unique, and therefore resemble the features of project management. The paper discusses the project management method derived from the theory of constraints -CCPM. The paper describes an original algorithm for CCPM implementation and presents the results of success CCPM implementation in a company from the Wielkopolska region. The implementation of CCPM resulted in improved timeliness of order delivery, improved communication and standardization of processes related to order delivery.Keywords project management, theory of constraints, critical chain project management. IntroductionOne of the main challenges facing SMEs in made to order manufacturing is having to combine their regular operations with continuous development, improvement and following new trends, regularly appearing in the economic environment. The pace of change taking place in the economic system, the development of techniques and technologies, changes in the law and constantly changing customer requirements mean that the failure to adapt causes obvious losses to SMEs. Global competition and market demand for customized products and services, delivered just in time, exert real stress on businesses [1,2]. Additionally, nowadays companies establish new manufacturing sites in different locations and form strategic relationships with business partners in order to increase their responsiveness to market changes and to share resources more effectively and efficiently [3,4]. These factors make it even more difficult to manage the project: deliver the customer's order [5,6].Project management is a management method whose aim is to effectively reach the project objective within the specified time and a fixed budget [7]. There are many methods supporting project management described in the literature. The most commonly used in practice include Gantt chart, Critical Path Method (CPM) and Project Evaluation and Review Technique (PERT).A Gantt chart is a horizontal bar chart developed as a production control tool in 1917 by Henry L. Gantt, an American engineer and social scientist. Frequently used in project management, a Gantt chart provides a graphical illustration of a schedule that helps to plan, coordinate, and track specific activities in a project. CPM and PERT originated in 1957 and 1958, respectively, with CPM examin-ing the tradeoffs between project duration reduction and increases in activity and project costs; and with PERT examining the uncertainty aspects of completion dates for development projects. CPM was originally developed for use with manufacturing plant rebuilds by DuPont and PERT for use with the Polaris These methods...

No abstract

PurposeThe paper aims to describe ideas and implementation of the computer tool for computer‐aided and recycling‐oriented design. Currently, there is a strong tendency to take into account the impact of a product on the natural environment. The authors concentrated on the issue of the recycling process of the product, taking into account the phase of its design. The purpose, structure, technology and example results are presented in this paper.Design/methodology/approachIt was assumed that analysis will be performed based on a set of measures of a product. These measures (developed by the authors), describing the product from the recycling point of view, are calculated automatically, according to the changes (variants) in the product's model. The presented tool is based on agent technology. The structure of the system – the agents, its roles and communication between them – has been described.FindingsThe main achievement of the work presented in this paper is the method supporting eco‐design based on agent technology. Based on the analysis of the process of recycling‐oriented eco‐design, the authors designed and then implemented the tool that aids designers' activities in the area of eco‐design. The drawn‐up method supports decision making concerning designing environmental‐friendly products. Thanks to agent technology, the design process can be conducted in the distributed design environment.Research limitations/implicationsThe usage of the presented computer‐aided and recycling‐oriented system during the design process requires the duplication of some of the engineer's work. The analysis is performed within the presented tool, outside the typical contemporary design environment, such as CAD 3D systems. As a consequence, there is a need to enter the product structure into the described system, which can be inconvenient. The further work of the authors assumes implementation of this concept into a CAD 3D system.Originality/valueThere has been a limited amount of research work regarding application agent technology in the field of end‐of‐life oriented design. The structure of the system, the measurements and idea of extension of product structure are the original results of the work. The results could be implemented into commercial computer‐aided design systems, especially into its PLM (product lifecycle management) group, due to the weak representation of end‐of‐life phases in these tools.

Technological and material issues in 3D printing technologies should take into account sustainable development, use of materials, energy, emitted particles, and waste. The aim of this paper is to investigate whether the sustainability of 3D printing processes can be supported by computational intelligence (CI) and artificial intelligence (AI) based solutions. We present a new AI-based software to evaluate the amount of pollution generated by 3D printing systems. We input the values: printing technology, material, print weight, etc., and the expected results (risk assessment) and determine if and what precautions should be taken. The study uses a self-learning program that will improve as more data are entered. This program does not replace but complements previously used 3D printing metrics and software.

No abstract