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Buildings are one of the largest contributors to negative environmental impacts because of the high consumption of energy and materials during their life cycle. The present work proposes a framework, able to make available information, both of general materials and specific commercial solutions; moreover, it overcomes the current state of the art, since, although focused on environmental sustainability, provides the opportunity to compare simultaneously several choices, also considering their properties and characteristics. Based on the proposed methodology, a tool structure and workflow are presented. The main potentiality is represented by the possibility of executing sustainability assessment already in the early stages of building design using the proposed tool when design choices significantly contribute to the global environmental impact of solutions. A validation procedure to quantitatively evaluate the main tool's limits and potentialities is proposed.

The scientific literature highlights how Mixed Reality (MR) simulations allow obtaining several benefits in healthcare education. Simulation-based training, boosted by MR, offers an exciting and immersive learning experience that helps health professionals to acquire knowledge and skills, without exposing patients to unnecessary risks. High engagement, informational overload, and unfamiliarity with virtual elements could expose students to cognitive overload and acute stress. The implementation of effective simulation design strategies able to preserve the psychological safety of learners and the investigation of the impacts and effects of simulations are two open challenges to be faced. In this context, the present study proposes a method to design a medical simulation and evaluate its effectiveness, with the final aim to achieve the learning outcomes and do not compromise the students' psychological safety. The method has been applied in the design and development of an MR application to simulate the rachicentesis procedure for diagnostic purposes in adults. The MR application has been tested by involving twenty students of the 6th year of Medicine and Surgery of Università Politecnica delle Marche. Multiple measurement techniques such as self-report, physiological indices, and observer ratings of performance, cognitive and emotional states of learners have been implemented to improve the rigour of the study. Also, a user-experience analysis has been accomplished to discriminate between two different devices: Vox Gear Plus® and Microsoft Hololens®. To compare the results with a reference, students performed the simulation also without using the MR application. The use of MR resulted in increased stress measured by physiological parameters without a high increase in perceived workload. It satisfies the objective to enhance the realism of the simulation without generating cognitive overload, which favours productive learning. The user experience (UX) has found greater benefits in involvement, immersion, and realism; however, it has emphasized the technological limitations of devices such as obstruction, loss of depth (Vox Gear Plus), and narrow FOV (Microsoft Hololens).

In the era of the fourth industrial revolution, human has still a central role. Manufacturing industries have to deal with human sustainability in order to guarantee workers’ health and well-being. Several studies have proved the importance of ergonomics in workplace design and the benefits related to the adoption of the human-centered approach. The enabling technologies of Industry 4.0 are changing the role of the operator and can support him from a physical and cognitive point of view. On the other hand, companies are increasingly implementing lean philosophies, such as World Class Manufacturing, to maintain their competitiveness by reducing wastes and costs. However, the need arises for a comprehensive methodology to support the design of manufacturing equipment considering human factors by integrating Industry 4.0 technologies and World Class Manufacturing elements. It aims at improving both ergonomic and efficiency aspects of the workstation. The proposed methodology allows identifying and in-depth analyzing the problem, thus finding and implementing a solution that complies with all the requirements and constraints defined. Each step of the methodology can be strengthened by Industry 4.0 technologies. The methodology has been experimented in a real case study with a global company of agriculture and industrial vehicles, leading to the design and implementation of a new equipment. Relevant benefits in terms of ergonomics, efficiency, and process standardization have been achieved.

The work environment influences workers’ well-being and contributes to the growth of personal experiences. In fact, working in an unhealthy workplace can cause stress, frustration, and anxiety. Therefore, companies have to deal with the workers’ well-being in the work environment, making the management of human factors a crucial aspect. In this context, the introduction of Industry 4.0 technologies can support workplace monitoring and improvement. Some researchers propose structured methods that consider several ergonomic domains together; however, it is necessary to create platforms that support data collection, elaboration, and correlation in an integrated way. Accordingly, this paper presents a tool that supports the monitoring of operators’ activities, the data analysis, and the implementation of corrective actions to make the workplace socially sustainable. Preliminary tests were conducted to assess the functionality of the tool architecture and two use cases are presented. They focus on posture analysis and stress detection by inertial sensors and unsupervised machine learning algorithms, respectively.

Design for Manufacturing and Assembly (DfMA) is a consolidated engineering activity that suffers a real integration with 3D CAD systems. DfMA principles are currently applied downstream of the 3D m...

Refrigeration applications is responsible for approximately 17% of the total electricity and around 8% of greenhouse gas emissions. This study presents a comparative life cycle assessment between t...

Engineering design shows a growing interest in exploring cost analysis to anticipate manufacturing issues and integrate production aspects within the product development process. This research aims to highlight key elements (inputs, parameters, models) to accurately predict the cost of a forged part using a complete model, with important information that can be available during the design phase. For this purpose, a systematic literature review of existing engineering methodologies developed for cost analysis of forged parts (i.e., cost estimation, DtC, and ABC) was performed with characterizations of the different approaches for evaluating the most important topics related to this objective. As a result, the most important insights related to the aim of this review are provided: (i) among quantitative methods, analytical and parametric models are the most suitable approaches to develop a cost estimation, (ii) a cost model based on a linear equation supported by single or multiple variables seems to be the most accurate tool to establish a robust cost analysis in the design of forged components, and (iii) input parameters related to the material type and geometrical features are the most critical cost-drivers in the cost assessment. Moreover, this review contributes to identifying emerging applications and obsolete topics, providing the ground to investigate unexplored areas relevant to future research.

Investment casting (IC) is a manufacturing process for making components giving an excellent surface finish, dimensional accuracy, and complex shapes. Despite these benefits, IC requires high cost and lead time for realizing the metallic mold for pattern manufacturing. To avoid the shortcoming of casting processes, Additive Manufacturing (AM) is widely used for fabricating patterns, cores, and molds (Rapid Casting - RC). Several applications concerning rapid investment casting (RIC) through Fused Filament Fabrication (FFF) and Stereolithography (SLA) were developed. However, a techno-economic analysis based on SLA application for RIC is missing. This paper presents a techno-economic analysis regarding SLA adoption for RIC. SLA is compared with wax injection, the traditional approach for realizing wax patterns. The article provides a decision-making tool that supports stakeholders in evaluating when AM is convenient (cost and lead time) for creating wax patterns, considering different production scenarios. For the compared technologies, the authors developed analytical cost models capable of assessing the cost of the entire production process and evaluating the postprocessing phases. The following characteristics were considered: (i) different production volume, (ii) machine load factor, and (iii) parts features (i.e., dimensions and shape). The results show that SLA, for small parts, is more convenient than wax injection when the production volume is higher than 1E+03.

Collaborative robotics is a key pillar of the smart factory of the future making production systems more flexible and responsive. To this aim, the research communities have made considerable efforts to enable direct interaction between humans and robots in a safe and integrated shared workspace. However, the industrial sector still shows a mismatch between the HRC potentialities and the HRC existing applications. The design is often technology-driven, and coexistence prevails on cooperation or collaboration. Through the case study, this article describes the human-driven design approach that a company should follow to define and evaluate different scenarios and choose the one that best suits its context and workforce. It considers safety, ergonomic, technical, spatial, and equipment issues. It presents an application common to all sectors, the packaging, addressing the complexities of the new production paradigm of mass customization. The design approach has been tested by the major Italian kitchen manufacturer and the resulting collaborative workstation has been simulated by using the software Tecnomatix Process Simulate. The simulation allowed the analysis and evaluation of risks, layout, and performance. The simulation results showed significant benefits in terms of efficiency ensuring a safe collaboration.

Background Simulation has been recognized as a shift in health care education that can improve skills and patient safety and outcomes. High-fidelity simulation of critical medical situations can be a source of stress among participants that can interferes with students' abilities leading to unexpected emotional response. In the current study, we sought to evaluate stress responses during a session including a high-fidelity simulation dedicated to the management of an emergency and one performing a complex technique such as spinal tap, in a population of medical students during a session including high-fidelity simulation (HF) dedicated to the management of an emergency and one performing a complex technique such as spinal tap (ST). Methods A quasi-experimental before-after study was used including the administration of questionnaires before and after simulation, and biomarkers evaluation by salivary cortisol samples. A total of 148 students 148 were eligible and agreed to participate in the study. We used paired T-test for mean comparison regarding State-trait anxiety for both HF and ST simulations. For NASA-TLX we performed a T-test mean comparison between groups. We used paired T-test mean comparison for cortisol analysis in the HF simulation. Multivariable linear regression has been used to assess variables associated with AUCg Results values of State-Trait Anxiety Inventory scores were relatively higher at the end of the HF and ST simulation sessions. NASA task load index (NASA-TLX) was significantly higher at baseline for the high fidelity simulations, with respect to the ST simulation. Regarding HF simulation, a T-test for paired samples showed a statically significant difference between cortisol levels at 10 minutes after the start of the training and after 30 minutes from the simulation in both groups. Fold increase was similar in the two groups. Linear regression showed that cortisol AUCg was related to the STAI-Y score in both groups. Conclusions This work highlights how our students developed a stress response, testified by psychological and biological outputs more related to their personal attitude rather than complexity of simulated scenario. More studies are needed to confirm this trend and to clarify the role of simulated stress response in a long-term learning scenario.

Customers are aware of the complexity of supply chains and link this to a high environmental burden. However, if not coupled with quantitative results, this may be overestimated. The present paper proposes a general method used in the specific case of deep environmental evaluation of a swimming goggles distribution. The results are compared to the consumers’ perceptions, collected and analyzed from questionnaires, and the remaining product lifecycle stages, assessed through the life cycle assessment (LCA). The design and prototyping phases take place in Italy, the goods are manufactured in Asia, and further commercialized worldwide. The environmental analysis enabled the identification of the most impacting lifecycle phases. Results highlight that for small products, with a simple use phase like the goggles, the distribution phase retains a small percentage of the overall environmental impact; thus, it is reasonable to simplify the modeling of the supply chain by selecting a limited number of significant scenarios. This work proves how important is to quantify and rationalize the environmental impacts of a complex system. The end of life and transport phases are perceived as high impacting, but the life cycle assessment analysis reveals that the materials and manufacturing phases retain the highest impacts and are the first that should be improved. Consequently, proper dissemination, cooperation, and communication strategies should be carried out by the goggles brand toward suppliers and stakeholders; the company is now studying how to replace virgin materials with recycled content to lower the environmental impact without hampering the product’s performance.

The ever-increasing competitiveness, due to the market globalisation, has forced the industries to modify their design and production strategies. Hence, it is crucial to estimate and optimise costs as early as possible since any following changes will negatively impact the redesign effort and lead time.This paper aims to compare different parametric cost estimation methods that can be used for analysing mechanical components. The current work presents a cost estimation methodology which uses non-historical data for the database population. The database is settled using should cost data obtained from analytical cost models implemented in a cost estimation software. Then, the paper compares different parametric cost modelling techniques (artificial neural networks, deep learning, random forest and linear regression) to define the best one for industrial components.Such methods have been tested on 9 axial compressor discs, different in dimensions. Then, by considering other materials and batch sizes, it was possible to reach a training dataset of 90 records. From the analysis carried out in this work, it is possible to conclude that the machine learning techniques are a valid alternative to the traditional linear regression ones.

De-manufacturing and re-manufacturing are fundamental technical solutions to efficiently recover value from post-use products. Disassembly in one of the most complex activities in de-manufacturing because i) the more manual it is the higher is its cost, ii) disassembly times are variable due to uncertainty of conditions of products reaching their EoL, and iii) because it is necessary to know which components to disassemble to balance the cost of disassembly. The paper proposes a methodology that finds ways of applications: it can be applied at the design stage to detect space for product design improvements, and it also represents a baseline from organizations approaching de-manufacturing for the first time. The methodology consists of four main steps, in which firstly targets components are identified, according to their environmental impact; secondly their disassembly sequence is qualitatively evaluated, and successively it is quantitatively determined via disassembly times, predicting also the status of the component at their End of Life. The aim of the methodology is reached at the fourth phase when alternative, eco-friendlier End of Life strategies are proposed, verified, and chosen.

In the medical education field, the use of highly sophisticated simulators and extended reality (XR) simulations allow training complex procedures and acquiring new knowledge and attitudes. XR is considered useful for the enhancement of healthcare education; however, several issues need further research.The main aim of this study is to define a comprehensive method to design and optimize every kind of simulator and simulation, integrating all the relevant elements concerning the scenario design and prototype development.A complete framework for the design of any kind of advanced clinical simulation is proposed and it has been applied to realize a mixed reality (MR) prototype for the simulation of the rachicentesis. The purpose of the MR application is to immerse the trainee in a more realistic environment and to put him/her under pressure during the simulation, as in real practice.The application was tested with two different devices: the headset Vox Gear Plus for smartphone and the Microsoft Hololens. Eighteen students of the 6th year of Medicine and Surgery Course were enrolled in the study. Results show the comparison of user experience related to the two different devices and simulation performance using the Hololens.

As nearly one third of global energy demand and CO2 emissions are attributable to manufacturing activities, the reduction of energy/resource consumption in the industrial sector is increasingly crucial. Therefore, research and innovation for the factories of the future is not only a matter of developing and integrating new technologies, but also a challenge to make manufacturing less dependent on energy and managed in an optimized way. This requires considering the efficiency of resource exploitation according to a systematic approach. To this aim, the present paper proposes a resource-saving tool, called Resource Value Mapping (RVM), and describes its application in a smart multinational company that produces electromechanical components for the automotive industry. The RVM tool is composed by three main modules that jointly allow the involved stakeholders to collaborate toward the optimization of the plant management: the Cloud data center that represents the repository of the collected real-time and offline data, the Analytics module that is responsible for data elaboration with the aim of calculating a set of key performance indicators useful to identify process inefficiencies, and the Web-based platform that represents the user interface of the tool. The case study demonstrated how such a tool allows (1) mapping the energy/resource flows to multiple levels (machine, line, plant), (2) characterizing them to identify the most critical activities that do not generate value and (3) supporting multiple stakeholders (plant manager, energy manger, operators) in the management of resource anomalies and definition of a more sustainable action plan.

The fashion industry is responsible for a significant contribution to environmental pressure in the European Union. The present study aims to quantify the environmental impacts of a leather shoe pr...

The paper proposes a method to couple manufacturing knowledge in the context of casting with 3D CAD modelling and design. The approach allows formalizing tacit into explicit design knowledge, for aiding engineers during the development of metal casted components. It is based on three main pillars: (i) identification of geometrical features (3D CAD features) and parameters that can cause an issue during the manufacturing process, (ii) definition of a numerical threshold for feature parameters that guarantee the feasibility of the casting process, and (iii) representation of design rules within a CAD system to support product design review. The method is considered the starting point for future developing a software tool (CAD tool plug-in), here just presented. Two case studies are reported with the aim to show the advantages of the proposed method and tool in the identification of manufacturing issues early in the product development process. Results highlight how the CAD-based tool is a useful assistant to avoid design problems related to the metal casting process.

Even in the era of the fourth industrial revolution, companies should necessarily deal with human sustainability, with the aim of improving workers’ health and safety and enhancing their skills. Several studies have faced this issue by proposing approaches or framework to boost the consideration of human factors in the workstation design and support ergonomic evaluations. However, the need arises for a methodology that collects and systematizes existing methods and tools in order to create workplaces that fit to human needs. For this aim, this paper proposes a structured methodology that supports the design and engineering of manufacturing equipment in order to improve workstations in terms of ergonomics and efficiency. It allows identifying a problem, solving it and carrying out a complete and objective evaluation from different perspectives. The methodology has been experimented in a real case study in collaboration with a global manufacturer of agriculture and industrial vehicles. Significant benefits in terms of productivity, process standardization and human factors have been achieved.

The implementation of symbiosis approaches is recognized as an effective industrial strategy towards the optimization of resource exploitation and the improvement of collaboration in the context of Industry 4.0. An industrial system can be considered as a complex environment in which material, energy, machine, and human resources should cooperate towards the improvement of efficiency and the creation of value. According to this vision, the paper presents a detailed literature review about the existing symbiosis approaches: (i) industrial symbiosis models, which mainly aim at the sharing of resources among different companies, and (ii) human symbiosis, which focuses on how to effectively strengthen the synergy among humans and machines. Strengths, weaknesses and correlations among the most common symbiosis approaches are analysed and classified. Finally, the existing symbiosis models are related with the pillars of the Industry 4.0 paradigm, in order to understand what should be the future directions of research in the context of collaborative manufacturing.

This paper presents a novel framework for manufacturing and cost-related knowledge formalization. This artefact allows industries to capitalize the knowledge of experienced practitioners in the field of manufacturing and assembly, so that it can be used by designers for quickly and analytically estimating the production costs of components during product development. The framework consists of the following: (i) a cost breakdown structure used for splitting out the manufacturing cost, (ii) a data model (cost routing) to collect the knowledge required to define a manufacturing process, (iii) a data model (cost model) for collecting the knowledge required to compute the manufacturing cost of each operation within a manufacturing process, and (iv) a workflow to define the manufacturing process. The proposed framework provides several advantages: (i) knowledge formalization of product manufacturing cost, (ii) knowledge sharing among design/engineering departments, and (iii) knowledge capitalization for decision-making process. The proposed framework is used to formalize the knowledge required for analytically estimating the manufacturing cost of open-die forged components. Results highlight that the framework addresses the most important requirements for a knowledge-based cost estimation system.

As life expectancy increases, the likelihood of more falls and fall-related hospitalizations increases with a significant impact on the health system. Given the high incidence of falls in healthy elderly people, in order to prevent them, it is necessary to identify predisposing risk factors, analyze the specific needs of the subjects and use a targeted preventive strategy. This paper investigates the influence of multidimensional health parameters on the fall risk of community-dwelling older people, living in inner areas of Marche Region (Italy). Multidimensional data on the global health of each individual has been collected among several health domains (i.e., mobility, psychological, nutritional, cardiological, social). Statistical analysis has been applied for the assessment of the relationship among the defined health variables and the influence on the fall risk. The binary logistical regression analysis has produced a statistical model with good characteristics of fit and good predictability. The following features have been proven to be strong predictors of fall: female (OR for Gender, 5.526; 95% CI, 1.49–20.53), limited range of movement (OR 3.278; 95% CI, 1.01-10.68), diabetes (OR 4.487; 95% CI, 1.02-19.80), previous syncope (OR 7.686; 95% CI, 1.01-58.55), and body mass index (OR 1.176; 95% CI, 1.03-1.35). Future work will allow the development of a fall prediction index to have a framework of the elder’s global health status and to define a personalized intervention strategy for any adverse event prevention.

The numerousness of over-65 people in the EU-27 population will significantly increase by about 43% by 2050. The main issue is to help aging people stay healthy, active, and integrated into society. In this context, this work aims to develop a novel assistance model, for the elderly’ social and health care, based on a patient-oriented approach. The assistance model is provided through a software platform that integrates three different modules: i) frailty diagnosis and user-product/service matching, ii) users’ monitoring, data acquisition, and telemedicine, iii) organization and management of logistics. This paper focuses on the first module, showing the variables, the logics, and the necessary rules to realise the personalised product-service assignment, within the user-centred method.

The Selective Laser Melting (SLM) is accelerating the adoption of Additive Manufacturing (AM) technologies in the industry. One of the most critical benefits concerns the possibility of manufacturing complex-shaped components, which are not feasible or too expensive using traditional processes. Recent studies are evaluating the SLM manufacturability of closed impellers through laboratory tests. The adoption of numerical simulation models for achieving this goal is still limited due to the complexity of the additive process and the number of phases to be considered. The paper presents a numerical model developed in the ANSYS workbench platform for simulating the SLM process of closed impellers. This work is one of the first studies available in the literature for such a particular kind of components. The paper describes the overall simulation model and the steps required for its definition. Furthermore, boundary conditions and process parameters are provided for a better understanding of the model. A case study illustrates its application to a specific part, intending to evaluate (i) impact between the recoater and the component, (ii) maximum von Mises stress and (iii) maximum displacement during the printing phase and following post-processing. These evaluations will support design and manufacturing engineers during product and process engineering. The promising results of this study are encouraging further research about the application of SLM for closed impellers.

Several implant materials are used in cranial surgery. Still, each one has its drawbacks, such as the risk of infections, low mechanical strength, or low osseointegration. Implants with a porous surface are considered more effective than a smooth and rough coating. The porosity density and structure also influence the mechanical properties of the final implant. Moreover, the implant properties depend on the manufacturing method.

Nowadays, climate change requires companies to reduce their energy consumption and make their production systems more efficient. However, the complexity of the methodologies, the lack of transparency or high efforts (personnel/time) make this challenge especially difficult for SMEs. In this context, the present paper proposes a workflow to supports SMEs in a lean energy analysis. Through the implementation of several methodologies, a comprehensive assessment of energy consumption was carried out. The application to a real case study allowed to identify energy inefficiencies and to evaluate the energy saving and performance improvement actions.

Design for disassembly is a key enabling strategy for the development of new business models based on the Industry 4.0 and circular economy paradigms. This paper attempts to define a method, based on Data Mining, for modelling disassembly data from large amount of records collected through the observation of de-manufacturing activities. The method allows to build a repository to characterize the disassembly time of joining elements (e.g. screws, nuts) considering different features and conditions. The approach was preliminary tested on a sample of 344 records for nuts disassembly retrieved by in-house tests. Disassembly time and corrective factors were assessed including the analysis of probability distribution function and standard deviation for each feature (i.e. disassembly tool).

In the recent years, many methodologies and tools to support the energy efficiency re-design of production systems have been developed, however, they do not investigate the real-time manufacturing process. In this paper, a methodology for energy efficiency re-design of production systems in a context of smart manufacturing is proposed. The continuous production-machine data collection with operator feedbacks enables the creation of a knowledge-based repository that provides useful support during the design of manufacturing systems. A case study in an automotive sector company has allowed to implement the methodology and to assess its effectiveness.

Nowadays, packaging represents around 35% of the total municipal solid waste yearly generated. This paper aims at analyzing a methodology to support the redesign of a sustainable packaging for the household appliances. The approach considers the environmental impacts related to geometrical parameters and materials. In particular, here the test case is focused on the packaging for kitchen hoods. Through the proposed method, based on the use of virtual prototyping tools, an eco-design approach has been identified to analyze the main environmental impacts. A packaging redesign has been performed to reduce waste and increase the use of the components from the perspective of the circular economy. This study has been performed in accordance with the international standards ISO 14040/14044, by using a Life Cycle Assessment (LCA) from Cradle to Gate. The integration with a CAD tool has been considered to redefine the packaging shape, materials, and internal composition, keeping the same standard requirements (performance, security, etc.). LCA software SimaPro 8.5 has been used to carry out the life cycle assessment, and ReCiPe method has been chosen for the life cycle impact assessment (LCIA). A comparison has been proposed between a traditional packaging for household appliances and a new solution which integrates an interior part in molded pulp. The results show the possibility to cut down the environmental impacts of approximately 15% by a redesign with a molded pulp interior and avoiding EPS structures.