Systematic integration of 2D and 3D sources for the virtual reconstruction of lost heritage artefacts: the equestrian monument of Francesco III d’Este (1774–1796, Modena, Italy)

The role of 3D virtual reconstruction of lost heritage artefacts is acquiring ever-greater importance, as a support for archaeological research and art history studies, as well as a vehicle for the cultural and evocative involvement of the end-user. The main risk of virtual reconstruction is the lack of a faithful restitution but, conversely, very often the artefact conservation state does not allow a complete 3D reconstruction. Therefore, 2D sources, both textual and iconographic, represent a precious integration and completion of the existing 3D sources. This paper proposes an operating systematic workflow to integrate retrieved 2D and 3D sources and assess their compatibility for the virtual reconstruction of lost heritage artefacts using and integrating 3D survey and digital modelling. As a case study, we virtually reconstructed the lost equestrian monument of Duke Francesco III d'Este, 7 m high, built in 1774 in Modena, Italy, by the sculptor Giovanni Antonio Cybei and completely destroyed a little over 20 years later during the revolutionary uprisings. Following the proposed workflow, we integrate data coming from: a still preserved preparatory stucco model, paintings and engravings showing the missing details of the 3D model, a series of urban views returning the proportion and positioning of the monument (statue, pedestal and base), a fragment of the right foot providing the statue size and the appearance of the original white Carrara marble. The final 3D digital model shows a faithful correspondence to the 2D sources and guarantees an effective user’s fruition thanks to dedicated virtual applications. Besides the scientific and cultural goal, we highlight the evocative role of this work, which has contributed to the restitution of a monument that is unknown to most citizens and visitors

ADDITIVE MANUFACTURING TECHNOLOGIES FOR SUSTAINABLE-INTELLIGENT STRUCTURES: A NEW CONCEPT OF MULTIDIMENSIONAL PRINTING

In recent years, additive manufacturing has become a disruptive technology for its ability to build structures with complex geometries. In the future, additive manufacturing will combine multidimensional printing technology with the use of programmable-smart materials, achieving higher levels of structural freedom, sustainability and efficiency in advanced manufacturing processes, ultimately driving the social, economic and environmental impact of this technology

Computer-Aided Tolerancing Analysis of a High-Performance Car Engine Assembly

This paper proposes the analysis of the tolerances (values, types, datum) and their effects on a mechanical assembly, as a high-performance car engine, by means of a Computer-Aided Tolerancing software. The 3D tolerance stack-ups are investigated to assess the fulfillment of the functional requirements as well as the performance specifications of the assembly. Moreover, after identifying the tolerances that mainly affect the product variability, we finally propose some corrective actions on the tolerances and assess their functional allocation, tightening or relaxing their values, ensuring assemblability and cost reduction

A Review of Automotive Spare-Part Reconstruction Based on Additive Manufacturing

In the Industry 4.0 scenario, additive manufacturing (AM) technologies play a fundamental role in the automotive field, even in more traditional sectors such as the restoration of vintage cars. Car manufacturers and restorers benefit from a digital production workflow to reproduce spare parts that are no longer available on the market, starting with original components, even if they are damaged. This review focuses on this market niche that, due to its growing importance in terms of applications and related industries, can be a significant demonstrator of future trends in the automotive supply chain. Through selected case studies and industrial applications, this study analyses the implications of AM from multiple perspectives. Firstly, various types of AM processes are used, although some are predominant due to their cost-effectiveness and, therefore, their better accessibility and wide diffusion. In some applications, AM is used as an intermediate process to develop production equipment (so-called rapid tooling), with further implications in the digitalisation of conventional primary technologies and the entire production process. Secondly, the additive process allows for on-demand, one-off, or small-batch production. Finally, the ever-growing variety of spare parts introduces new problems and challenges, generating constant opportunities to improve the finish and performance of parts, as well as the types of processes and materials, sometimes directly involving AM solution providers

Simulation-Based Design of Reconfigurable Moulds for Injection Overmoulding

The injection moulding process enables the production of complex shaped parts, thanks to the accurate kinematics and the tight tolerances of the mould. This process is suitable for large batch production, leading to reduced single part costs, but involves high initial investments. The life of a mould can be increased by exploiting reconfigurable cavity inserts. So, a design method has been conceived for reconfigurable injection moulds by integrating Design for Assembly and Computer Aided Engineering techniques. From the early phases of a systematic design approach, the simulation models are configured with the different geometries as requested by design specifications. The mould inserts are designed with standard features in order to be quickly changed. A case study on a reconfigurable mould for the overmoulding of polymer wheels to be produced in different sizes is presented. The simulations with Moldex3D software are finally compared with the experimental data from the actual production

Learning the noise fingerprint of quantum devices

Noise sources unavoidably affect any quantum technological device. Noise's main features are expected to strictly depend on the physical platform on which the quantum device is realized, in the form of a distinguishable fingerprint. Noise sources are also expected to evolve and change over time. Here, we first identify and then characterize experimentally the noise fingerprint of IBM cloud-available quantum computers, by resorting to machine learning techniques designed to classify noise distributions using time-ordered sequences of measured outcome probabilities.Comment: 20 pages, 3 figures, 5 tables, research articl

The integration of morphological design and topology optimization to enhance the visual quality of electricity pylons

Purpose: This paper aims to enhance the visual quality of artificial above-ground structures, like pylons, masts, and towers of infrastructures and facilities, through a systematic design method for their morphological and structural optimization. Design/methodology/approach: The method achieves the functional and aesthetic goals based on the application of computer-aided tools. In particular, this is achieved according to three key steps: • Morphological development of landscape-related symbolism, environment, or culture and social needs. • Topology optimization of the design concept to reduce the structural weight and its visual impact. • Engineering of the resulting optimized structure. Practical implications: As a case study, the method is used for designing electricity pylons for the coastal territory of a Mediterranean European country, such as Italy. Citizens were involved during the identification phase of a symbolic shape for the concept development and during the final assessment phase. Research limitations/implications: The engineering phase has been performed by assembling standard lattice components with welded connections. Even if the use of this truss-like structure should lead to a minimum cost, the developed structure employs an additional 15%–20% of trusses and sheet metal covers the final cost is higher than a standard lattice pylon. Findings: The result is a structure with enhanced visual quality according to the international guidelines and fully complying with mandatory and functional requirements, such as regulatory and industrial feasibility, as well as those arising from social components. Originality/value: The method shows its potential in defining a custom design for lightweight structures with enhanced visual quality regarding the critical situation discussed here. The method considers both the subjective perception of citizens and their priorities and the landscape where the structures will be installed

Classification of geometric forms in mosaics using deep neural network

The paper addresses an image processing problem in the field of fine arts. In particular, a deep learning-based technique to classify geometric forms of artworks, such as paintings and mosaics, is presented. We proposed and tested a convolutional neural network (CNN)-based framework that autonomously quantifies the feature map and classifies it. Convolution, pooling and dense layers are three distinct categories of levels that generate attributes from the dataset images by introducing certain specified filters. As a case study, a Roman mosaic is considered, which is digitally reconstructed by close-range photogrammetry based on standard photos. During the digital transformation from a 2D perspective view of the mosaic into an orthophoto, each photo is rectified (i.e., it is an orthogonal projection of the real photo on the plane of the mosaic). Image samples of the geometric forms, e.g., triangles, squares, circles, octagons and leaves, even if they are partially deformed, were extracted from both the original and the rectified photos and originated the dataset for testing the CNN-based approach. The proposed method has proved to be robust enough to analyze the mosaic geometric forms, with an accuracy higher than 97%. Furthermore, the performance of the proposed method was compared with standard deep learning frameworks. Due to the promising results, this method can be applied to many other pattern identification problems related to artworks

Noise sensing via stochastic quantum Zeno

The dynamics of any quantum system is unavoidably influenced by the external environment. Thus, the observation of a quantum system (probe) can allow the measure of the environmental features. Here, to spectrally resolve a noise field coupled to the quantum probe, we employ dissipative manipulations of the probe, leading to so-called Stochastic Quantum Zeno (SQZ) phenomena. A quantum system coupled to a stochastic noise field and subject to a sequence of protective Zeno measurements slowly decays from its initial state with a survival probability that depends both on the measurement frequency and the noise. We present a robust sensing method to reconstruct the unkonwn noise power spectral density by evaluating the survival probability that we obtain when we additionally apply a set of coherent control pulses to the probe. The joint effect of coherent control, protective measurements and noise field on the decay provides us the desired information on the noise field