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The purpose of this review is to comprehensively consider the material properties and processing of additive titanium alloy and provide a new perspective for the robotic grinding and polishing of additive titanium alloy blades to ensure the surface integrity and machining accuracy of the blades.

At present, robot grinding and polishing are mainstream processing methods in blade automatic processing. This review systematically summarizes the processing characteristics and processing methods of additive manufacturing (AM) titanium alloy blades. On the one hand, the unique manufacturing process and thermal effect of AM have created the unique processing characteristics of additive titanium alloy blades. On the other hand, the robot grinding and polishing process needs to incorporate the material removal model into the traditional processing flow according to the processing characteristics of the additive titanium alloy.

Robot belt grinding can solve the processing problem of additive titanium alloy blades. The complex surface of the blade generates a robot grinding trajectory through trajectory planning. The trajectory planning of the robot profoundly affects the machining accuracy and surface quality of the blade. Subsequent research is needed to solve the problems of high machining accuracy of blade profiles, complex surface material removal models and uneven distribution of blade machining allowance. In the process parameters of the robot, the grinding parameters, trajectory planning and error compensation affect the surface quality of the blade through the material removal method, grinding force and grinding temperature. The machining accuracy of the blade surface is affected by robot vibration and stiffness.

This review systematically summarizes the processing characteristics and processing methods of aviation titanium alloy blades manufactured by AM. Combined with the material properties of additive titanium alloy, it provides a new idea for robot grinding and polishing of aviation titanium alloy blades manufactured by AM.

This article sets out to show how principles and questions about method that underlie a way of interpreting the discipline of conservation and restoration can find results in research and studies, aiming at achieving even conscious reuse process. The occasion is the very recent research performed on the former Church of Saints Gerolamo and Francesco Saverio in Genoa, Italy, the Jesuit church annexed to the 17th-century College of the order. It is a small Baroque jewel in the heart of the ancient city, former University Library and actually abandoned, forgotten for years, inaccessible and awaiting a new use.

The two-year work carried out on the monumental building was conducted according to a study and research methodology developed and refined over the years within the activities of the School of Specialisation in Architectural Heritage and Landscape of the University of Genoa. It is a multidisciplinary and rigorous approach, which aims to train high-level professionals, up-to-date and aware of the multiple problems that interventions on existing buildings, especially of a monumental nature, involve.

The biennal study has been carried out within the activities of the Post-Graduate Programme in Architectural Heritage and Landscape of the University of Genoa. The work methodology faces the challenges of the contemporary complexity, raised by the progressive broadening of the concept of cultural “heritage” and by the problems of its conservation, its active safeguard and its reuse: safety in respect of seismic risk, fire and hydro geological instability, universal accessibility – cognitive, physical and alternative – resource efficiency, comfort and savings in energy consumption, sustainability, communication and involvement of local communities and stakeholders.

The goals of the work were the following: understanding of the architectural heritage, through the correlated study of its geometries, elements and construction materials, surfaces, structures, spaces and functions; understanding of the transformations that the building has undergone over time, relating the results of historical reconstructions from indirect sources and those of direct archaeological analysis; assessment of the state of conservation of the building recognising phenomena of deterioration, damage, faults and deficits that affect materials, construction elements, systems and structures; identification of the causes and extent of damage, faults and deficits, assessing the vulnerability and level of exposure of the asset to the aggression of environmental factors and related risks; evaluation of the compatibility between the characteristics of the available spaces, the primary needs of conservation, the instance of regeneration and possible new uses; the definition of criteria and guidelines for establishing the planning of conservation, restoration and redevelopment interventions.

With the help of basic physics, the application of computer algorithms in the form of recent advances such as machine learning and neural networking in textile Industry has been discussed in this review. Scientists have linked the underlying structural or chemical science of textile materials and discovered several strategies for completing some of the most time-consuming tasks with ease and precision. Since the 1980s, computer algorithms and machine learning have been used to aid the majority of the textile testing process. With the rise in demand for automation, deep learning, and neural networks, these two now handle the majority of testing and quality control operations in the form of image processing.

The state-of-the-art of artificial intelligence (AI) applications in the textile sector is reviewed in this paper. Based on several research problems and AI-based methods, the current literature is evaluated. The research issues are categorized into three categories based on the operation processes of the textile industry, including yarn manufacturing, fabric manufacture and coloration.

AI-assisted automation has improved not only machine efficiency but also overall industry operations. AI's fundamental concepts have been examined for real-world challenges. Several scientists conducted the majority of the case studies, and they confirmed that image analysis, backpropagation and neural networking may be specifically used as testing techniques in textile material testing. AI can be used to automate processes in various circumstances.

This research conducts a thorough analysis of artificial neural network applications in the textile sector.