The present globalised market is forcing many companies to invest in new strategies and tools for supporting knowledge management. This aspect is becoming a key factor in the industrial competitiveness for the presence of extended enterprises that normally deal with huge data exchange and share processes. This scenario is due to the presence of partners, geographically distributed over the entire globe, that participate, in different steps of the product lifecycle, to product development, maintenance and recycling. At present Product Lifecycle Management (PLM) seems to be the right solution for supporting enterprises in this complex scenario, even if a real standardised approach for the implementation of knowledge sharing and management tools doesn't exist. For this reason the aim of this paper is to develop a knowledge management strategy able to support the formalisation and the reuse of the enterprise expertise acquired working on previous products. Focusing the attention on consumer packaged goods enterprises the strategy has developed a systematic a methodology integrating the Quality Function Deployment (QFD) and the Teoriya Resheniya Izobreatatelskikh Zadatch (TRIZ) for supporting the knowledge codification and management. A case of study, with the intent to solve the problem of waste disposal, has been conducted to validate the proposed methodology.

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In recent years, bone fracture detection and classification has been a widely discussed topic and many researchers have proposed different methods to tackle this problem. Despite this, a universal approach able to classify all the fractures in the human body has not yet been defined. We aim to analyze and evaluate a selection of papers, chosen according to their representative approach, where the authors applied different deep learning techniques to classify bone fractures, in order to select the strengths of each of them and try to delineate a generalized strategy. Each study is summarized and evaluated using a radar graph with six values: area under the curve (AUC), test accuracy, sensitivity, specificity, dataset size and labelling reliability. Plus, we defined the key points which should be taken into account when trying to accomplish this purpose and we compared each study with our baseline. In recent years, deep learning and, in particular, the convolution neural network (CNN), has achieved results comparable to those of humans in bone fracture classification. Adopting a correct generalization, we are reasonably sure that a computer-aided diagnosis (CAD) system, correctly designed to assist doctors, would save a considerable amount of time and would limit the number of wrong diagnoses.

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The variational analysis of compliant assemblies is mainly based on linear elastic models. Some guidelines have been defined to integrate material plasticity into a tolerance analysis model in order to improve its results when considering the resistance spot welding (RSW) process. A finite element model that simulates the body-in-white and RSW processes has been applied to butt and slip joints, with parts subjected to dimensional and geometrical tolerances that cause gap mismatching condition and loading interference on fixtures. The dimensional quality of assemblies is affected by plasticization near the welding spot, at the base of welded flanges and near the locators. The springback evidenced relative rotation of parts.

Recently a wide variety of applications has been developed integrating 3D functionalities. Advantages given by the possibility of relying on depth information allows the developers to design new algorithms and to improve the existing ones. In particular, for what concerns face morphology, 3D has led to the possibility to obtain face depth maps highly close to reality and consequently an improvement of the starting point for further analysis such as Face Detection, Face Authentication, Face Identification and Face Expression Recognition. The development of the aforementioned applications would have been impossible without the progress of sensor technologies for obtaining 3D information. Several solutions have been adopted over time. In this paper, emphasis is put on passive stereoscopy, structured light, time-of-flight (ToF) and active stereoscopy, namely the most used technologies for the cameras design and fulfilment according to the literature. The aim of this article is to investigate facial applications and to examine 3D camera technologies to suggest some guidelines for addressing the correct choice of a 3D sensor according to the application that has to be developed.

Design and manufacturing of composite structures are driving the next generation innovation cycles for the aerospace, automotive and energy markets. Automated fiber placement (AFP) is quickly becoming the preferred manufacturing method of those structures as it offers manufacturing automation, reduces production cycle times, and decreases human induced errors. One of the major steps towards manufacturing structures with AFP technology is the selection of the optimal layup strategy. This is limited by, not only geometrical and process parameters, but certification allowable and guidelines. This paper outlines a systematic review of the multiple layup strategies practices currently used and/or investigated for the AFP manufacturing process. The optimal layup strategy needs to be selected and verified to obtain laminates with little to no manufacturing defects. Through a methodical description, the different layup strategies found in the literature are described as well as their mathematical implementation. Following, a geometrical benchmark is presented so that new layup strategies can be compared to others based on the same reference. The article can be the foundation for any new layup strategy investigation.

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