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Today, the improvement of the product value in consumer goods, such as new services to increase the positive customer experience, is the subject of many research activities. In a context where the product complexity becomes ever greater and the product life-cycle is always shorter, the use of intelligent tools for supporting all phases of the product life-cycle is very important. One of the aspects that is taking interest is to support the consumer in fault management. This analysis are well-known practices in the industrial, automotive fields, etc. but less used for consumer electronics. This paper analizes a Cloud service based on a Machine Learning (ML) approach used to provide fault detection capabilities to household appliances equipped with electric motors and compare the results with on premise ML algorithms provided research tools. The purpose of this paper is to perform a preliminary comparison of ML algorithm performances provided by two software, namely Microsoft Azure (cloud solution) and MATLAB (on premise solution), on a study case. In detail, the vibration data of an asynchronous motor installed in an oven extractor hood for commercial restaurant kitchen have been analyzed. To this end, two classification algorithms have been selected to implement fault diagnosis techniques.

The paper describes an approach for analyzing the use of a Laser-Guided Vehicle (LGV) in the context of the small and medium-sized enterprise. The use of LGVs is an efficient solution to provide more flexibility in the context of Just-In-Time production; however, the investment cost can limit this application. A methodology has been proposed in this work to analyze the technical feasibility of using an LGV in the manufacturing industry of customized products. The test case focuses on the study of a laser-guided system to optimize the handling of molds for customized production. In this scenario, an LGV is proposed to substitute manual carts used for moving molds from the warehouse to the injection machines. The traditional path included an intermediate station for pre-heating the molds in hot-air ovens. The proposed solution includes the study of an induction heating system on the LGV to optimize time and energy consumption.

Molding is one of the most widely used processing technologies in manufacturing. Among typical molding parameters, the mold temperature is a critical one for the quality of the molding process. A solution to this issue can be the employment of induction heating which, through a high-frequency electromagnetic field, produces eddy currents and a consequent rapid heating of the material into the cavity of the mold. The necessity to maintain the mold walls at the operative temperature makes the induction heating to be one of the most efficient non-contact means of heating. In fact, induction heating is characterized by quickness, efficiency, and energy saving; however, the design and the sizing of an induction heating system is complex due to different parameters involved in the electromagnetic and thermal phenomena. In this context, the paper aims to define a methodology to support engineers in the design and sizing of an induction heating system for molds, taking as case study a mold for composite parts. A model-based approach is proposed to analyze and simulate the mold heating, considering three different levels of modelling: Analytical (0D), Finite-Difference Methods (2D) and Finite Element Methods (3D). The Analytical approach investigates the solution of the physical equations applied to the volume of the material involved. Instead, the Finite-Difference approach (2D) solves the heat transfer problem by discretizing the domain and by solving for temperature at discrete points. Finally, the Finite Element method (3D) solves partial differential equations on a 3D discretized domain.

The fourth industrial age takes the manufacturing factory to a new level by introducing smart, extendible, flexible, modular and customized mass production technologies. Production lines or machines need to be integrated at the management level to be industry 4.0 compliant: in this way they can create and optimize a customer-oriented production, while constantly maintaining good performance conditions. In this context, one of the main challenges is the possibility to detect faults as fast as possible, to accurately diagnose those faults which can negatively affect the overall production cycle, and finally address them before it is too late. Due to the great importance that electric motors play in this context, an online smart algorithm for fault detection in electric motors is proposed in this paper. The effectiveness of the proposed method has been validated by applying it on an experimental benchmark, where the results show that the method is accurate and fast in detection of faults.

Nowadays, although the lithium-ion batteries have been widely applied in the context of electric vehicles for passengers, lead-acid batteries are still prevalent in motive-power applications, such as electric pallet jacks and laser guided vehicles. The battery cost is the main disadvantage that limits the employment of lithium-ion solutions in such applications. Several strategies for reducing the battery life cycle cost have been discussed in the scientific literature. The opportunity charging is one of them, even though it is suitable only for batteries having high lifecycles and high charging/discharging rates, such as the Lithium Titanium Oxide ones. This paper aims at assessing a feasible solution to reduce the life cycle cost of the energy storage units for laser guided vehicles. A tool has been proposed to analyze the Total Cost of Ownership of batteries, under the adoption of an opportunity charging strategy. Simulations of energy consumption have also been included, to predict the battery cycles and the operation costs. The life cycle analysis has investigated the use of a compacted Lithium Titanium Oxide battery in comparison with a traditional lead-acid battery. The results have shown the feasibility of the Lithium Titanium Oxide solution and its economic advantage in an industrial context.