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Nowadays, electromagnetic high-frequency induction is very used for different non-contact heating applications such as the molding process. Every molding process requires the preheating and the thermal maintenance of the molds, to enhance the filling phase and the quality of the final products. In this context, an induction heating system, mostly, is a customized equipment. The design and definition of an induction equipment depends on the target application. This technology is highly efficient and performant, however it provides a high-energy consumption. Therefore, optimization strategies are very suitable to reduce energy cost and consumption. The proposed paper aims to define a method to optimize the induction heating of a mold in terms of time, consumption, and achieved temperature. The proposed optimization method involves genetic algorithms to define the design parameters related to geometry and controller. A test case describes the design of an induction heating system for a polyurethane molding process, which is the soles foaming. This case study deals with the multiobjective optimization of parameters such as the geometrical dimensions, the inductor sizing, and the controller setting. The multi-objective optimization aims to reduce the energy consumption and to increase the wall temperature of the mold.

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.

The pillars of Industry 4.0 require a modern smart factory to be integrated, store data into the Cloud, access the Cloud for data analytics and share information at software level for simulation and Hardware-In-the-Loop capabilities. The resulting Cyber-Physical System is often called Cyber-Physical Manufacturing System, and it becomes fundamental to cope with the increased system complexity and the desired performances. However, since a lot of old production systems are based on monolitic architectures, with limited external communication ports and reduced local computational capabilities, it is very difficult to make such production lines compliant to Industry 4.0 pillars. Wireless Sensor Network is a solution for the smart connection of a production line to a Cyber-Physical System architecture, data processing through Cloud Computing. The scope of this research work is to propose an intermediate layer within the architecture that allows each device, production line and machine to be independently connected despite the adopted protocol. The solution is based on OSGi Framework, which is able to seamlessly integrate both hardware and software wireless sensors, send data into the Cloud for further data analysis, and grant both Hardware-In-the-Loop and Cloud Computing capabilities. A general description of the architecture is here proposed, together with preliminary results on a real manufacturing line for data collection and analysis over a period of two months.

Mold heating is an important key factor for the mold cycle which affects the quality of the molded product. The involved molding processes regards polymers foaming, plastics injection, or resin-curing with composites. While the manufacturing cycle of plastics injection molding requires a heating/cooling system, other processes do not require the cooling phase. Polymers foaming and resin-curing require specific values of temperature to provide full chemical reactions and a good product quality on the surface. Induction heating systems are suitable for such applications; however, many industrial cases require customized solutions to support the molding cycle of different parts. A temperature control is always mandatory to reduce the energy cost and increase the heating efficiency. This paper studies a molds temperature control applied in the polyurethane foaming of footwear soles. The proposed induction heating system and its control have been studied using the Hardware-In-the-Loop simulations. Due to a high mold thermal inertia, which increases the mold temperature even if the control system turns off the thermal power, tailored controllers have been analyzed to achieve the desired temperature set-point. The thermal model of the foaming mold and the induction heating system have been modelled and developed in the MATLAB/Simulink® framework. An ATMEGA processor was used to implement and test a discrete PID controller while Simulink induction-heating system model was running, creating an Hardware-In-The-loop platform.

In this work, a PI-Sliding mode controller is developed in feedback loop from a non-inverting buck boost converter, in order to control the recharge of a Li-Ion battery of a Laser Guided Vehicle (LGV), which is the last stage of an industrial wireless recharge power station. In detail, the industrial case study regards the wireless recharge of a LGV employed to transport molds used for footwear soles production. The proposed solution has been modelled and developed in the MATLAB/Simulink® framework, and the performances of this controller are compared with the classical PID industrial controller. The obtained results depend only from the developed recharge system and can be applied in other industrial cases (e.g., car battery recharge). In particular, the numerical simulations show that the proposed solution has an important impact on the energy consumptions.

Induction cooktops are very efficient systems, but, their energy consumption should be reduced using a temperature controller for optimizing the electrical power. Such controllers are already widely spread in several applications (air conditioning, ovens, etc.). Induction cooktops work with discrete levels of power, and, therefore, the user can select and modify the requested power level during the cooking. This paper presents the Hardware-In-the-Loop simulation to develop an active temperature controller, which optimizes the energy management of the water boiling using an induction cooktop. A thermal and induction model has been developed in MATLAB/Simulink® framework, while a discrete PID controller has been implemented inside a physical ATMEGA processor and tested within the Hardware-In-the-Loop platform.

The pillars of Industry 4.0 require the integration of a modern smart factory, data storage in the Cloud, access to the Cloud for data analytics, and information sharing at the software level for simulation and hardware-in-the-loop (HIL) capabilities. The resulting cyber-physical system (CPS) is often termed the cyber-physical manufacturing system, and it has become crucial to cope with this increased system complexity and to attain the desired performances. However, since a great number of old production systems are based on monolithic architectures with limited external communication ports and reduced local computational capabilities, it is difficult to ensure such production lines are compliant with the Industry 4.0 pillars. A wireless sensor network is one solution for the smart connection of a production line to a CPS elaborating data through cloud computing. The scope of this research work lies in developing a modular software architecture based on the open service gateway initiative framework, which is able to seamlessly integrate both hardware and software wireless sensors, send data into the Cloud for further data analysis and enable both HIL and cloud computing capabilities. The CPS architecture was initially tested using HIL tools before it was deployed within a real manufacturing line for data collection and analysis over a period of two months.

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.

In this research, a localization system for a particular Automated Guided Vehicle (AGV), namely a Laser Guided Vehicle (LGV), which takes advantage of RSSI data, is described. The vehicle, its sensors and the wireless sensor network are virtualized, in order to provide a software replica of that available in a real factory. A Co-Simulator, composed by two simulation environments, has been implemented: from one hand, Robot Operating System (ROS), the RVIZ and GAZEBO for modeling and simulating the virtual system, and from the other hand, COOJA for reproducing the Wireless Sensor Network (WSN) behavior and RSSI signals. The virtualized LGV in ROS, together with its virtual sensors set, is integrated with the WSN in COOJA, so that localization can be performed by sensor fusion of odometric and RSSI data according to a Kalman Filter approach. The Co-simulator can be used to deploy path-tracking policies and test them before actual implementation in a Cyber Physical System (CPS) scenario.

Induction cooktops are very used as an efficient alternative to traditional cooking systems such as gas hobs. Even if the energy efficiency of induction cooktops is twice as much as the traditional gas burners, the differences in terms of energy price can limit the diffusion and marketing of induction cooktops. Recent eco-normative are regulating the energy consumption of this kind of household appliances sold in different countries. This paper proposes an automatic temperature control to reduce the energy consumption for induction cooktops. The water boiling test was used as case study to simulate and validate the proposed approach.

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.

In the production of plastic components based on injection molding, like shoe sole manufacturing, the temperature control and the on-line process optimization are important issues in order to preserve the quality of the plastic components and improve the time performance, while maintaining high product quality. This research proposes an induction preheating control technique based on Model Predictive Controller (MPC) for a steel-aluminium mold for production of soles, performed on an automated Laser Guided Vehicle (LGV) with an innovative induction heating functionality. A thermal model has been studied using a finite-difference approach to describe the mold heating system. The resulting system has been simulated using Simulink/MATLAB. Then, three types of controllers have been modelled in the proposed simulation workflow, in order to compare the different behavior of the system. Due to the high mold thermal inertia, which increases the mold temperature even if the control system turns off the thermal power, innovative controllers are needed in order to track the desired temperature setpoint. The comparison with standard industrial controllers, based on PI and PID controllers, shows the effectiveness of proposed solution.

In recent years, the Cyber-Physical Systems (CPSs), have become a new trend to increase and to enrich the interactions between physical and virtual systems with the goal to create a truly connected world in which smart objects interact and exchange data with each other. The CPS is the core of the new industrial revolution called “Industry 4.0”, which promotes the computerization of manufacturing to make decentralized decisions. Within the modular structured smart factories, Cyber-Physical Systems monitor physical processes, create a virtual copy of the physical world, simulate parts of process and implement sophisticated control policies in order to take optimized decisions. This research proposes the modelling and simulation of an induction heating process for aluminium-steel mold, which is used in the production of footwear soles. The modelling supports the simulation of a CPS model related to the use of a multi-use LGV (Laser Guided Vehicle) which transports aluminum-steel molds from a mechanized warehouse to the final rotary production line, used for the soles foaming. In detail, a thermal model and an induction heating electronic circuit model have been studied to describe the whole mold heating system and they have been simulated using Simulink/MATLAB. In addition, two types of controllers, an induction preheating control technique based on a Model Predictive Controller (MPC), and another one based on PID, have been developed in order to analyse the different behaviour of the system.