EMOGA: a hybrid genetic algorithm with extremal optimization core for multiobjective disassembly line balancing

In a world where products get obsolescent ever more quickly, discarded devices produce million tons of electronic waste. Improving how end-of-life products are dismantled helps reduce this waste, as resources are conserved and fed back into the supply chain, thereby promoting reuse and recycling. This paper presents the Extremal MultiObjective Genetic Algorithm (EMOGA), a hybrid nature-inspired optimization technique for a multiobjective version of the Disassembly Line Balancing Problem (DLBP). The aim is to minimize the number of workstations, and to maximize profit and disassembly depth, when dismounting products in disassembly lines. EMOGA is a Pareto-based genetic algorithm (GA) hybridized with a module based on extremal optimization (EO), which uses a tailored mutation operator and a continuous relaxation-based seeding technique. The experiments involved the disassembly of a hammer drill and a microwave oven. Performance evaluation was carried out by comparing EMOGA to various efficient algorithms. The results showed that EMOGA is faster or gets closer to the Pareto front, or both, in all comparisons

A bi-articular model for scapular-humeral rhythm reconstruction through data from wearable sensors

Patient-specific performance assessment of arm movements in daily life activities is fundamental for neurological rehabilitation therapy. In most applications, the shoulder movement is simplified through a socket-ball joint, neglecting the movement of the scapular-thoracic complex. This may lead to significant errors. We propose an innovative bi-articular model of the human shoulder for estimating the position of the hand in relation to the sternum. The model takes into account both the scapular-toracic and gleno-humeral movements and their ratio governed by the scapular-humeral rhythm, fusing the information of inertial and textile-based strain sensors

Development of a New Digital Signal Processing Platform for the Square Kilometre Array

A novel digital hardware platform has been designed for the Low Frequency Aperture Array (LFAA) component of the Square Kilometre Array (SKA). This board, called Analog Digital Unit (ADU), is a 6U board containing sixteen dual-inputs Analog to Digital Converters (ADC) and two Field Programmable Gate Array (FPGA) devices, capable of digitizing and processing 32 RF input signals. We present the main features of the board and the signal processing firmware that has been developed for LFAA. Although the ADU has been conceived mainly for the low frequency band (50-350 MHz), its use has been proved effective also for higher frequencies (375-650 MHz). In this paper we describe also the application of ADU as the digital acquisition and processing system for PHAROS2, a cryogenically cooled 4-8 GHz Phased Array Feed (PAF) demonstrator. The final part is focused on the future developments of the board

Augmented Reality in Assembly Systems: State of the Art and Future Perspectives

Assembly represents a fundamental step in manufacturing, being a time-consuming and costly process, on which the final quality of the product mostly depends. Augmented Reality (AR) may represent a key tool to assist workers during assembly, thanks to the possibility to provide the user of real-time instructions and information superimposed on the work environment. Many implementations have been developed by industries and academic institutions for both manual and collaborative assembly. Among the most remarkable examples of the last few years are applications in guidance of complex tasks, training of personnel, quality control and inspection. This keynote paper aims to provide a useful survey by reviewing recent applications of AR in assembly systems, describing potential advantages, as well as current limitations and future perspectives

Drilling carbon fiber reinforced plastics with pre-cooling treatment by cryogenic fluid

The high technological properties of carbon fiber reinforced plastics (CFRPs) have led to their ever-increasing use in recent decades for industrial applications, especially in the aerospace and automotive sectors, where the demand for lightweight structures is high. The panels of composite, to be assembled with other materials, may require repeated realization of holes, making the final quality of the union highly dependent on the drilling process. However, the heterogeneity of the composite material may lead to several inconveniencies, such as rapid tool wear and hole delamination, as during machining the drill bit encounters fiber and matrix alternatively, which have different properties. These damages could be reduced through cryogenic drilling, as demonstrated in the related literature, mainly because the machining behavior of composite changes at low temperatures. Starting from this evidence, the present paper proposes an experimental study conducted on CFRP panels using a new methodology of cryogenic drilling, based on the pre-cooling of the composite before starting the machining process, in order to create a uniformly refrigerated material. The analysis is aimed at comparing the hole quality obtained under dry and pre-cooled cryogenic conditions in terms of delamination, surface roughness and dimensional accuracy and at evaluating thrust force, tool wear and the influence of feed rate

Job rotation and human–robot collaboration for enhancing ergonomics in assembly lines by a genetic algorithm

Currently, the largest percentage of the employed workforce in the manufacturing industry is involved in the assembly process, making ergonomics a key factor when dealing with assembly-related problems. During these processes, repetitive tasks and heavy component handling are frequent for workers, who may result overloaded from an energetic point of view, thus affecting several aspects not only relating to the human factor but also to potentially reduced productivity. Different organizational strategies and technological solutions could be adopted to overcome these drawbacks. For these purposes, the present paper proposes a genetic algorithm for solving the typical problem of assembly line balancing, taking into account job rotation and human–robot collaboration for enhancing ergonomics of workers. The objectives of the problem are related to both economic aspects and human factor: (i) the cost for implementing the assembly line is minimized, evaluated on the basis of the number of workers and differentiated by skill levels and on equipment installed on workstations, including collaborative robots, and (ii) the energy load variance among workers is also minimized, so as to smooth their energy expenditure in performing the assigned assembly operations, calculated according to their movements, physiological characteristics, job rotations and degree of collaboration with robots. The paper finally presents and discusses the application of the developed tool to an industrial assembly case

An augmented reality approach for supporting panel alignment in car body assembly

One of the major problems in automotive assembly consists in achieving alignment within the specified tolerances between car panels that make up the exterior bodywork. To reduce errors and time requested to perform the assigned assembly tasks, workers should be guided during these panel fitting operations. Augmented Reality (AR) could be particularly suitable in this regard, as it represents one of the most promising tools to support personnel, with constantly growing applications in production processes. Following this trend, the present work aims to present an AR prototype system for supporting the operator during panel fitting operations of car body assembly, by providing instructions to correct alignment errors in terms of gap and flushness. A real case study concerning the fine alignment of car body panels with respect to the front light projector is also presented

Designing assembly lines with humans and collaborative robots: A genetic approach

Human-robot collaboration represents a significant evolutionary step in manufacturing. A crucial point is to establish a proper task assignment to combine robot productivity with human flexibility. In this regard, this paper proposes a genetic algorithm to approach the Assembly Line Balancing Problem (ALBP) in the case of human-robot collaborative work. The aim is the minimization of: i) the assembly line cost, evaluated according to the number of workers and equipment on the line, including collaborative robots, ii) the number of skilled workers on the line, iii) the energy load variance among workers, based on their energy expenditures and thus on their physical capabilities and on the level of collaboration with robots