This paper reviews the literature on the emerging digital technologies of Industry 4.0 (I4.0) focussed on the applicability of the Internet of Things (IoT), Virtual Reality (VR) and Augmented Reality (AR) in remanufacturing. Inspired by the frameworks developed to support exploration and realisation of I4.0 technologies for disassembly, the paper discusses the same emerging technologies in the wider context of remanufacturing. Trends and gaps have been identified from a value-creation perspective that encompasses the product to be remanufactured, the remanufacturing equipment and processes adopted and related organisation issues. Findings suggest there is a need to explore the connection of cyber-physical systems to the IoT to support smart remanufacturing, whilst aligning with evolving information and communication infrastructures and circular economy business models. The review highlights twenty-nine research topics that require attention to support this field.

The problem of determining energy optimal walking motions for a bipedal walking robot is considered. A full dynamic model of a planar seven-link biped with feet is derived including the effects of impact of the feet with the ground. Motions of the hip and feet during a regular step are then modelled by 3rd order polynomials, the coefficients of which are obtained by numerically minimising an energy cost function. Results are given in the form of walking profiles and energy curves for the specific cases of motion over level ground, motion up and down an incline, and varying payload.

No abstract

No abstract

Remanufacturing has gained increasing attention due to its economic, environmental and societal benefits as well as its contribution to the sustainability of natural resources. Disassembly is the first and usually the most difficult process in remanufacturing a product. Disassembly sequence planning, which aims to find the optimal disassembly sequence, is required to improve efficiency and reduce cost. There have been many investigations in this field and several disassembly sequence planning methods have been developed. This article reviews the main existing disassembly sequence planning methods from the perspectives of disassembly mode, disassembly modelling and planning method. The characteristics of different methods are analysed and summarised from those perspectives. Future trends in disassembly sequence planning are also discussed to reveal gaps in existing research.

This paper presents a novel version of the bees algorithm. This version is characterized by an extended set of search operators, and a mechanism that protects the most recently generated solutions from competition with more evolved individuals. Compared to the standard implementation of the bees algorithm, the new procedure requires the selection of an additional set of parameters. A new statistical method is proposed to tune these extra parameters. The proposed tuning method was used to determine a unique set of learning parameters for the modified bees algorithm on eight popular function optimization benchmarks. When tested against the standard bees algorithm and two other well-known optimization procedures, the new algorithm attained top performances on nearly all the benchmarks. The experimental results also proved that, tested on a search space much larger than that where it was tuned, the modified bees algorithm still outperformed the standard method, and the degradation of the performance of the two algorithms was comparable. These results prove the effectiveness of the modified bees algorithm, and show that the proposed tuning procedure is a valuable alternative to the complex and subjective trial-and-error methods that are often used.

No abstract

No abstract