Intelligent Computer-Automated Crane Design Using An Online Crane Prototyping Tool

Abstract

In an accompanying paper submitted concurrently to this conference, we present our first complete version of a generic and modular software framework for intelligent computer-automated product design. The framework has been implemented with a client-server software architecture that automates the design of offshore cranes. The framework was demonstrated by means of a case study where we used a genetic algorithm (GA) to optimise the crane design of a real and delivered knuckleboom crane. For the chosen objective function, the optimised crane design outperformed the real crane. In this paper, we augment our aforementioned case study by implementing a new crane optimisation client in Matlab that uses a GA both for optimising a set of objective functions and for multi-objective optimisation. Communicating with an online crane prototyping tool, the optimisation client and its GA are able to optimise crane designs with respect to two selected design criteria: the maximum safe working load and the total crane weight. Our work demonstrates the modularity of the software framework as well as the viability of our approach for intelligent computer-automated design, whilst the results are valuable for informing future directions of our research.

Grey wolf optimizer (GWO) for automated offshore crane design

“…The GUI also provides a simple visualization of the designed crane and its 2D workspace safe working load (SWL) chart [5]. Additionally, the MCOC uses various optimization algorithms for optimizing the design parameters in a manner that achieves the crane's desired design criteria [7][8]. Each KPI is typically related to overall performance, weight and cost of the designed crane.…”

“…This large number of parameters makes the space of all possible combinations of parameter values very large and a manual trial-and-error approach will necessarily be cumbersome and running the risk of suboptimal designs. Previously [7][8], we used the Matlab ® Global Optimization Toolbox from Mathworks ® [11] to implement a crane optimization client (MCOC) that used evolutionary algorithms to find the best configuration of the design parameters. Here, we extend the MCOC with the GWO algorithm.…”

“…We have previously developed a computer-automated design (CautoD) software framework for product design optimization and tested it on the design of offshore cranes [5][6][7][8]. The framework has several components.…”

“…For design optimization, we have developed a client-side product optimization client, namely the Artificial Intelligence for Product Optimization (AIPO) module that uses a genetic algorithm (GA) for optimizing the design parameters in a manner that achieves the crane's desired design criteria (e.g., KPIs related to performance and cost specifications) [6]. Complementing the AIPO module, we have also implemented a Matlab ® software module, the Matlab ® crane optimization client (MCOC), that also used a GA for single and multiobjective optimization [7]. Subsequently, we have added other evolutionary algorithms such as Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithms to the MCOC [8].…”

An Intelligent Winch Prototyping Tool

“…Our main focus is on the development of a generic and modular software framework for intelligent CautoD of maritime products, exemplified by offshore cranes and winches. We have previously presented the software framework with respect to the design of cranes (Bye, Osen, Pedersen, Hameed and Schaathun, 2016) and how various intelligent algorithms can be applied to optimise the design (Hameed, Bye, Osen, Pedersen and Schaathun, 2016;Hameed, Bye and Osen, 2016a,b).…”