AI tools for use in assembly automation and some examples of recent applications

Abstract

This paper reviews some of those tools. Applications of these tools in Assembly Automation have become more widespread due to the power and affordability of present-day computers. Research limitations/implications (limit 100 words): Many new Assembly Automation applications may emerge and greater use may be made of hybrid tools that combine the strengths of two or more of the tools reviewed in the paper. The tools and methods reviewed in this paper have minimal computation complexity and can be implemented on small assembly lines, single robots or systems with low-capability microcontrollers. Practical implications (limit 100 words): It may take another decade for engineers to recognize the benefits given the current lack of familiarity and the technical barriers associated with using these tools and it may take a long time for direct digital manufacturing to be considered commonplace… but it is expanding. The appropriate deployment of the new AI tools will contribute to the creation of more competitive Assembly Automation systems. Social Implications (limit 100 words): Other technological developments in AI that will impact on Assembly Automation include data mining, multi-agent systems and distributed self-organising systems. Originality/value (limit 100 words): The novel approaches proposed use Ambient Intelligence and the mixing of different AI tools in an effort to use the best of each technology. The concepts are generically applicable across all industrial assembly processes and this research is intended to prove that the concepts work in manufacturing.

Research on assembly quality adaptive control system for complex mechanical products assembly process under uncertainty

“…To ensure the assembly performance, the downstream process will be adjusted by considering the relationship between the process and the upstream process. In view of the BP neural network has the characteristics of strong self-learning, selforganizing and self-adaptive and can approximate any nonlinear function with any precision and has better fault tolerance [32][33][34][35][36]. So, this paper proposed the model of AQCTO and APP based on the BP neural network.…”

“…As to the nonlinear, multi-objective and multi-dimensional problems, to ensure better optimum and avoid to fall into local optimal for the particles, as well as to improve convergence for the algorithms, the learning factor should be set at 2 and maximum also be set at 2 [33][34][35][36][37]. The inertia weight is set between 0.4 and 0.9, which can improve convergence rate and convergence results [36].…”

“…(2) and (3), update the position and velocity of each particle. And ensure the updated position and velocity in a limited range [32,33]…”

The generation of adaptive assembly from predicting change propagation

“…Kim et al (2000Kim et al ( , 2004 established the transformation matrix to mark the position of the parts, and realized the relocation of the parts by solving constraints between them. Sanders and Gegov (2013) pointed out that artificial intelligence technology has already been used in the field of assembly automation and has great application potential. Gupta et al (2001) developed an assembly editor to edit the feature information of parts and used feature recognition technology to achieve the automatic positioning of the parts.…”

Sensor-based ambient intelligence for optimal energy efficiency

“…The research described in this paper combined ambient intelligence (AmI) (Sanders et al, 2007;Sanders, 2009e) and knowledge management (KM) technologies (Sanders and Gegov, 2006;Snidaro and Foresti, 2007) with AI (Sanders, 2009a, b;Sanders et al, , 2012aSanders and Gegov, 2013) and expert systems (Sanders et al, 2009(Sanders et al, , 2011a. That was to addresses two of the most critical problems in energy management in industrial production technologies: how to efficiently and promptly acquire and provide information online for optimising energy consumption; and how to effectively use such knowledge to support decision making.…”