Assembly Support System Research Articles

Real-Time Assembly Support System with Hidden Markov Model and Hybrid Extensions

This paper presents a context-aware adaptive assembly assistance system meant to support factory workers by embedding predictive capabilities. The research is focused on the predictor which suggests the next assembly step. Hidden Markov models are analyzed for this purpose. Several prediction methods have been previously evaluated and the prediction by partial matching, which was the most efficient, is considered in this work as a component of a hybrid model together with an optimally configured hidden Markov model. The experimental results show that the hidden Markov model is a viable choice to predict the next assembly step, whereas the hybrid predictor is even better, outperforming in some cases all the other models. Nevertheless, an assembly assistance system meant to support factory workers needs to embed multiple models to exhibit valuable predictive capabilities.

Towards an Assembly Support System with Dynamic Bayesian Network

Due to the new technological advancements and the adoption of Industry 4.0 concepts, the manufacturing industry is now, more than ever, in a continuous transformation. This work analyzes the possibility of using dynamic Bayesian networks to predict the next assembly steps within an assembly assistance training system. The goal is to develop a support system to assist the human workers in their manufacturing activities. The evaluations were performed on a dataset collected from an experiment involving students. The experimental results show that dynamic Bayesian networks are appropriate for such a purpose, since their prediction accuracy was among the highest on new patterns. Our dynamic Bayesian network implementation can accurately recommend the next assembly step in 50% of the cases, but to the detriment of the prediction rate.

Robust Assembly Assistance Using Informed Tree Search with Markov Chains.

Manual work accounts for one of the largest workgroups in the European manufacturing sector, and improving the training capacity, quality, and speed brings significant competitive benefits to companies. In this context, this paper presents an informed tree search on top of a Markov chain that suggests possible next assembly steps as a key component of an innovative assembly training station for manual operations. The goal of the next step suggestions is to provide support to inexperienced workers or to assist experienced workers by providing choices for the next assembly step in an automated manner without the involvement of a human trainer on site. Data stemming from 179 experiment participants, 111 factory workers, and 68 students, were used to evaluate different prediction methods. From our analysis, Markov chains fail in new scenarios and, therefore, by using an informed tree search to predict the possible next assembly step in such situations, the prediction capability of the hybrid algorithm increases significantly while providing robust solutions to unseen scenarios. The proposed method proved to be the most efficient for next assembly step prediction among all the evaluated predictors and, thus, the most suitable method for an adaptive assembly support system such as for manual operations in industry.

МОДЕЛИРОВАНИЕ СИСТЕМ УПРАВЛЕНИЯ ОРГАНИЗАЦИОННО-ТЕХНОЛОГИЧЕСКИМ ОБЕСПЕЧЕНИЕМ МЕХАНИЧЕСКОЙ СБОРКИ НА ПРЕДПРИЯТИЯХ АВТОМОБИЛЕСТРОИТЕЛЬНОГО КЛАСТЕРА

A system for providing mechanical assembly based on threaded joints used at enterprises of the automotive cluster. This system combines a subsystem of assembly equipment, a subsystem of spare tools. The model of the mechanical assembly support system can be based on a set-theoretic approach as a set of subsystems of organizational and technological support for mechanical assembly at automotive cluster enterprises. Subsystems of organizational and technological support of mechanical assembly for automotive cluster enterprises can be described from the standpoint of management theory. In each control system being formed, an object, a control subject, a feedback mechanism is allocated, and it is also assumed that the PMD will interact with the subsystem of organizational and technological support for mechanical assembly through the information system database being formed. The task of managing the organizational and technological system for providing mechanical assembly involves solving particular problems, including those of managing the subsystem of assembly equipment, also the subsystem of controlling and testing, the subsystem of spare tools, instruments and devices. The criteria of optimal control of the generated control tasks are presented.

Assembly Assistance System with Decision Trees and Ensemble Learning.

This paper presents different prediction methods based on decision tree and ensemble learning to suggest possible next assembly steps. The predictor is designed to be a component of a sensor-based assembly assistance system whose goal is to provide support via adaptive instructions, considering the assembly progress and, in the future, the estimation of user emotions during training. The assembly assistance station supports inexperienced manufacturing workers, but it can be useful in assisting experienced workers, too. The proposed predictors are evaluated on the data collected in experiments involving both trainees and manufacturing workers, as well as on a mixed dataset, and are compared with other existing predictors. The novelty of the paper is the decision tree-based prediction of the assembly states, in contrast with the previous algorithms which are stochastic-based or neural. The results show that ensemble learning with decision tree components is best suited for adaptive assembly support systems.

Development of a Mechanical Watch Assembly Support System Using Smart Glasses

Development of a Mechanical Watch Assembly Support System Using Smart Glasses

Effectiveness of Augmented Reality Guides for Blind Insertion Tasks

Although many augmented reality (AR)-based assembly support systems have been proposed in academic research and industry, the effectiveness of AR to resolve the occlusion issue in the context of a blind assembly process remains an unexplored topic. Therefore, the present work investigates how AR can assist operators during the execution of blind manual assembly tasks. Specifically, an AR research set-up was designed to provide assistance in occlusion situations during a peg-in-hole task. The set-up featured a see-through device (HoloLens), which provides operators with two modes of visual augmentations that directly overlay on the assembly objects. The first mode referred to as the “wireframe overlay” displays the inner part of the objects, providing an inside view of the occluded parts, and the second one referred to as the “axes overlay,” displays the axes of the objects and their slots, indicating how to align the different parts during the assembly. The effectiveness of these AR visualizations was compared to a baseline augmentation-free situation in a controlled experiment. Thus, following a within-subject design, 30 participants performed a two-stages blind insertion task. Their performances represented by task completion time, insertion errors, and smoothness of the insertions were recorded. In addition, a post-questionnaire reported their subjective perception of task difficulty during the task and their preferences. Results indicated a strong acceptance of participants for AR visualizations that they rated as allowing them to perform the task more easily. However, no statistically significant differences in terms of objective performance measures were found. Yet, it was found that axes overlay produced smoother trajectories compared to the wireframe overlay, highlighting the potential effect of more abstract visualization aids.

Assembly support systems with Markov predictors

ABSTRACT In this paper, we analyse Markov prediction as a suitable model to suggest the next assembly step in the manufacturing process. The goal is a decision support system which can assist the workers in a factory, at least in their training period, to manually assemble a product. We evaluate the proposed context-based predictor on a dataset collected through an experiment involving 68 trainees and compare it with our previously implemented two-level context-based predictors. The experiment consisted of assembling a tablet composed of seven components. We were interested in finding the most efficient prediction method that can be soundly employed in real-life settings.

Information Presentation Combining Display-Mounted Workbench and Projector in Assembly Support System

Information Presentation Combining Display-Mounted Workbench and Projector in Assembly Support System

Hybrid Object and Screen Stabilized Visualization Techniques for an AR Assembly Support System

Hybrid Object and Screen Stabilized Visualization Techniques for an AR Assembly Support System

Context-related Visualization Modes of an AR-based Context-Aware Assembly Support System in Object Assembly( Mixed Reality)

Context-related Visualization Modes of an AR-based Context-Aware Assembly Support System in Object Assembly( Mixed Reality)

Multi-Modal Assembly-Support System for Cell Production

Cell production systems meet the diversified production and quantity requirements flexibly. However, their efficiency mainly depends on the human operators' working performance. In order to improve the efficiency of the cell production system, effective assembly-support systems should be developed to assist human operators during the assembly process. In this paper, a multi-modal assembly-support system (MASS) is proposed, which aims to support assembly human operators from both information aspect and physical aspect while meets the actual manufacturing requirements. With the information and physical support from this system, the assembly complexity and burden to the assembly operators are reduced. To evaluate the effect of MASS, a group of human operators are required to execute a cable-insert task. From the experimental results, we can conclude that using this system, the human operators' assembly performance is improved and their mental work load is reduced, which improves the efficiency of the cell production system.

Combination of robot control and assembly planning for a precision manipulator

This paper researches how to realize the automatic assembly operation on a two-finger precision manipulator. A multi-layer assembly support system is proposed. At the task-planning layer, based on the computer-aided design (CAD) model, the assembly sequence is first generated, and the information necessary for skill decomposition is also derived. Then, the assembly sequence is decomposed into robot skills at the skill-decomposition layer. These generated skills are managed and executed at the robot control layer. Experimental results show the feasibility and efficiency of the proposed system.