Utilising low cost RGB-D cameras to track the real time progress of a manual assembly sequence

Purpose The purpose of this paper is to explore the role that computer vision can play within new industrial paradigms such as Industry 4.0 and in particular to support production line improvements to achieve flexible manufacturing. As Industry 4.0 requires “big data”, it is accepted that computer vision could be one of the tools for its capture and efficient analysis. RGB-D data gathered from real-time machine vision systems such as Kinect ® can be processed using computer vision techniques. Design/methodology/approach This research exploits RGB-D cameras such as Kinect® to investigate the feasibility of using computer vision techniques to track the progress of a manual assembly task on a production line. Several techniques to track the progress of a manual assembly task are presented. The use of CAD model files to track the manufacturing tasks is also outlined. Findings This research has found that RGB-D cameras can be suitable for object recognition within an industrial environment if a number of constraints are considered or different devices/techniques combined. Furthermore, through the use of a HMM inspired state-based workflow, the algorithm presented in this paper is computationally tractable. Originality/value Processing of data from robust and cheap real-time machine vision systems could bring increased understanding of production line features. In addition, new techniques that enable the progress tracking of manual assembly sequences may be defined through the further analysis of such visual data. The approaches explored within this paper make a contribution to the utilisation of visual information “big data” sets for more efficient and automated production

A reliable turning process by the early use of a deep simulation model at several manufacturing stages

The future of machine tools will be dominated by highly flexible and interconnected systems, in order to achieve the required productivity, accuracy, and reliability. Nowadays, distortion and vibration problems are easily solved in labs for the most common machining operations by using models based on the equations describing the physical laws of the machining processes; however, additional efforts are needed to overcome the gap between scientific research and real manufacturing problems. In fact, there is an increasing interest in developing simulation packages based on "deep-knowledge and models" that aid machine designers, production engineers, or machinists to get the most out of the machine-tools. This article proposes a methodology to reduce problems in machining by means of a simulation utility, which uses the main variables of the system and process as input data, and generates results that help in the proper decision-making and machining plan. Direct benefits can be found in (a) the fixture/ clamping optimal design; (b) the machine tool configuration; (c) the definition of chatter-free optimum cutting conditions and (d) the right programming of cutting toolpaths at the Computer Aided Manufacturing (CAM) stage. The information and knowledge-based approach showed successful results in several local manufacturing companies and are explained in the paper.The work presented in this paper was supported in some sections within the project entitled MuProD-Innovative Proactive Quality Control System for In-Process Multi-Stage Defect Reduction- of the Seventh Framework Program of the European Union [FoF.NMP.2011-5] and UPV/EHU under program UFI 11/29. Thanks are given to Tecnalia, for collaboration in testing, and especially to Ainhoa Gorrotxategi and Ander Jimenez for the sound work in the project. Thanks to Gamesa Eolica and Guruzpe, for the time, technical advices, and efforts during the analysis in examples

Improving Fire Emergency Management Using Occupant Information and BIM-Based Simulation

The increasing complexity of buildings has brought some difficulties for emergency response. When fires occur in a building, limited perception regarding the disaster area and occupants can increase the probability of injuries and damages. Thus, the availability of comprehensive and timely information may help understand the existing conditions and plan an efficient evacuation. For this purpose, Building Information Modeling (BIM) should be integrated with three sets of information: (1) occupancy that defines the type of space usage; (2) occupants’ information; and (3) sensory data. The Industry Foundation Classes (IFC), as a standard of BIM, has the definitions for all areas, volumes, and elements of a building. IFC also has the basic definitions of sensor and occupant entities. However, these entities do not provide enough dynamic and accurate information for supporting emergency management systems. In addition, building renovation projects have an effect on evacuation time. During the building renovation projects, space is shared between the construction crews and occupants. The construction works change the building layout and movement flow, which increase the occupants’ vulnerability, affecting their evacuation behavior under emergency conditions. Hence, the safety and wellbeing of the occupants as well as their evacuation time should be considered under emergency incidents. This thesis aims to improve fire emergency management using occupant information and BIM-based simulation. For this purpose, a “dynamic BIM” for fire emergency real-time management is developed that captures enough dynamism regarding the building condition as well as environmental conditions and occupants’ behavior. Also, an Agent-Based Model (ABM) is used to assist in the analysis of the static and dynamic behavior of the environment and occupants in BIM. The specific objectives of the research are: (1) extending IfcSensor entity for occupant’s sensors; (2) adding new attributes to IfcOccupant to support emergency response operations and defining a new entity for occupancy; (3) defining the relationships between sensors, occupants, occupancy, time series, and building components in the context of building evacuation; (4) creating dynamic BIM for tracking occupants and environmental states; and (5) evaluating the evacuation time for specific scenarios where additional spatio-temporal constraints exist during a fire incidence. Renovation construction operations are considered as such constraint and an ABM co-simulation framework is developed under emergency conditions. The feasibility of the proposed methods is discussed using different case studies