The limitations of using only CAD and DHM in design relating to high value manufacturing

The ergonomics suites available within computer aided design and digital human modelling programs are increasingly being used to predict and prevent ergonomic and human factors risk due to poor design. To further aid the reduction in poor design, it is of importance to understand the need for user input and the limitations of these software programs. These limitations include: the small number of available anthropometric population samples; and the disconnect between what a designer perceives as possible, and what is possible within a manufacturing environment. A method of mitigating these limitations is the use of user input using virtual reality suites, mock-ups and motion capture technology

A study to trial the use of inertial non-optical motion capture for ergonomic analysis of manufacturing work

It is going to be increasingly important for manufacturing system designers to incorporate human activity data and ergonomic analysis with other performance data in digital design modelling and system monitoring. However, traditional methods of capturing human activity data are not sufficiently accurate to meet the needs of digitised data analysis; qualitative data are subject to bias and imprecision, and optically derived data are hindered by occlusions caused by structures or other people in a working environment. Therefore, to meet contemporary needs for more accurate and objective data, inertial non-optical methods of measurement appear to offer a solution. This article describes a case study conducted within the aerospace manufacturing industry, where data on the human activities involved in aircraft wing system installations was first collected via traditional ethnographic methods and found to have limited accuracy and suitability for digital modelling, but similar human activity data subsequently collected using an automatic non-optical motion capture system in a more controlled environment showed better suitability. Results demonstrate the potential benefits of applying not only the inertial non-optical method in future digital modelling and performance monitoring but also the value of continuing to include qualitative analysis for richer interpretation of important explanatory factors

The use of job aids for visual inspection in manufacturing and maintenance

Visual inspection is a task regularly seen in manufacturing applications and is still primarily carried out by human operators. This study explored the use of job aids (anything used to assist the operator with the task, such as lists, check sheets or pictures) to assist with visual inspection within a manufacturing facility that inspects used parts. Job aids in the form of inspection manuals were used regularly during the inspection process, and how accurately they were followed was dependent on a number of factors such as size of part, experience of the operator, and accuracy of the inspection manuals. If the job aids were well structured, well written and accessible, then the inspectors were seen to follow them, however for certain jobs inspectors were seen to change the inspection order making inspection more efficient. The findings of the study suggest that prior experience can help in designing efficient, easy to use job aids and that a collaborative approach to design as well as using pictorial examples for comparison purposes would improve the inspection process

Investigating the effects of signal light position on human workload and reaction time in human-robot collaboration tasks

Critical to a seamless working relationship in human-robot collaborative environments is effective and frequent communication. This study looked to assess whether placing a light source on a robot was more effective for informing the human operator of the status of the robot than conventional human-machine interfaces for industrial system signaling such as light towers. Participants completed an assembly task while monitoring a robot and changes to the light sources: either from one of two light towers or LED strip lights attached to the robot. Workload was assessed by measuring reaction times to light changes and by counting number of completed assemblies. Although both the ANOVA and Friedman tests returned none significant results, total misses per condition showed that the participants did not miss any of the robot lights, whereas signals were missed for the light towers

Putting people and robots together in manufacturing: are we ready?

Traditionally, industrial robots have been completely segregated from people in manufacturing systems to mitigate the dangers posed by their operational speeds and heavy payloads. Putting human operators together with large-scale industrial robots is now becoming increasingly possible with the development of integrated safety monitoring systems, and with smaller force-limited robotics that are now being produced with sufficient robustness for industry. However, with long-standing perceptions of robots as hazardous, we do not yet know how manufacturing workforces will accept collaborative systems with either large or small scale robotics and there is a need to identify and define new ethical and safety standard requirements for integrating people and robots to work collaboratively in industrial assembly tasks. To date there is little or no attention to ethical issues or psychological safety in the industrial safety standards that govern robotics and automated work systems. This paper describes the current situation and specific ways in which human-robot collaboration will significantly improve efficiency and flexibility, and outlines some early work that is being performed to identify the requirements that will be needed in order to facilitate this new way of bringing people and robots together in manufacturing. It presents a brief summary of initial findings that support the need for ethical issues to be considered as a candidate for new and / or revised safety standards

How and why we need to capture tacit knowledge in manufacturing: Case studies of visual inspection

Human visual inspection skills remain superior for ensuring product quality and conformance to standards in the manufacturing industry. However, at present these skills cannot be formally shared with other workers or used to develop and implement new solutions or assistive technologies because they involve a high level of tacit knowledge which only exists in skilled operators' internal cognitions. Industry needs reliable methods for the capture and analysis of this tacit knowledge so that it can be shared and not lost but also so that it can be best utilised in the transfer of manual work to automated systems and introduction of new technologies and processes. This paper describes two UK manufacturing case studies that applied systematic task analysis methods to capture and scrutinise the tacit knowledge and skills being applied in the visual inspection of aerospace components. Results reveal that the method was effective in eliciting tacit knowledge, and showed that tacit skills are particularly needed when visual inspection standards lack specification or the task requires greater subjective interpretation. The implications of these findings for future research and for developments in the manufacturing industry are discussed

Understanding how proximity to a robot affects perceived workload

Human-robot collaboration is fast becoming a preferable alternative to traditional manual assembly work in manufacturing. Large industrial robots and small force-limited ‘co-bots’ can now be deployed with sophisticated safety systems to enable human skills and robot skills to be employed most effectively. However, an understanding of the affect this introduction may have on cognitive workload is required prior to their full installation into industry. This paper describes an investigation which explored the effect of working in proximity to a robot on people’s workload. The NASA- Task Load Index (NASA-TLX) was used to assess subjective workload for participants after completing trials. Results showed centralised workload levels which were higher for the assembly activity requiring greater fine motor control

Adaptive automation assembly: Identifying system requirements for technical efficiency and worker satisfaction

Manual assembly work systems bring high flexibility but low productivity in comparison to fully automated systems. To increase productivity but maintain flexibility, future systems need to incorporate greater levels of automation which complement or augment the capabilities of the human operators who provide the manual work. Future systems should be designed for social and economic sustainability within fluctuating conditions and for adaptive utilisation of operators’ individual capabilities to maintain levels of productivity and personal satisfaction. To successfully create such systems with greater adaptivity and interactivity between people and technology a comprehensive understanding of design requirements is needed; the current problem is that there is no standard valid framework. The work described in this paper employed a three-component investigation to identify the various key requirements that are needed to form such a design framework for future human-automation assembly systems. This involves separate activities with different methodologies involving literature reviews, surveys and business case analysis to define use case scenarios and requirements for creating adaptive automation assembly system demonstrators. The different methodological approaches and results for all of the three component studies are described, along with conclusions and implications for further research work and for industry in general