The effect of job similarity on forgetting in multi-task production

For many decades, research has been done on the effect of learning and forgetting for manual assembly operations. Due to the evolution towards mass customization, cycle time prediction becomes more and more complex. The frequent change of tasks for an operator results in a rapid alternation between learning and forgetting periods, since the production of one model is causing a forgetting phase for another model. a new mathematical model for learning and forgetting is proposed to predict the future cycle time of an operator depending on the product mix of his actual assembly schedule. A main factor for this model is the job similarity between the task that is being learned and is being forgotten. In our experimental study the impact of job similarity onto the forgetting effect is measured. Two groups of operators were submitted to an equal time schedule, with other tasks to perform. At first, both groups were asked to perform the same main task. In the subsequent phase, they were submitted to different assembly tasks, each with another job similarity towards the main task, before again executing that main task. After a period of inactivity, the main task was assembled again by every subject. Results confirm that a higher job similarity results in a lower forgetting effect for the main task

Virtual commissioning of industrial control systems : a 3D digital model approach

With the growing presence of industry 4.0, flexible workstations and distributed control logic, software development has become an even more important part of the automation engineering process than before. In a traditional workflow, the main commissioning part of industrial control systems is performed on the real set-up and consequently during a time critical phase of the project. Virtual commissioning can be used to reduce the real commissioning time and can allow an earlier commissioning start, reducing the overall project lead time, risk of damaging parts, amount of rework and cost of error correction. Previous research showed already a reduction potential of the real commissioning time by 73\%, when using a virtual commissioning strategy based on a 3D digital model. However, the robustness of that approach still highly depends on the human expertise to fully evaluate the correct behavior in all possible use scenarios. This paper describes an approach to further automate these virtual commissioning steps by embedding functional specifications and use scenarios through a formal notation inside the 3D digital model. Configuration steps inside the virtual environment describe the conditions, independent from the control logic but related to component states and transitions in the digital model (actuator and sensor values, time restrictions, counters, positions of objects, etc.). These conditions are continuously monitored during an extensive commissioning run of the digital model covering all possible component states and transitions. A small scale experiment will show the reduction of the virtual commissioning time and earlier detection of quality issues

Intelligent authoring and management system for assembly instructions

Continuously increasing complexity and variance within high variety low volume assembly systems causes a vast amount of work instructions. As the amount of new models and variants increases, the need of efficient generation of unambiguous instructions rises. Continuous instruction modifications are unavoidable due to design, customer or process changes. Case based research in cooperation with four manufacturing companies with manual assembly environments points out that assembly instructions authors currently are combining different authoring tools for creating and updating work instructions. Consequently, keeping the rising amount of work instructions up to date becomes less trivial. Furthermore, authors often create work instructions from scratch while instructions of product variants are mostly identical. This causes a large amount of similar work instructions stored as separate documents. As a result, the amount of inconsistent and outdated assembly instructions increases. Poor assembly instruction quality causes frustration and a lower performance of assembly operators. An automatic authoring system and intelligent operator feedback must eliminate these problems. The automatic authoring system provides the author with an overview of preprocessed information and related historical assembly instructions that can serve as a basis for the newly created instructions. In this way, the creation of instructions can be significantly accelerated and work instructions will become more consistent. An experimental lab setup is built in order to test the presented framework. Based on the first tests, the authoring process was significantly accelerated. Further tests within production environments are required in order to validate the presented framework

Defining flexibility of assembly workstations through the underlying dimensions and impacting drivers

The concept of mass customization is becoming increasingly important for manufacturers of assembled products. As a result, manufacturers face a high variety of products, small batch sizes and frequent changeovers. To cope with these challenges, an appropriate level of flexibility of the assembly system is required. A methodology for quantifying the flexibility level of assembly workstations could help to evaluate (and improve) this flexibility level at all times. That flexibility model could even be integrated into the standard workstation design process. Despite the general consensus among researchers that manufacturing flexibility is a multi-dimensional concept, there is still no consensus on its different dimensions. A Systematic Literature Review (SLR) shows that many similarities can be found in the multitude of flexibility dimensions. Through a series of interactive company workshops, we achieved to reduce them to a shortlist of 9 flexibility dimensions applicable to an assembly workstation. In addition, a first step was taken to construct a causal model of these flexibility dimensions and their determining factors, the so called drivers, through the Interpretive Structural Modelling (ISM) approach. In the next phase, a driver scoring mechanism will be initiated to achieve an overall assembly workstation flexibility assessment based on the scoring of drivers depending on the workstation design

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

State-based verification of industrial control programs with the use of a Digital Model

Today, the verification of the control logic of automation projects is mainly a manual task during real commissioning phase on the physical installation. This involves a lot of risks towards possible damage and unanticipated delays of project completion. Virtual commissioning allows the verification to start earlier in the project timeline, but is often also a manual task and requires a lot of human knowledge and expertise. This paper proposes a workflow to provide additional support to the automation engineer based on a 3D Digital Model that interacts with the real control logic. The digital model records all occurring states and transitions in the background and visualizes the state graph to highlight unexpected behaviour and to support troubleshooting. The proposed approach is validated on a test case confirming (1) the effectiveness of highlighting unexpected behaviour during programming phase and (2) the substantial reduction in real commissioning time. The added value of the approach is concluded, although a number of limitations still exist. Future research includes the validation of the approach on real industrial cases

AutomationML in industry 4.0 environment : a systematic literature review

AutomationML is an open neutral XML based data exchange format used in automation systems. It has come into the public for more than 10 years and is being used in many different areas in all kinds of manufacturing applica-tions, such as digital twin, reconfigurable manufacturing systems, heteroge-neous data exchange, etc. However, no comprehensive literature review on the research and application progress of AutomationML has been found since the initiation of AutomationML. Based on the study and analysis of AutomationML related publications, this paper gives a detailed illustration on the state-of-the-art of AutomationML. Firstly, the background and terminolo-gies related to AutomationML are introduced. Secondly, the paper applies a methodology to collect AutomationML related publications, on which an analysis based on a multidimensional literature classification is conducted. Thirdly, according to the analysis results, current research status and whether AutomationML can meet the requirements for industry 4.0 environment are discussed. Finally, conclusion and outlook are illustrated in the end

Countering the forgetting effect in mixed-model manual assembly

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