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

Real time implementation of learning-forgetting models for cycle time predictions of manual assembly tasks after a break

Industry 4.0 provides a tremendous potential of data from the work floor. For manufacturing companies, these data can be very useful in order to support assembly operators. In literature, a lot of contributions can be found that present models to describe both the learning and forgetting effect of manual assembly operations. In this study, different existing models were compared in order to predict the cycle time after a break. As these models are not created for a real time prediction purpose, some adaptations are presented in order to improve the robustness and efficiency of the models. Results show that the MLFCM (modified learn-forget curve model) and the PID (power integration diffusion) model have the greatest potential. Further research will be performed to test both models and implement contextual factors. In addition, since these models only consider one fixed repetitive task, they don't target mixed-model assembly operations. The learning and forgetting effect that executing each assembly task has on the other task executions differs based on the job similarity between tasks. Further research opportunities to implement this job similarity are listed

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

Competence-aware support for manual assembly operators

Operatoren in manuele assemblage ervaren tegenwoordig een aanzienlijke cognitieve belasting door de diversiteit aan taken. Het personaliseren van cognitieve ondersteuning, gebaseerd op competenties, is waardevol, maar wordt nagenoeg niet toegepast in de industrie. Een gebrek aan inzicht in de competenties van operatoren vormt hierbij een obstakel. Vooral wanneer een taak lang niet dient uitgevoerd te worden, is een inschatting maken van het competentieniveau van de operator erg moeilijk. Dit onderzoek gebruikt de cyclustijd als indicator voor de competentie van een operator. Er werd een algoritme ontwikkeld op basis van bestaande leer- en vergeetmodellen. Deze modellen zijn typisch een wiskundige beschrijving van hoe de cyclustijd verloopt in de tijd, met behulp van parameters. In een eerste fase werd een algoritme ontwikkeld om deze parameters te bepalen aan de hand van historische en real-time tijdsregistraties. Een empirische studie toonde aan dat een gelijkaardige taak uitvoeren, het vergeeteffect vermindert. Om die reden werd de factor "taakgelijkenis" opgenomen in het algoritme. Op het einde van elke fase werden de aanpassingen geëvalueerd op basis van empirische data die zelf gecapteerd werd door het opzetten van experimenten in een labosetting

A Competency Framework for Assembly Operators

A competency framework identifies the required competencies for effective and efficient work in assembly operations. Detailed competency assessment helps to keep track of the competencies and evaluate their impact on the worker’s performance. The availability of an assembly operator competency framework will support industrial companies in workforce development in an Industry 5.0 context by optimizing the allocation of assembly and training tasks, towards a tradeoff between productivity and competence build-up, while keeping the motivation of the workforce on a high level. So far no such competency framework for assembly operators has been reported in literature so far. This paper contributes to the state-of-the-art by introducing a detailed description of a competency framework for assembly operators. Firstly, the background and structure of existing manufacturing-related competency models are introduced. Secondly, the basic principles of competency framework development are applied resulting in a dedicated competency framework for assembly operators. Thirdly, an experimental approach is proposed for a first evaluation of the assembly operator’s competency framework. Finally, a conclusion is made and further research steps are presented

The evaluation of an elementary virtual training system for manual assembly

Due to the low volume high variety strategies of manufacturing companies, manual assembly operators have a much larger cognitive load than before. The expertise of the operators must be kept up to date at any time. Since the high investment and low flexibility of a real setting to perform a manual assembly training, a virtual replica is introduced in many cases. The aim of this paper is to study the effect of an elementary virtual training for manual assembly tasks. In literature, different studies on the topic can be found; nevertheless, a comparison between the different studies is not possible due to diverse evaluation methods and descriptions. A benchmark for a uniform evaluation of virtual training systems is presented and applied to this experiment. Two groups were submitted to a number of manual assembly tasks. The test group got a virtual training period in advance. A significant learning transfer during that training period was observed. When the first assembly of the reference group is counted as a real training, no significant difference can be found between the virtual and real training. The outcomes of this experiment will be used in future work to compare different virtual training systems and influential factors such as the assembly complexity. Furthermore, the application of virtual training to manual assembly in a mixed-model environment and its industrial usability are topics that still need to be studied