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

The role of equipment flexibility in Overall Equipment Effectiveness (OEE)-driven process improvement

In manufacturing and assembly operations, Overall Equipment Effectiveness (OEE) is a frequently used quantitative metric for measuring the overall productivity of a single machine, cell or an integrated manufacturing system. However, it does neglect and typically even penalizes flexibility capabilities. Today’s customer needs for highly customized products put these productivity-based measurements more and more under pressure. Frequent product changes on assembly workstations typically result in lower availability through more set-up, more performance losses due to slower cycles and the learning-forgetting effect of operators, and start-up defects resulting in more frequent quality issues. A contradiction arises: in modern production and assembly this flexibility becomes more and more important as an enabler for the mass customization paradigm, but is difficult to incorporate in (or put in relation to) an OEE figure or trend and conflicts with the OEE-driven process improvement strategies. Consequently, it can be argued that flexibility capabilities should be embedded in the equipment effectiveness calculation. Modern manufacturing and assembly cells should have a high equipment effectiveness through a high product mobility with a stable and uniform productivity across the complete range of products. This paper first highlights the importance of flexibility in the measurement of equipment effectiveness to facilitate the mass customization paradigm and to try to continuously improve towards a resilient manufacturing system. Next, the heuristic measurement framework for the Flexibility-included Overall Equipment Effectiveness (OEEFlex) metric is introduced, based on three core indicators: mobility, uniformity and range. The three factors are introduced and described. Links to current OEE measurement frameworks are made. The approach towards the new metric starts from a long list of losses and variables and possible calculation methods for the indicator values. Future research describes illustrative simulation scenarios to filter towards a short list of relevant and valuable calculation options for the overall metric. Followed by an expert based approach towards final selection of the metric and a case based in-company validation of the result

A formal skill model to enable reconfigurable assembly systems

As assembly systems move into the era of mass customisation, the complexity of design processes, (re)configurations and operations rises. Well-structured data are key in keeping this complexity manageable. Here to, this paper presents a multidimensional formal skill model designed to deliver generic descriptions of needs and capacities with skills as the connector between products, processes and resources. The model formalises resource structures in relation to the processes they master and products they can produce. This paper discusses the case-based evaluation in a reconfigurable assembly system and highlights the added-value of a skill-based modelling approach. The presented formal model combines concepts coming from both offline and online modelling perspectives and allows for various applications and levels of detail. The resource structures embedded in the prerequisites of a skill enable matchmaking of resources for workspace design and reconfigurations. The mapping of the model to standardised ISA-95 models couples production needs to the resources allowing for more optimal production planning, control and a structured interface between enterprise and control systems. The possibility to couple states to the assembly environment allows for optimal runtime orchestration

A formal skill model facilitating the design and operation of flexible assembly workstations

In the Industry 4.0 area, there is an increasing demand for highly customized products in small batch sizes. Final assembly operations are frequently targeted to embed flexibility and compensate for the growing manufacturing uncertainties. Therefore, an adequately designed and operated flexible assembly workstation is crucial. Converting the flexibility needs into design and operational decisions requires versatile formal models delivering generic descriptions of needs and capacities. Skills form the central connector between products, processes and resources. Here, a skill-centered model for describing resource activities, the related production needs and flexibility impacts is introduced. The model fits both plug and produce and design optimization settings and goes beyond current skill-based modelling by offering a framework which, by design, does not limit the applications and easily adapts to the desired level of detail. One key strength is its ability to combine abstract and executable skills. Next to the product-action skills, also assistive skills related to operator support, parts storing, ergonomics etc. can be easily modelled. The use of the model is illustrated by an example based on an industrial use case from Flemish industry