Developing manufacturing simulation models usually requires experts with knowledge of multiple areas including manufacturing, modeling, and simulation software. The expertise requirements increase for virtual factory models that include representations of manufacturing at multiple resolution levels. This paper reports on an initial effort to automatically generate virtual factory models using manufacturing configuration data in standard formats as the primary input. The execution of the virtual factory generates time series data in standard formats mimicking a real factory. Steps are described for auto-generation of model components in a software environment primarily oriented for model development via a graphic user interface. Advantages and limitations of the approach and the software environment used are discussed. The paper concludes with a discussion of challenges in verification and validation of the virtual factory prototype model with its multiple hierarchical models and future directions.
A number of past and recent initiatives, such as smart manufacturing (SMLC 2012) and Industrie 4.0 (Mario, Tobias, and Boris 2015) have identified modeling and simulation as key to the advancement of manufacturing. Some have proposed the use of simulation at multiple levels within manufacturing, ranging from physics-based models of the manufacturing process at a very detailed level to high level supply chain models and everything in between.
The development of simulation models of manufacturing systems requires expertise in multiple areas including manufacturing operations, conceptual modeling of manufacturing systems at the right level of abstraction, and implementation of the conceptual model using appropriate simulation software. The expertise requirement goes up substantially if multiple levels of manufacturing are to be modeled. The expertise requirement can be a deterrence to wider use of simulation in manufacturing. It could be a roadblock for the move towards smart manufacturing and Industrie 4.0.
A virtual factory has been presented as a multi-resolution simulation model of a corresponding real factory with the capability to model with high fidelity if desired (Jain et al. 2015). Such a virtual factory model can provide the modeling and simulation capabilities envisaged in smart manufacturing and Industrie 4.0. However, at present developing a virtual factory model may only be an option for large corporations with substantial budgets given the high expertise requirement.
This paper proposes automatic generation of virtual factory models based on data as a way to reduce the expertise requirement and thus facilitate the increased use of simulation. This is admittedly not a new idea. There have been solutions from commercial vendors of discrete event simulation (DES) software in the past that generated factory models when provided with a data file in their proprietary data formats. This effort proposes going beyond the prior offerings in two key ways. First, the effort proposes the use of standard formats for input data describing the subject manufacturing system. Second, the generated model is proposed to be a virtual factory model as defined with multi-resolution capabilities rather than the single level models available in commercial offerings. The current implementation of the concept is a first step with limited scope with use of only a couple standards towards achieving the proposed capability.
The next section discusses relevant efforts reported in the literature over the last year. Section 3 discusses the proposed overall approach to achieve the vision of virtual factory and provides an overview of the approach for the current implementation. The implementation using AnyLogic, the selected software for this initial step, is then described in detail in section 4. Section 5 concludes the paper with a discussion of future work to continue implementation of the virtual factory concept.
Figure 1 shows the logic network in an AnyLogic model that represents a machine shop. This machine shop is composed of a turning machine cell and a milling machine cell. The parts processed in this workshop have two operations. They are machined first in the turning cell and then in the milling cell. The parts are processed in batches of varying sizes.
The logic network representation of the workshop starts with a Source block to model arrivals of batches. Upon arrival the batches wait in the raw material storage area represented by a Delay block. This Delay is used as a queue and holds the batch until a turning machine is available. Since the turning machine cell is composed of four machines, a SelectOutput5 block (an output selector with 5 conditional outputs) is used to decide to which machine the batch should go. After the turning machine cell, the batch moves to the work in progress area represented by another Delay block, where it stays until a milling machine is available. Again, since the milling machine cell is composed of 2 machines, a SelectOutput block (an output selector with two conditional outputs) is used to select the available machine. After completion of the second operation, the batch moves to the finished goods area where batches are waiting for pick up. This area is represented with yet another Delay block. The shipping of batches from the workshop to customers is represented by batches leaving the model via a Sink block.