Networked engineering notebooks for smart manufacturing

A goal of the industrial internet is to make information about manufacturing processes and resources available wherever decision making may be required. Agile use of information is a cornerstone of data analytics, but analytical methods more generally, including model-based investigations of manufacturability and operations, do not so easily benefit from this data. Rather than relating anonymous patterns of data to outcomes, these latter analytical methods are distinguished as relying on conceptual or physics-based models of the real world. Such models require careful consideration of the fitness of the data to the purpose of the analysis. Verification of these analyses, then, is a significant bottleneck. A related problem, that of ascertaining reproducible results in scientific claims, is being addressed through executable notebook technology. This paper proposes to use notebook technologies to address that bottleneck. It describes how this notebook technology, linked to internet-addressable ontologies and analytical metamodels, can be used to make model-based analytical methods more verifiable, and thus more effective for manufacturers

Production system identification with genetic programming

Modern system-identification methodologies use artificial neural nets, integer linear programming, genetic algorithms, and swarm intelligence to discover system models. Pairing genetic programming, a variation of genetic algorithms, with Petri nets seems to offer an attractive, alternative means to discover system behaviour and structure. Yet to date, very little work has examined this pairing of technologies. Petri nets provide a grey-box model of the system, which is useful for verifying system behaviour and interpreting the meaning of operational data. Genetic programming promises a simple yet robust tool to search the space of candidate systems. Genetic programming is inherently highly parallel. This paper describes early experiences with genetic programming of Petri nets to discover the best interpretation of operational data. The systems studied are serial production lines with buffers

Dynamic production system identification for smart manufacturing systems

This paper presents a methodology, called production system identification, to produce a model of a manufacturing system from logs of the system's operation. The model produced is intended to aid in making production scheduling decisions. Production system identification is similar to machine-learning methods of process mining in that they both use logs of operations. However, process mining falls short of addressing important requirements; process mining does not (1) account for infrequent exceptional events that may provide insight into system capabilities and reliability, (2) offer means to validate the model relative to an understanding of causes, and (3) updated the model as the situation on the production floor changes. The paper describes a genetic programming (GP) methodology that uses Petri nets, probabilistic neural nets, and a causal model of production system dynamics to address these shortcomings. A coloured Petri net formalism appropriate to GP is developed and used to interpret the log. Interpreted logs provide a relation between Petri net states and exceptional system states that can be learned by means of novel formulation of probabilistic neural nets (PNNs). A generalized stochastic Petri net and the PNNs are used to validate the GP-generated solutions. The methodology is evaluated with an example based on an automotive assembly system

Multi-job production systems: definition, problems, and product-mix performance portrait of serial lines

This paper pursues two goals: (a) Define a class of widely used in practice flexible manufacturing systems, referred to as Multi-Job Production (MJP) and formulate industrially motivated problems related to their performance. (b) Provide initial results concerning some of these problems pertaining to analysis of the throughput and bottlenecks of MJP serial lines as functions of the product-mix. In MJP systems, all job-types are processed by the same sequence of manufacturing operations, but with different processing time at some or all machines. To analyse MJP with unreliable machines, we introduce the work-based model of production systems, which is insensitive to whether single- or multi-job manufacturing takes place. Based on this model, we investigate the performance of MJP lines as a function of the product-mix. We show, in particular, that for the so-called conflicting jobs there exists a range of product-mixes, wherein the throughput of MJP is larger than that of any constituent job-type manufactured in a single-job regime. To characterise the global behaviour of MJP lines, we introduce the Product-Mix Performance Portrait, which represents the system properties for all product-mixes and which can be used for operations management. Finally, we report the results of an application at an automotive assembly plant

Production scheduling as joint cognitive work

The trend over the past four decades to move manufacturing operations to low-wage countries has left many developed nations with diminished manufacturing skills. Manufacturing’s current trajectory appears aimed at automating away human roles in tasks requiring cognitive skills much as it has automated away human roles in tasks requiring physical skills. This may prove difficult owing to the diversity of industrial contexts and skills involved in manufacturing. It may be possible, however, for humans to perform some cognitive tasks (such as production scheduling) jointly with artificial intelligence (AI) machine agents. This thesis reports the research undertaken to improve outcomes in the cognitive task of formulating production scheduling solutions. The aim is to develop an effective methodology by which the work of production scheduling can be divided among human and machine agents, then to develop tools to support the methodology. Requirements on joint cognitive work were established through a detailed literature review and an industrial pilot. These efforts revealed that, though machine guidance and validation of analysts’ scheduling solutions is theoretically possible, research gaps make this joint cognitive work impractical. Specifically, difficulties are encountered in: (1) analysts translating requirements into analytical models; (2) machine agents recognizing the scheduling formulation analysts are attempting; and (3) both analysts and machine agents staying apprised of emerging impediments to production. Analysis of the requirements and research gaps pointed to the development of a sociotechnical system in which the joint work is performed as the manipulation of two shared objects: a notebook for formulating the scheduling problem, and computer simulations highlighting current production challenges. Methodology and software were developed to support the shared objects and to validate their use in addressing the research gaps.</div

Architecture definition in complex system design using model theory

Architecture definition, which is central to system design, is one of the two most used technical processes in the practice of model-based systems engineering. In this article, a fundamental approach to architecture definition is presented and demonstrated. The success of its application to engineering problems depends on a precise but practical definition of the term architecture. In the standard for architecture description, ISO/IEC/IEEE 42010:2011, a definition was adopted that has been subsumed into later standards. In 2018, the working group JTC1/SC7/WG42 on system architecture began a review of the adopted definition, holding sessions late in the year. This article extends and complements a position paper submitted during the meetings, in which Tarski model theory and ISO/IEC 24707:2018 (logic-based languages) were used to better understand relationships between system models and concepts related to architecture. Independent from the working group, it now contributes intuitive fundamental definitions of the terms architecture and system that are used to specify a mathematically based technical process for architecture definition. The engineering utility and benefits to complex system design are demonstrated in a diesel engine emissions reduction case study.</div