Sreedhar Reddy

Scaling up model driven engineering-experience and lessons learnt

Model driven engineering (MDE) aims to shift the focus of software development from coding to modeling. Models being at a higher level of abstraction are easy to understand and analyze for desired properties, leading to better control over software development life cycle. Models are also used to automate generation of implementation artefacts resulting in greater productivity and uniform quality. The focus of the MDE community is largely on exploring modeling languages and model transformation techniques. Not much attention is paid to the issues of scale. Large business applications are typically developed over multiple geographical locations and have a lifecycle running into decades. This puts several additional demands on MDE infrastructure - multi-user multi-site model repository, versioning and configuration management support, change-driven incremental processes etc. We describe our MDE infrastructure, experience of using it to deliver several large business applications over past 15 years, and the lessons learnt.

An abstraction for reusable MDD components: model-based generation of model-based code generators

We discuss our experience of using model-based techniques to generate model-based code generators. The central idea behind model-driven development (MDD) is to use suitable models to specify various concerns and transform these models to a variety of text artifacts. A business product needs to deliver a given business functionality on a wide variety of implementation platforms and architectures thus necessitating multiple sets of code generators. However, there is a considerable commonality across these code generators. In absence of a suitable abstraction for capturing this commonality, there is little or no reuse across these code generators. We present an abstraction for organizing model-based code generators as a hierarchical composition of reusable building blocks. A building block is a localized specification of a concern in terms of a concern-specific meta model, model to model trans-formation, and model to text transformation. Model-based code generation is a 3-step walk over the composition tree wherein the first step transforms individual concern-specific models into a unified model, the second step transforms the unified model into individual concern-specific text artifacts, and the third step composes these text artifacts.

PREMΛP: A Platform for the Realization of Engineered Materials and Products

Integrated computational materials engineering (ICME), an integrated systems engineering approach is expected to (a) reduce the time and cost of discovery and development of materials and their manufacture, and (b) enable faster development of products assisted with richer material information. Development of a comprehensive IT platform that facilitates this through the integration of models, knowledge, and data for designing both the material and the product is a need of the day. In this paper, we introduce PREMΛP—Platform for Realization of Engineered Materials and Products conceptualized for this purpose. We also introduce two foundational problems that include (a) the development and production of steel mill products meeting stringent requirements of quality and cost and (b) the integrated design of gears and their manufacture, and how these are envisaged to be executed on PREMΛP. We envisage PREMΛP to be a platform for discovering new materials and concurrently designing materials, manufacturing processes and engineered components. The three associated papers in this series deal with application of the compromise Decision Support Problem construct for a manufacturing process design and component design problems, and perspectives on knowledge engineering application in the platform being proposed.

PREMΛP: Knowledge Driven Design of Materials and Engineering Process

In this paper, we present the knowledge engineering aspects of an IT infrastructure for a Platform for Realization of Engineered Materials and Products (PREMΛP). PREMΛP enables harnessing available knowledge, learning emerging knowledge and continually creating new knowledge. It consists of an ontology-based, knowledge-assisted method and platform to capture, structure, configure and reuse knowledge for designing materials and engineering systems. The PREMΛP ontology provides extensible representation of data and knowledge. The semantic mappings of concepts in the ontology are used to draw inferences and provide pro-active guidance while designing manufacturing processes and selecting parameters to meet the product specification that addresses a given engineering problem. We show how the ontological models can help in (a) automating process design starting from a requirements statement to selecting a suitable design process and (b) creating a parameterized instance of the selected process. In the context of two engineering examples, namely, the simultaneous design of a gear and the steel from which it is to be made, we illustrate the salient features of knowledge driven design.

Knowledge Assisted Integrated Design of a Component and Its Manufacturing Process

Integrated design of a product and its manufacturing processes would significantly reduce the total cost of the products as well as the cost of its development. However this would only be possible if we have a platform that allows us to link together simulations tools used for product design, performance evaluation and its manufacturing processes in a closed loop. In addition to that having a comprehensive knowledgebase that provides systematic knowledge guided assistance to product or process designers who may not possess in-depth design knowledge or in-depth knowledge of the simulation tools, would significantly speed up the end-to-end design process. In this paper, we propose a process and illustrate a case for achieving an integrated product and manufacturing process design assisted by knowledge support for the user to make decisions at various stages. We take transmission component design as an example. The example illustrates the design of a gear for its geometry, material selection and its manufacturing processes, particularly, carburizing-quenching and tempering, and feeding the material properties predicted during heat treatment into performance estimation in a closed loop. It also identifies and illustrates various decision stages in the integrated life cycle and discusses the use of knowledge engineering tools such as rule-based guidance, to assist the designer make informed decisions. Simulation tools developed on various commercial, open-source platforms as well as in-house tools along with knowledge engineering tools are linked to build a framework with appropriate navigation through user-friendly interfaces. This is illustrated through examples in this paper.

A Model-Driven Approach to Enterprise Data Migration

In a typical data migration project, analysts identify the mappings between source and target data models at a conceptual level using informal textual descriptions. An implementation team translates these mappings into programs that migrate the data. While doing so, the programmers have to understand how the conceptual models and business rules map to physical databases. We propose a modeling mechanism where we can specify conceptual models, physical models and mappings between them in a formal manner. We can also specify rules on conceptual models. From these models and mappings, we can automatically generate a program to migrate data from source to target. We can also generate a program to migrate data access queries from source to target. The overall approach results in a significant improvement in productivity and also a significant reduction in migration errors.

A Graph-Pattern Based Approach for Meta-Model Specific Conflict Detection in a General-Purpose Model Versioning System

Model driven engineering is the key paradigm in many large system development efforts today. A good versioning system for models is essential for change management and coordinated development of these systems. Support for conflict detection and reconciliation is one of the key functionalities of a versioning system. A large system uses a large number of different kinds of models, each specifying a different aspect of the system. The notion of conflict is relative to the semantics of a meta-model. Hence conflicts should be detected and reported in a meta-model specific way. In this paper we discuss a general purpose model versioning system that can work with models of any meta-model, and a graph-pattern based approach for specifying conflicts in a meta-model specific way. We also present an efficient algorithm that uses these graph-patterns to detect conflicts at the right level of abstraction.

A Data Science Approach for Analysis of Multi-Pass Wire Drawing

The process of reducing the cross-section of a wire by pulling the wire through a series of dies belongs to a special class of manufacturing processes that have a sequential nature. In each pass, the diameter of incoming wire is reduced by a certain factor. Determining number of passes and their configurations required to achieve the desired reduction while optimizing properties such as tensile strength, strain distribution, energy consumption, etc. is an optimization problem. An essential building block of this optimization problem is a model of a drawing pass that can predict the output properties for a given parameter configuration of the pass (forward inference), or its inverse, i.e. predict the configuration parameters required to achieve the desired properties.In this paper, we present a case study on the application of Bayesian networks to address the problem of inference in multi-pass wire drawing. Also we explore the building of a generic model that can be used for any pass and compare its effectiveness with pass-specific models. Our findings so far are as follows: For forward inference, the generic model has prediction accuracy close enough to pass-specific models. Given that it can be used to solve a problem with arbitrary number of passes, this model is clearly more useful. Further, this being a generative model, it can be used for inverse inference as well.Copyright

A Relation Aware Search Engine for Materials Science

Knowledge of material properties, microstructure, underlying material composition, and manufacturing process parameters that the material has undergone is of significant interest to materials scientists and engineers. A large amount of information of this nature is available in publications in the form of experimental measurements, simulation results, etc. However, getting to the right information of this kind that is relevant for a given problem on hand is a non-trivial task. First, an engineer has to go through a large collection of documents to select the right ones. Then, the engineer has to scan through these selected documents to extract relevant pieces of information. Our goal is to help automate some of these steps. Traditional search engines are not of much help here, as they are keyword centric and weak on relation processing. In this paper, we present a domain-specific search engine that processes relations to significantly improve search accuracy. The engine preprocesses material publication repositories to extract entities such as material compositions, material properties, manufacturing processes, process parameters, and their values and builds an index using these entities and values. The engine then uses this index to process user queries to retrieve relevant publication fragments. It provides a domain-specific query language with relational and logical operators to compose complex queries. We have conducted an experiment on a small library of publications on steel on which searches such as “get the list of publications which have carbon composition between 0.2 and 0.3 and on which tempering is carried out for about 30 to 40 min” are performed. We compare the results of our search engine with the results of a keyword-based search engine.

From Building Systems Right to Building Right Systems

Model driven engineering community has made considerable progress in the last decade and a half as regards developing software systems with enhanced productivity, quality and platform independence. However, in the increasingly dynamic world, enterprises are facing a different kind of challenge where the focus shifts from how to build to what to build. This paper proposes a shift in focus of model driven engineering community to meet these challenges. We outline an approach and a research agenda to realize the same.