Pulak Bandyopadhyay

Maintenance Opportunity Planning System

Timely performance of preventive maintenance (PM) tasks is a critical element of manufacturing systems. Since the majority of PM tasks requires that equipment be stopped, these tasks can generally only be performed during nonproduction shifts, breaks, or other scheduled downtime. Thus, there is a trade-off between time dedicated to production and time available for preventive maintenance. One approach to mitigate this trade-off is to perform maintenance during scheduled production time by strategically shutting down equipment for short time periods. This research developed a systematic method on when to shut down equipment to do maintenance in an automotive assembly environment. It is called maintenance opportunity. The method incorporated real-time information about production and machine failure conditions. A simulation-based algorithm is developed by utilizing the buffer contents as well as machine starvation and congestion to obtain maintenance opportunities during production time.

Supervisory Factory Control Based on Real-Time Production Feedback

One key characteristic of any process performance is variability; that is, a process rarely performs consistently over time. The bottleneck is one of the main reasons causing the system variability and fluctuation in production. Short-term production analysis and short-term bottleneck identification are imperative to enable manufacturing operations to optimally respond to dynamic changes in system behavior. However, conventional throughput and bottleneck analysis focus on long-term statistic bottleneck identification, which is usually not applicable to a short-term period. An on-line supervisory control method is introduced to search for short-term production constraints with unknown machine reliability distribution and mitigate those constraints to improve system throughput. The control mechanism uses playback simulation of the real production data to identify the bottleneck station, and control parameters of that station to reach a near balanced production line operation by understanding the bottleneck inertia phenomenon. The results ensure the smooth flow of products on the production line and increase the line’s performance.

An integrated framework for effective service and repair in the automotive domain: An application of association mining and case-based-reasoning

In this paper, we propose a novel integrated framework combining association rule mining, case-based-reasoning and text mining that can be used to continuously improve service and repair in an automotive domain. The developed framework enables identification of anomalies in the field that cause customer dissatisfaction and performs root cause investigation of the anomalies. It also facilitates identification of the best practices in the field and learning from these best practices to achieve lean and effective service. Association rule mining is used for the anomaly detection and the root cause investigation, while case-based-reasoning in conjunction with text mining is used to learn from the best practices. The integrated system is implemented in a web based distributed architecture and has been tested on real life data.

A domain-specific decision support system for knowledge discovery using association and text mining

We propose a novel association and text mining system for knowledge discovery (ASTEK) from the warranty and service data in the automotive domain. The complex architecture of modern vehicles makes fault diagnosis and isolation a non-trivial task. The association mining isolates anomaly cases from the millions of service and claims records. ASTEK has shown 86% accuracy in correctly identifying the anomaly cases. The text mining subscribes to the diagnosis and prognosis (D&P) ontology, which provides the necessary domain-specific knowledge. The root causes associated with the anomaly cases are identified by discovering frequent symptoms associated with the part failures along with the repair actions used to fix the part failures. The best-practice knowledge is disseminated to the dealers involved in the anomaly cases. ASTEK has been implemented as a prototype in the service and quality department of GM and its performance has been validated in the real life set up. On an average, the analysis time is reduced from few weeks to few minutes, which in real life industry are significant improvements.

Trends in the development of system-level fault Dependency matrices

A Dependency matrix (D-matrix) is a consistent and systematic way to capture hierarchical system-level fault diagnostic information. The D-matrix is derived from a dependency modeling framework to capture the causal relationships between failure modes and symptoms. D-matrices are developed from various sources such as historical field failure data, service documents, engineering schematics, and Failure Modes, Effects and Criticality Analysis (FMECA) data. Here, we survey the existing research work on developing D-matrices from disparate data sources and data formats. We classify the D-matrices based on their data source and the imperfectness of symptoms for both boolean and real-valued [0,1] D-matrices. An industrial perspective is offered to describe the pros and cons of various types of D-matrices along with the challenges faced while developing and applying them for vehicle health management. 1 2

Modeling Machining Errors on a Transfer Line to Predict Quality

Abstract This paper introduces a methodology for predicting part quality based on the expected and measured process variations (geometric, static, and dynamic errors). Part quality in terms of dimensional (location), orientation, form, and profile tolerances can be predicted using a “stream of variation model” in a multistation machining system (serial, parallel, or hybrid) and validated on an engine cylinder head. The understanding gained from an application of this methodology to a machined component can help achieve substantial part-quality and process improvements.

Development, analysis and applications of a quantitative methodology for assessing customer satisfaction using evolutionary optimization

Graphical abstractDisplay Omitted HighlightsQuantitative modeling of customer satisfaction for consumer vehicles is proposed.Real-world service and sales datasets of five vehicle models are used.Model sensitivity to various features of the service datasets is studied.Classification rules for identifying dissatisfied customers are obtained.Method for identifying high-priority vehicular problems is proposed. Consumer-oriented companies are getting increasingly more sensitive about customers perception of their products, not only to get a feedback on their popularity, but also to improve the quality and service through a better understanding of design issues for further development. However, a consumers perception is often qualitative and is achieved through third party surveys or the companys recording of after-sale feedback through explicit surveys or warranty based commitments. In this paper, we consider an automobile companys warranty records for different vehicle models and suggest a data mining procedure to assign a customer satisfaction index (CSI) to each vehicle model based on the perceived notion of the level of satisfaction of customers. Based on the developed CSI function, customers are then divided into satisfied and dissatisfied customer groups. The warranty data are then clustered separately for each group and analyzed to find possible causes (field failures) and their relative effects on customers satisfaction (or dissatisfaction) for a vehicle model. Finally, speculative introspection has been made to identify the amount of improvement in CSI that can be achieved by the reduction of some critical field failures through better design practices. Thus, this paper shows how warranty data from customers can be utilized to have a better perception of ranking of a product compared to its competitors in the market and also to identify possible causes for making some customers dissatisfied and eventually to help percolate these issues at the design level. This closes the design cycle loop in which after a design is converted into a product, its perceived level of satisfaction by customers can also provide valuable information to help make the design better in an iterative manner. The proposed methodology is generic and novel, and can be applied to other consumer products as well.

Ontology-driven data collection and validation framework for the diagnosis of vehicle healthmanagement

The current warranty data collection processes exhibit several data quality issues – the level of detail and precision is missing in the collected data, the semantic heterogeneity is observed and no systematic data quality validation mechanism to automatically certify the data quality. Such data cannot be translated seamlessly into the knowledge assets to perform business functions, for example, fault diagnosis. An ontology-driven structured data collection framework is proposed to acquire the necessary data in the warranty domain. The proposed framework uses the integrated vehicle health management ontology as an information model to populate necessary data acquisition fields of the framework. A novel three-dimensional data quality metric is proposed to validate the completeness, correctness and relevance of newly collected data. We also evaluate the performance of the tool by using the real-life data. The data accuracy precision after using the framework has been improved from 0.30 to 0.80, whereas the recall is improved from 0.28 to 0.70. Furthermore, the precision and recall of the tool is evaluated for the 500 real-life field failure cases and it was greater than 90% for data completeness and relevance. Throughout this paper we will use the words ‘correctness’ and ‘accuracy’ interchangeably.

Automotive field failure analysis based on mileage — Feasibility & benefits

Most failures in the automotive systems depend on age and accumulated usage. Typically, these systems are covered under warranty for months-in-service (i.e. MIS/age) and a certain amount of usage (mileage) after the sales of the products. Warranty analysis of these systems enables manufacturers to understand field failures, and identify focus areas to make product improvements. Typically warranty analysis is performed based on MIS. However, mileage based warranty analysis has two added benefits - (1) some failures are, by their physical nature, related to mileage rather than age. Hence, mileage is a better indicator for observing and quantifying these failures. (2) Our observations also indicate that most vehicles leave warranty due to the mileage restrictions to the warranty coverage, rather than MIS. In such a scenario, mileage-based warranty calculations can provide us with early information. Warranty analysis based on mileage has been presented in the literature as a supplement to the traditional MIS based analysis. However, these are based on the following two major assumptions, and their validity has not been established yet: · The accumulation of miles is approximately linear with age. · The distribution of mileage accumulation rate in vehicles without any claims is same as that of vehicles which have at least one warranty claim. Using the real-time diagnostic data collected from Telematics systems, this work demonstrates that the above two assumptions are valid. As a result, we can achieve accurate warranty rates and take the advantage of early detection of problems using mileage. Early detection is based on high usage vehicles. We also demonstrate that the proportion of such vehicles is statistically significant, which enables mileage based analysis feasible earlier than age based analysis. This lead time is of crucial importance to the OEMs, especially close to the launch of new products, where they can identify and rectify field failures early.

Production Validation of Agile Machining Fixture: A Machine-Automated Reconfigurable Tooling

Agile machining fixture is a machine-automated reconfigurable tooling first introduced in 2003. The fixture is reconfigurable and programmable on a CNC machine. To evaluate its viability in production, the agile machining fixture is reconfigured from one machining operation to another for showing its flexibility to processing needs. The fixture is further tested and validated in a low volume production environment to address various challenges in product designs, machine conditions and process details. The good dimensional quality of the production parts machined in this study confirms that the agile machining fixture can perform as well as conventional ones on a regular CNC machine and thus deserves the consideration for future production implementation.Copyright