2022 SDSU Data Science Symposium Presentation Abstracts

This document contains abstracts for presentations and posters 2022 SDSU Data Science Symposium

2022 SDSU Data Science Symposium Presentation Abstracts

This document contains abstracts for presentations and posters 2022 SDSU Data Science Symposium

2022 SDSU Data Science Symposium Program

https://openprairie.sdstate.edu/ds_symposium_programs/1003/thumbnail.jp

Developing Statistical Models to Assess Productivity in the Automotive Manufacturing Sector

The purpose of this study is to identify the most important activity in a value chain, effective factors, their impact, and to find estimation models of the most well-known productivity measurement, Hours per Vehicle (HPV), in the automotive industry in North American manufacturing plants. HPV is a widely recognized production performance indicator that is used by a significant percentage of worldwide automakers. During a comprehensive literature review, 13 important factors that affect HPV were defined as launching a new vehicle, ownership, car segment, model types, year, annual available working days, vehicle variety, flexibility, annual production volume, car assembly and capacity (CAC) utilization, outsourcing, platform strategy, and hourly employee\u27s percentage.;Data used in this study was from North American plants that participated in the Harbour\u27s survey from 1999 to 2007. Data are synthesized using a uniform methodology from information supplied by the plants and supplemented with plant visits by Harbour Consulting auditors. Overall, there are 682 manufacturing plants in the statistical sample from 10 different multinational automakers.;Several robust and advanced statistical methods were used to analyze the data and derive the best possible HPV regression equations. The final statistical models were validated through exhaustive cross-validation procedures. Mixed integer distributed ant colony optimization (MIDACO) algorithm, a nonlinear programming algorithm, that can robustly solve problems with critical function properties like high non-convexity, non-differentiability, flat spots, and even stochastic noise was used to achieve HPV target value.;During the study period, the HPV was reduced 48 minutes on the average each year. Annual production volume, flexible manufacturing, outsourcing, and platform strategy improve HPV. However, vehicle variety, model types, available annual working days, CAC, percentage of the hourly employees, and launching a new model penalize HPV. Japanese plants are the benchmark regarding the HPV followed by joint ventures and Americans. On average, the HPV is lower for Japanese and joint ventures in comparison to American automakers by about 1.83 and 1.28 hours, respectively. Launching a new model and adding a new variety in body styles or chassis configurations raises the HPV, depending on the car class; however, manufacturing plants compensate for this issue by using platform sharing and flexible manufacturing strategies. While launching a new vehicle common platform sharing, flexible manufacturing, and more salaried employees (lower hourly) strategies will help carmakers to overcome the effect of launching new vehicles productivity penalization to some extent.;The research investigates current strategies that help automakers to enhance their production performance and reduce their productivity gap. The HPV regression equations that are developed in this research may be used effectively to help carmakers to set guidelines to improve their productivity with respect to internal and external constraints, strengths, weaknesses, opportunities, and threats

Detecting anomalies in multivariate time series from automotive systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In the automotive industry test drives are conducted during the development of new vehicle models or as a part of quality assurance for series vehicles. During the test drives, data is recorded for the use of fault analysis resulting in millions of data points. Since multiple vehicles are tested in parallel, the amount of data that is to be analysed is tremendous. Hence, manually analysing each recording is not feasible. Furthermore the complexity of vehicles is ever-increasing leading to an increase of the data volume and complexity of the recordings. Only by effective means of analysing the recordings, one can make sure that the effort put in the conducting of test drives pays off. Consequently, effective means of test drive analysis can become a competitive advantage. This Thesis researches ways to detect unknown or unmodelled faults in recordings from test drives with the following two aims: (1) in a data base of recordings, the expert shall be pointed to potential errors by reporting anomalies, and (2) the time required for the manual analysis of one recording shall be shortened. The idea to achieve the first aim is to learn the normal behaviour from a training set of recordings and then to autonomously detect anomalies. The one-class classifier “support vector data description” (SVDD) is identified to be most suitable, though it suffers from the need to specify parameters beforehand. One main contribution of this Thesis is a new autonomous parameter tuning approach, making SVDD applicable to the problem at hand. Another vital contribution is a novel approach enhancing SVDD to work with multivariate time series. The outcome is the classifier “SVDDsubseq” that is directly applicable to test drive data, without the need for expert knowledge to configure or tune the classifier. The second aim is achieved by adapting visual data mining techniques to make the manual analysis of test drives more efficient. The methods of “parallel coordinates” and “scatter plot matrices” are enhanced by sophisticated filter and query operations, combined with a query tool that allows to graphically formulate search patterns. As a combination of the autonomous classifier “SVDDsubseq” and user-driven visual data mining techniques, a novel, data-driven, semi-autonomous approach to detect unmodelled faults in recordings from test drives is proposed and successfully validated on recordings from test drives. The methodologies in this Thesis can be used as a guideline when setting up an anomaly detection system for own vehicle data

Pilot To Full-Scale Production: A Battery Module Assembly Case Study

Electric vehicles are currently on the rise due to environmental and legal concerns. Furthermore, improvements made in battery assembly steadily boosts the efficiency of electric vehicles. A well-prevalent method to overcome the uncertainties that emerge from the ever-changing battery technology, is to assemble products using pilot production lines. However, literature pertaining to the scale-up of pilot production lines for full scale production is scarce. Therefore, in this paper, potential scale-up scenarios for battery module assembly line are proposed in a discrete event simulation software and results are compared. Furthermore, the benefits of the proposed method are discussed with a test case

What determines product ramp-up performance? : a review of characteristics based on a case study at Nokia Mobile Phones

We present a conceptual model to explore the essential characteristics that affect product ramp-up performance in the consumer electronics industry, specifically in the mobile phones sector. Our findings are based on data analysis within Nokia’s mobile phones business group. Fast product ramp-ups are particularly critical for companies in which short product lifecycles prevail and in which development teams are required to work on new development projects than spending time with ramp-up support. Our model analyzes, extends and structures the results from other studies into five main characteristics: the product architecture, the product development process, the logistics system, the manufacturing capability and the external environment. We discuss the factors that describe and represent these five main characteristics on a quantitative basis and assess the impact of these characteristics on ramp-up performance with different measures in the model

Rotational and ply-level uncertainty in response of composite shallow conical shells

This paper presents the quantification of rotational and ply level uncertainty of random natural frequency for laminated composite conical shells by using surrogate modeling approach. The stochastic eigenvalue problem is solved by using QR iteration algorithm. Sensitivity analysis is carried out to address the influence of different input parameters on the output natural frequencies. The sampling size and computational cost is reduced by employing the present approach compared to direct Monte Carlo simulation. The stochastic mode shapes are also depicted for a typical laminate configuration. Statistical analysis is presented to illustrate the results and its performance

Estimation of Nonstationary Process Variance in Multistage Manufacturing Processes Using a Model-Based Observer

In this paper, we propose a recursive algorithm to estimate the process variance in multistage manufacturing or assembly processes. We use a replicated model that includes the process variance to be estimated as a time-varying state that changes slowly. For this model, we develop an estimation strategy including tuning parameters that play a direct role in the tradeoff between the estimation accuracy and the adaptation to changes. We also develop a statistical confidence interval for the estimations which enhances the decision of whether the process variances have changed. Unlike other batch methods in the literature, our proposal is computed recursively, and it allows us to tune the tradeoff between the convergence speed and the accuracy without modifying the sample size, which only contains the data of the last manufactured piece