Enhancing manufacturing operations with synthetic data: a systematic framework for data generation, accuracy, and utility

Addressing the challenges of data scarcity and privacy, synthetic data generation offers an innovative solution that advances manufacturing assembly operations and data analytics. Serving as a viable alternative, it enables manufacturers to leverage a broader and more diverse range of machine learning models by incorporating the creation of artificial data points for training and evaluation. Current methods lack generalizable framework for researchers to follow and solve these issues. The development of synthetic data sets, however, can make up for missing samples and enable researchers to understand existing issues within the manufacturing process and create data-driven tools for reducing manufacturing costs. This paper systematically reviews both discrete and continuous manufacturing process data types with their applicable synthetic generation techniques. The proposed framework entails four main stages: Data collection, pre-processing, synthetic data generation, and evaluation. To validate the framework’s efficacy, a case study leveraging synthetic data enabled an exploration of complex defect classification challenges in the packaging process. The results show enhanced prediction accuracy and provide a detailed comparative analysis of various synthetic data strategies. This paper concludes by highlighting our framework’s transformative potential for researchers, educators, and practitioners and provides scalable guidance to solve the data challenges in the current manufacturing sector

Taxonomies for Reasoning About Cyber-physical Attacks in IoT-based Manufacturing Systems

The Internet of Things (IoT) has transformed many aspects of modern manufacturing, from design to production to quality control. In particular, IoT and digital manufacturing technologies have substantially accelerated product development- cycles and manufacturers can now create products of a complexity and precision not heretofore possible. New threats to supply chain security have arisen from connecting machines to the Internet and introducing complex IoT-based systems controlling manufacturing processes. By attacking these IoT-based manufacturing systems and tampering with digital files, attackers can manipulate physical characteristics of parts and change the dimensions, shapes, or mechanical properties of the parts, which can result in parts that fail in the field. These defects increase manufacturing costs and allow silent problems to occur only under certain loads that can threaten safety and/or lives. To understand potential dangers and protect manufacturing system safety, this paper presents two taxonomies: one for classifying cyber-physical attacks against manufacturing processes and another for quality control measures for counteracting these attacks. We systematically identify and classify possible cyber-physical attacks and connect the attacks with variations in manufacturing processes and quality control measures. Our taxonomies also provide a scheme for linking emerging IoT-based manufacturing system vulnerabilities to possible attacks and quality control measures

MSEC2006-21087 VARIATION PROPAGATION ANALYSIS ON COMPLIANT ASSEMBLIES CONSIDERING CONTACT INTERACTION

ABSTRACT Dimensional variation is inherent to any manufacturing process. In order to minimize its impact on assembly products is important to understand how it propagates through the assembly process. Unfortunately, manufacturing processes are complex and in many cases highly non-linear. Traditional assembly models have represented assembly as a linear process. However, assemblies that include the contact between their components and tools show a highly non-linear response. This paper presents a new assembly methodology considering the contact effect. In addition, an efficient to predict output response is presented. The enhance dimension reduction method (eDR) is used to accurately and efficiently predict the statistical response of the assembly to variation on the input parameters

Variation propagation analysis on compliant assemblies considering contact interaction

Dimensional variation is inherent to any manufacturing process. In order to minimize its impact on assembly products it is important to understand how the variation propagates through the assembly process. Unfortunately, manufacturing processes are complex and in many cases highly nonlinear. Traditionally, assembly process modeling has been approached as a linear process. However, many assemblies undergo highly complex nonlinear physical processes, such as compliant deformation, contact interaction, and welding thermal deformation. This paper presents a new variation propagation methodology considering the compliant contact effect, which will be analyzed through nonlinear frictional contact analysis. Its variation prediction will be accurately and efficiently conducted using an enhanced dimension reduction method. A case study is presented to show the applicability of the proposed methodology

Modeling and diagnosis of dimensional variation for assembly systems with compliant parts.

Sheet metal assembly is widely used in the fabrication of automotive body structures, aircraft fuselages, office furniture and home appliances. One of the most important challenges for sheet metal assembly is the dimensional variation. Dimensional variation can stem from both the design and manufacture of a product and may affect the final product functionality and process performance. Therefore, dimensional variation reduction in assembly processes is necessary to improve the final product quality. The purpose of this research is to develop a variation propagation model for multi-station assembly system with compliant sheet metal parts. The model is used to study how the variation propagates from the components and tooling to the final product. In addition, a diagnostic tool to isolate multiple fixture fault occurrences for compliant parts is developed. The proposed methodologies are applied to automotive body assembly. The main contribution of this dissertation can be summarized as: (1) Multi-station sheet metal assembly model or compliant parts. A methodology for variation propagation analysis in compliant sheet metal assembly is presented. The model uses a state space representation and extended the method of influence coefficients to a multi-station system. The multi-station model estimates how variation propagates from the components and tooling to the final product during the assembly process. (2) Sheet metal assembly modeling using geometric covariance. A new method for variation analysis was developed using the components covariance matrix. The method, called variation vectors, replaces the method of influence coefficients. The method combines principal component analysis (PCA) with finite element methods to estimate the effect of components variation on assembly variation. This methodology can significantly reduce the computation effort required for variation analysis in sheet metal assembly. (3) Fixture location impact on dimensional variation for sheet metal assembly. The proposed methodology focuses on the impact of fixture position on the dimensional quality of sheet metal assemblies, considering part and tooling variation and assembly springback. An optimization algorithm is presented that combines finite element analysis and nonlinear programming methods to find the optimal fixture position such that the assembly variation is minimized. (4) Multiple fixture fault diagnosis considering a N-2-1 locating scheme. Using designated component analysis, it is possible to successfully isolate multiple fixtures faults in compliant sheet metal locating systems. DCA extracts the significance contribution of specific designated pattern to the total variation of the production measurement data.Ph.D.Applied SciencesMechanical engineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/123140/2/3068836.pd

Assembly faults diagnosis using neural networks and process knowledge

Traditional manufacturing process fault monitoring and detection methodologies have been based on Statistical Process Control (SPC) charts and rules. In most cases, SPC is used to detect a fault or an out-of-control condition while fault diagnosis relies on operator expertise to identify the potential root causes. Current sensor developments allow for the acquisition of large amounts of data from parts and processes in a manufacturing environment. In addition, new modeling tools have increased the efficiency and accuracy of process modeling, providing useful knowledge about product-processes interaction. This paper presents a new methodology for fault diagnosis using a Feed Forward Back-Propagation Neural Network. The proposed neural network is trained using process knowledge and then applied to the detection of manufacturing process faults. The methodology results in a modified control chart that uses measurement data from the assembly components and plots an indicator representing the presence or absence of a predefined fault. Two case studies are presented: a diagnosis system for fixture faults in a generic assembly process, and a diagnosis tool for fault detection and identification in an automobile door assembly. Copyright © 2007 by ASME

Monitoring and diagnosis of assembly fixture faults using modified multivariate control charts and surface scanning content

Recent advances in process monitoring technology have introduced an influx of exceptionally large data sets containing information on manufacturing process health. Recorded data sets are comprised of numerous parameters for which multivariate statistical process control (MSPC) methodologies are required. Current multivariate control charts are ideal for monitoring data sets with a minimal amount of parameters, however, new monitoring devices such as surface scanning cameras increase the number of parameters by two orders of magnitude in some cases. This paper proposes a modified form of the original multivariate Hotelling T2 chart possessing the capability to monitor manufacturing processes containing a large number of parameters and a fault diagnosis procedure incorporating least squares analysis in conjunction with univariate control charts. A case study considering surface scanning of compliant sheet metal components and comparisons to processes utilizing Optical CMM\u27s is presented as verification of the proposed assembly fixture fault diagnosis methodology and modified Hotelling T2 multivariate control chart. Copyright © 2007 by ASME

Quality monitoring and fault detection on stamped parts using DCA and LDA image recognition techniques

New vision technologies provide an opportunity for fast detection and diagnosis of quality problems compared with traditional dimensional measurement techniques. This paper proposes a new use of image processing to detect quality faults using images traditionally obtained to guide manufacturing processes. The proposed method utilizes face recognition tools to eliminate the need of specific feature detection on determining out-of-specification parts. The algorithm is trained with previously classified images. New images are then classified into two groups, healthy and unhealthy. This paper proposes a method that combines Discrete Cosine Transform (DCT) with either Principal Component Analysis (PCA) or Linear Discriminant Analysis (LDA) to detect faults, such as cracks, directly from sheet metal parts. Copyright © 2008 by ASME

Modeling and Control of Compliant Assembly Systems

The assembly of compliant, non-rigid parts is widely used in automotive, aerospace, electronics, and appliance manufacturing. Dimensional variation is one important measure of quality in such assembly. This paper presents models for analyzing the propagation of dimensional variation in multi-stage compliant assembly systems and the use of such models for robust design and adaptive control of assembly quality. The models combine engineering structure analysis with advanced statistical methods in considering the effect part variation, tooling variation, as well as part deformation due to clamping, joining and springback. The new adaptive control algorithm makes use of the fine adjustment capabilities in new programmable tooling in achieving reduction of assembly variation