Darek Ceglarek

Maintenance : Changing Role in Life Cycle Management

As attention to environmental problems grows, product life cycle management is becoming a crucial issue in realizing a sustainable society. Our objective is to provide the functions necessary for such a society while minimizing material and energy consumption. From this viewpoint, we should redefine the role of maintenance as a prime method for life cycle management. In this paper, we first discuss the changing role of maintenance from the perspective of life cycle management. Then, we present a maintenance framework that shows management cycles of maintenance activities during the product life cycle. According to this framework, we identify technical issues of maintenance and discuss the advances of technologies supporting the change in the role of maintenance.

Fixture failure diagnosis for autobody assembly using pattern recognition

In this paper, a fault diagnostic method is proposed for autobody assembly fixtures. This method uses measurement data to detect and isolate dimensional faults of part caused by fixture. The proposed method includes a predetermined variation pattern model and a fault mapping procedure. The variation pattern model is based on CAD information about the fixture geometry and location of the measurement points. This fault mapping procedure combines Principal Component Analysis with pattern recognition approach. Simulations and one case study illustrate the proposed method.

A Knowledge-Based Diagnostic Approach for the Launch of the Auto-Body Assembly Process

This paper is the first attempt to implement a knowledge-based diagnostic approach for the auto-body assembly process launch. This approach enables quick detection and localization of assembly process faults based on in-line dimensional measurements. The proposed approach includes an auto-body assembly knowledge representation and a diagnostic reasoning mechanism. The knowledge representation is comprised of the product, tooling, process, and measurement representations in the form of hierarchical groups. The diagnostic reasoning performs fault diagnostic in three steps. First, an initial statistical analysis of measurement data is performed. Next, the Candidate Component and Candidate Station with the hypothetical fault are searched. Finally, the fault symptom is identified and the root cause is suggested. Two case studies are presented to demonstrate the implementation of the proposed method.

Dimensional Fault Diagnosis for Compliant Beam Structure Assemblies

A model-based diagnostic methodology is proposed for the dimensional fault diagnosis of compliant beam structures in automotive or aerospace assembly processes. In the diagnosis procedure, the product measurement data are used to detect and isolate dimensional faults caused by part fabrication error in compliant beam assemblies. The proposed method includes a predetermined fault patterns model and a fault mapping procedure. The fault patterns are modeled by the diagnostic vectors derived from the inversed stiffness matrix of the beam structure. The fault mapping procedure combines principal component analysis (PCA) of measurement data and fault pattern recognition using statistical hypothesis tests. Verification of the proposed method is presented through simulations and one case study.

Sensor Optimization for Fault Diagnosis in Single Fixture Systems: A Methodology

Fixture fault diagnosis is a critical component of currently evolving techniques aimed at manufacturing variation reduction. The impact of sensor location on the effectiveness of fault-type discrimination in such diagnostic procedures is significant. This paper proposes a methodology for achieving optimal fault-type discrimination through an optimized configuration of defined “sensor locales.” The optimization is presented in the context of autobody fixturing—a predominant cause of process variability in automobile assembly. The evaluation criterion for optimization is an improvement in the ability to provide consistency of best match, in a pattern recognition sense, of any fixture error to a classified, anticipated error set. The proposed analytical methodology is novel in addressing optimization by incorporating fixture design specifications in sensor locale planning—constituting a Design for Fault Detectability approach. Examples of the locale planning for a single fixture sensor layout and an application to an industrial fixture configuration are presented to illustrate the proposed methodology.

Sensor Optimization for Fault Diagnosis in Multi-Fixture Assembly Systems With Distributed Sensing

Sensing for the system-wide diagnosis of dimensional faults in multi-fixture sheet metal assembly presents significant issues of complexity due to the number of levels of assembly and the number of possible faults at each level. The traditional allocation of sensing at a single measurement station is no longer sufficient to guarantee adequate fault diagnostic information for the increased parts and levels of a complex assembly system architecture. This creates a need for an efficient distribution of limited sensing resources to multiple measurement locations in assembly. The proposed methodology achieves adequate diagnostic performance by configuring sensing to provide an optimally distinctive signature for each fault in assembly. A multi-level, two-step, hierarchical optimization procedure using problem decomposition, based on assembly structure data derived directly from CAD files, is used to obtain such a novel, distributed sensor configuration. Diagnosability performance is quantified in the form of a defined index, which serves the dual purpose of guiding the optimization and establishing the diagnostic worth of any candidate sensor distribution. Examples, using a multi-fixture layout, are presented to illustrate the methodology.

Sensor Location Optimization for Fault Diagnosis in Multi-Fixture Assembly Systems

The effectiveness of fault diagnosis in assembly is contingent on the effectiveness of the sensor measurement of assembled parts. Using a diagnosability enhancement methodology for a single fixture, a means to achieve an optimal sensor configuration for a multi-fixture assembly of sheet metal parts is proposed. A Hierarchical Group description of the assembly is used to build a State-Transition representation which, with fixture CAD information, is used in multi-level hierarchical optimization to arrive at the optima. A defined Coverage Effectiveness Index quantifies fault isolation performance. The index also serves to evaluate the performance effectiveness of the measurement station location and change in the sensor number. The approach has significant utility in automotive body assembly where system complexity makes the choice of sensor location vital to fault isolation performance. Examples using multi-fixture simulated and industrial automotive body assembly sequences are provided to illustrate the methodology.

Design Evaluation of Sheet Metal Joints for Dimensional Integrity

The design of joints between parts is one of the most critical issues in the design of sheet metal assemblies. This paper presents a new part-to-part joint design evaluation index developed for dimensional control of sheet metal assemblies. The proposed index provides a new analytical tool to address the dimensional capabilities of an assembly process in the early product design stage. It covers the three basic types of joints, which encompass the whole domain of joints used in sheet metal assembly. The part-to-part interactions for each type of joint are studied, and an analytical model is provided. Two evaluation indices, (1) product joint design evaluation and (2) critical part determination, are developed. The developed methodology is demonstrated using two industrial design examples of sport-utility automotive bodies.

Tolerance allocation for compliant beam structure assemblies

This paper presents a tolerance allocation methodology for compliant beam structures in automotive and aerospace assembly processes. The compliant beam structure model of the product does not require detailed knowledge of product geometry and thus can be applied during the early design phase to develop cost-effective product specifications. The proposed method minimizes manufacturing costs associated with tolerances of product functional requirements (key product characteristics, KPCs) under the constraint(s) of satisfying process requirements (key control characteristics, KCCs). Misalignment and fabrication error of compliant parts, two critical causes of product dimensional variation, are discussed and considered in the model. The proposed methodology is developed for stochastic and deterministic interpretations of optimally allocated manufacturing tolerances. An optimization procedure for the proposed tolerance allocation method is developed using projection theory to considerably simplify the solution. The non-linear constraints, that ellipsoid defined by (stochastic case) or rectangle defined by T x (deterministic case) lie within the KCC region, are transformed into a set of constraints that are linear in σ (or T x )-coordinates. Experimental results verify the proposed tolerance allocation method.

Genetic-algorithms-based algorithm portfolio for inventory routing problem with stochastic demand

This paper presents an algorithm portfolio methodology based on evolutionary algorithms to solve complex dynamic optimisation problems. These problems are known to have computationally complex objective functions, which make their solutions computationally hard to find, when problem instances of large dimensions are considered. This is due to the inability of the algorithms to provide an optimal or near-optimal solution within an allocated time interval. Therefore, this paper employs a bundle of evolutionary algorithms (EAs) tied together with several processors, known as an algorithm portfolio, to solve a complex optimisation problem such as the inventory routing problem (IRP) with stochastic demands. EAs considered for algorithm portfolios are the genetic algorithm and its four variants such as the memetic algorithm, genetic algorithm with chromosome differentiation, age-genetic algorithm, and gender-specific genetic algorithm. In order to illustrate the applicability of the proposed methodology, a generic method for algorithm portfolios design, evaluation, and analysis is discussed in detail. Experiments were performed on varying dimensions of IRP instances to validate different properties of algorithm portfolio. A case study was conducted to illustrate that the set of EAs allocated to a certain number of processors performed better than their individual counterparts.