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(1) Manufacturing
System Realization
Our research focuses on the integration of
manufacturing system CAD/CAM models with statistics-based methods
for design, control and diagnostics of multistage processes.
To this end our research addresses the following:
Recent studies show
that major challenges in design and implementation of complex
manufacturing systems are related to system failures and quality
deterioration during ramp-up phase. As high as 72% of root causes
of process variation in automotive assembly are due to fixture
failure [2].
However, relatively little
was done on systematic fixture failure diagnosis during ramp-up of
a new manufacturing system.
Traditional quality improvement methods do not provide systematic
approaches for fault root cause diagnostics. Until recently,
existing methods for quality improvement were primarily based on
statistical analysis of in-process measurements detecting process
change rather than determining root cause. Such methods are not
always effective for complex systems such as auto body assembly
and may not be applicable for new product/process launches
(ramp-up phase) due to the unavailability of historical
statistical data, pre-enumerated faults or identified case
studies. Yet, diagnostics methodologies consider limited
discrete definition of fault (fault/no fault or 1/0), which is
insufficient for quality improvement and ramp-up of new
manufacturing systems.
Our research has led to the development of a methodology for
quality improvement by root cause diagnosis of dimensional
variability in multiple station manufacturing processes (MMP) [1,
3, 17,
20]. This requires developing statistical
methods driven by engineering models (CAD/CAM) to isolate fault
root cause. This is a departure
from classical approaches in statistic, which utilize previously
captured data or solved case studies. Additionally, fault
definition is expanded to cover cases where fault is understood as
a continuous function spanning between 0 and 1. This integration
holds a promising direction for quality engineering research and
is especially relevant given the lack of historical data in new
and short-run manufacturing processes.
We proposed a new methodology for in-process dimensional fault
diagnostics and quality improvement of multi-station body assembly
systems by combining process variability detection, isolation and
root cause determination [1,
3, 12,
16, 17,
20]. In this research, a new hierarchical
model of assembly architecture and sequence is developed.
This represents a multi-level body assembly system [1,
3, 17] based on which a set of correlation clustering
and diagnostic reasoning techniques are studied. These
techniques are based on the developed fixture failure model
(CAD/CAM) and its relationship with measurement data represented
as principal components analysis (PCA) [3,
17, C9]. Our research proved the inherent
relationship between fixture failures and PCA [3]. This
builds a foundation for fixture fault diagnosis that further led
to the knowledge-based dimensional fault diagnosis methodology,
used during the launch of new automotive body assembly processes.
The implementation of this methodology helped to: (a)
accomplish the 2-mm dimensional variation level for automotive
body, achieved for the first time by a US auto assembly (JNAP/DaimlerChrysler);
(b) receiving the 1998 J.D. Powers award by Bramalea Plant,
DaimlerChrysler for best quality vehicle in the US market.
Additionally, it became part of the Chrysler Operating System
(COS) in 1998 for all new car and truck launches and of GM’s
“BIW Data Analyzer” software.
We feel that our work provides a theoretical basis for process
variability diagnosis for sheet metal assembly. This has created
the dual benefits of continuously improving quality while at the
same time reducing overall product development time through rapid
fault root cause detection. It also provides a model for optimal
sensor placement for single fixture [9]
and for multistage manufacturing processes (MMP) with end-of-line
sensing [7] or with
distributed in-process sensing [11,
23] (please
also see (2) Sensor
Networks in Manufacturing). This further led to
evaluation and optimizing of assembly design [8,
19] and tolerancing study [21,
27, C14] presented in (3) Integration
of Product and Process Characteristics.
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