Transcript
00:01
Data can be used to optimize design for manufacturing or DFM.
00:06
Engineers can collect and analyze data on various materials and
00:10
manufacturing processes to determine which combinations yield the best results.
00:14
In terms of product quality, cost and efficiency.
00:18
Data driven decisions can lead to the selection of
00:21
materials and processes that are more suitable for DFM.
00:25
Digital literacy and data skills are needed for the following aspects of DFM.
00:30
One
00:31
material selection,
00:33
two
00:34
manufacturing process selection and improvement.
00:38
Three product life cycle management,
00:41
four simulation and modeling,
00:44
five communication and collaboration.
00:50
Engineers leverage digital tools and databases to
00:53
compare materials based on mechanical properties,
00:56
cost and environmental impact,
00:59
necessitating data skills for informed decision making.
01:02
Once design and performance requirements are established,
01:05
data aids in selecting materials by providing insights into heat resistance,
01:10
weight capacity and environmental friendliness.
01:14
Data literacy is a valuable skill enabling
01:17
engineers to collect and interpret relevant data.
01:21
Utilizing digital tools engineers can weigh multiple materials,
01:25
optimizing choices to enhance product performance,
01:27
reduce costs and promote sustainability.
01:31
Specialized digital tools,
01:32
assist engineers and designers in comparing and
01:35
selecting optimal materials for their specific cases.
01:41
As stated in the previous slide,
01:43
digital literacy empowers engineers to collect
01:46
and analyze data from manufacturing equipment,
01:49
sensors, enabling real time adjustments for process improvement.
01:53
We can gather data from various sources including
01:56
sensors,
01:58
equipment,
01:59
quality control
02:01
supply chain
02:02
material, databases,
02:04
equipment specifications
02:06
and past production records based on product requirements.
02:10
And now applying advanced data analytics and machine
02:13
learning to identify patterns and areas for improvement.
02:17
This can optimize manufacturing processes by
02:20
choosing the most suitable process
02:23
considering material properties
02:25
cost
02:26
production volume
02:28
and quality requirements,
02:30
implementing real time monitoring and data driven decision
02:33
making for swift responses to changes and disruptions
02:37
reduces defects and improves resource utilization.
02:41
An exemplar is Briggs automotive company or B AC
02:44
utilizing digital technology for design and manufacturing enhancements.
02:49
B AC used auto desk fusion generative design to create a multi
02:53
material hybrid carbon composite wheel optimizing
02:56
material placement for stiffness and performance.
02:60
B AC used auto desk vault and oracle netsuite to facilitate data organization,
03:05
design management and predictive maintenance for improved efficiency.
00:01
Data can be used to optimize design for manufacturing or DFM.
00:06
Engineers can collect and analyze data on various materials and
00:10
manufacturing processes to determine which combinations yield the best results.
00:14
In terms of product quality, cost and efficiency.
00:18
Data driven decisions can lead to the selection of
00:21
materials and processes that are more suitable for DFM.
00:25
Digital literacy and data skills are needed for the following aspects of DFM.
00:30
One
00:31
material selection,
00:33
two
00:34
manufacturing process selection and improvement.
00:38
Three product life cycle management,
00:41
four simulation and modeling,
00:44
five communication and collaboration.
00:50
Engineers leverage digital tools and databases to
00:53
compare materials based on mechanical properties,
00:56
cost and environmental impact,
00:59
necessitating data skills for informed decision making.
01:02
Once design and performance requirements are established,
01:05
data aids in selecting materials by providing insights into heat resistance,
01:10
weight capacity and environmental friendliness.
01:14
Data literacy is a valuable skill enabling
01:17
engineers to collect and interpret relevant data.
01:21
Utilizing digital tools engineers can weigh multiple materials,
01:25
optimizing choices to enhance product performance,
01:27
reduce costs and promote sustainability.
01:31
Specialized digital tools,
01:32
assist engineers and designers in comparing and
01:35
selecting optimal materials for their specific cases.
01:41
As stated in the previous slide,
01:43
digital literacy empowers engineers to collect
01:46
and analyze data from manufacturing equipment,
01:49
sensors, enabling real time adjustments for process improvement.
01:53
We can gather data from various sources including
01:56
sensors,
01:58
equipment,
01:59
quality control
02:01
supply chain
02:02
material, databases,
02:04
equipment specifications
02:06
and past production records based on product requirements.
02:10
And now applying advanced data analytics and machine
02:13
learning to identify patterns and areas for improvement.
02:17
This can optimize manufacturing processes by
02:20
choosing the most suitable process
02:23
considering material properties
02:25
cost
02:26
production volume
02:28
and quality requirements,
02:30
implementing real time monitoring and data driven decision
02:33
making for swift responses to changes and disruptions
02:37
reduces defects and improves resource utilization.
02:41
An exemplar is Briggs automotive company or B AC
02:44
utilizing digital technology for design and manufacturing enhancements.
02:49
B AC used auto desk fusion generative design to create a multi
02:53
material hybrid carbon composite wheel optimizing
02:56
material placement for stiffness and performance.
02:60
B AC used auto desk vault and oracle netsuite to facilitate data organization,
03:05
design management and predictive maintenance for improved efficiency.
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