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Production Process Development

Sundbye, Richard LU and Strandberg, Marcus LU (2019) MMTM01 20191
Production and Materials Engineering
Abstract
The master thesis is a collaboration between Trelleborg AB and the division of Production and Materials Engineering at Lund University, Faculty of Engineering. The objective of the thesis has been to analyse the current production system from a lean perspective, identify an area in need of improvement and generate concrete solutions for how the studied area can be improved in terms of cost, performance and from a lean perspective.

The production system of a polymer product consists of three major processes: molding, post-molding and packing. The lean analysis identified a bottleneck in the manual quality inspection within the post-molding process. It was discovered that an introduction of automation is needed due to the manual quality... (More)
The master thesis is a collaboration between Trelleborg AB and the division of Production and Materials Engineering at Lund University, Faculty of Engineering. The objective of the thesis has been to analyse the current production system from a lean perspective, identify an area in need of improvement and generate concrete solutions for how the studied area can be improved in terms of cost, performance and from a lean perspective.

The production system of a polymer product consists of three major processes: molding, post-molding and packing. The lean analysis identified a bottleneck in the manual quality inspection within the post-molding process. It was discovered that an introduction of automation is needed due to the manual quality inspection having reached its lower time limit, thus limiting the overall production capacity.

Five non-destructive technologies were investigated and evaluated based on the ability to identify the known range of product defects, consisting of: cracks, surface defects and geometry defects. The evaluated technologies were vision sensors, laser sensors, leakage testing, ultrasonic testing, and computer tomography. The results showed that at best, approximately 50-70 % of the defects are possible to detect in an automated system.

The potential of an automated quality inspection is dependent on the investment cost in relation to level of automation, scalability and flexibility. A combination of vision and laser sensors is the most preferable solution and reaches an automation level of approximately 50 % together with the largest cost reduction as well as the highest scalability and flexibility. (Less)
Please use this url to cite or link to this publication:
author
Sundbye, Richard LU and Strandberg, Marcus LU
supervisor
organization
course
MMTM01 20191
year
type
H2 - Master's Degree (Two Years)
subject
report number
CODEN:LUTMDN/(TMMV-5301)/1-74/2019
language
English
id
8991311
date added to LUP
2019-08-05 08:10:58
date last changed
2019-08-05 08:10:58
@misc{8991311,
  abstract     = {The master thesis is a collaboration between Trelleborg AB and the division of Production and Materials Engineering at Lund University, Faculty of Engineering. The objective of the thesis has been to analyse the current production system from a lean perspective, identify an area in need of improvement and generate concrete solutions for how the studied area can be improved in terms of cost, performance and from a lean perspective.

The production system of a polymer product consists of three major processes: molding, post-molding and packing. The lean analysis identified a bottleneck in the manual quality inspection within the post-molding process. It was discovered that an introduction of automation is needed due to the manual quality inspection having reached its lower time limit, thus limiting the overall production capacity.

Five non-destructive technologies were investigated and evaluated based on the ability to identify the known range of product defects, consisting of: cracks, surface defects and geometry defects. The evaluated technologies were vision sensors, laser sensors, leakage testing, ultrasonic testing, and computer tomography. The results showed that at best, approximately 50-70 % of the defects are possible to detect in an automated system.

The potential of an automated quality inspection is dependent on the investment cost in relation to level of automation, scalability and flexibility. A combination of vision and laser sensors is the most preferable solution and reaches an automation level of approximately 50 % together with the largest cost reduction as well as the highest scalability and flexibility.},
  author       = {Sundbye, Richard and Strandberg, Marcus},
  language     = {eng},
  note         = {Student Paper},
  title        = {Production Process Development},
  year         = {2019},
}