A fundamental component of world-class manufacturing (WCM) is that of total productive maintenance (TPM), linked to both total quality management (TQM) and the concepts of continuous-¯ow manufacturing which are embedded in cellular manufacturing.An investigation was conducted in collaboration with a ®rst tier automotive component supplier to determine the overall equipment effectiveness (OEE) of a semi-automated assembly cell. The big losses associated with equipment effectiveness were also identi®ed.The production output of the cell over the observed period was 26 515. This represents 97% good components, 0.33% scrap and 2.67% rework. The number of stoppages recorded was 156, where the 10 most common causes were identi®ed. The OEE was 62% and the six big losses represent 38% loss of the productive time.Based on the ®ndings, it was recommended that a pilot project to be conducted to implement a TPM programme for the cell and expand it further to the other cells in the factory. #
Vehicle safety has increasingly become an economical factor for vehicle manufacturers and this has become most apparent in car safety [1-4]. Manufacturers are now spending considerable resources on safety research. Government requirements on safety have compelled manufacturers to carry out considerable number of crash tests to validate the safety of their cars [6-7]. The data from these tests is important in the development of simulation models employing finite element (FE) software. Many companies predict crashworthiness using commercially available software such as PAMCARSH and LS-DYNA. These simulations are based on mathematical constitutive equations and hence any simulation created is only as representative as the constitutive equations used. This project has studied the reliability of the material models used by LS-DYNA. Material models selected for analysis are used extensively by impact simulations software and were namely: Power Law Plasticity and Cowper/Symonds. Piecewise Linear Plasticity was also selected because it is based on a true stress/strain and is expected that the simulation would be representative. The models were developed using Belytschko-Lin-Tsay shell elements and were compared with experimental tests employing uni-axial tension strips carried out on three materials – aluminium, high strength steel and mild steel. The tests were carried out using a DARTEC tensile testing machine (up to strain rate of 2.0s-1) at UCE in Birmingham. Testing for the higher strain rates (aluminium up to 269.1s-1, mild steel up to 460s-1, and high strength steel up to 456.9s-1), were carried out at The Royal Military College, Shrivenham using a ROSAND tester.
For reasons of cost and weight, light gauge sheet is used wherever possible for metal fabrications. In sheet metal forming the process is to gather the metal into defined areas. The pulley forming process is no exception and is achieved by superimposing axial loads on top of radial loads using a pressure-controlled tailstock. Whilst the headstock-mounted tooling is fixed, that part held on the tailstock can be powered axially under controlled pressure. This pressure is governed by the width of the workpiece which changes during the forming process. Experiments have been designed to provide an understanding of the pulley forming process and to verify numerical models. The latter has been taken the form of finite element simulations to enable prediction of metal flow, tool forces and potential sources of defects and failures. There are three objectives for conducting the experiments which have been investigated in this paper: 1. providing data to define the movements of the forming tools for the finite element model, including displacements and velocities, 2. understanding the effects of the pulley forming operation on the flow of material, and 3. validating the finite element model.
A research investigation is presented which discusses the practicality of using several image processing and knowledge based techniques for the measurement and classification of cold rolled steel sections. Image analysis techniques can be applied to many different applications and assessing the quality and the accuracy of cold roll formed steel sections is no exception. The operations detailed within this paper are both traditional image processing methods and novel neural network based techniques which are combined together to give a bespoke alternative to the manual processing currently employed to test these sections. The results show the suitability of using image analysis and image processing to aid in the quality control of cold steel roll forming and initial tests have demonstrated great potential for this work.
Quality Management Systems (QMS) of volume production car makers tend to be large. Complexity levels in such structures are difficult to manage. From the design release to the production stage, a manufacturer has to control roughly 9000 parts (including fixings) for a large size car. A large number of subsystems, together with their coupled interactions increase a system’s complexity. Results of the application of a model to manage complexity in some automotive companies in the West Midlands (UK) suggest that complexity reduction leads to quality improvements.
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