Conference Agenda

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Session Overview
SES 4.4: Quality engineering and management
Wednesday, 28/Jun/2017:
9:00am - 10:00am

Session Chair: Paul-Eric Dossou
Location: Aula P (first floor)

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259. Application of SPC and quality tools for process improvement

Sérgio Dinis Sousa1, Nuno Rodrigues2, Eusébio Nunes1

1ALGORITMI Research Center, Campus de Gualtar, University of Minho - DPS, 4710-057 Braga, Portugal; 2Production and Systems Department, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal

1. Introduction

The implementation of quality tools and methodologies is necessary to reduce defective items, and thus reducing the overall quality costs. This can be achieved by reducing process variability, allowing further increase in organization’s competitiveness and sustainability.

The quality function within a company ensures compliance with product specifications and implements process improvements [1], to produce with greater efficiency.

This work was developed at a metal parts production company with the objective of improve the company's production through the application of quality methodologies and tools. From a large set of items produced by the company it was selected as object of this study the one that presented higher percentage of nonconformities. These nonconformities were related to the non-compliance with the specification of one variable (dimension 51).

2. Methodology

The methodology used in this work can be structured in the following steps:

Step 1: Sample data collection of the critical variable in the production phase;

Step 2: Construction of X-s charts [3];

Step 3: Assessment of process capability (calculation of Capability index Cpk);

Step 4: Analysis of the control charts and, comparison with historical data obtained during pre-production.

Step 5: Identify assignable causes of variability. If necessary, conduct Measurement System Analysis;

Step 6: Development of improvement proposals, to reduce variability of the critical variable.

3. Results and Discussion

The implementation of the SPC chart showed the process in statistical control but lacking the ability to produce parts within specification limits (Cp<1).

An analysis of SPC charts allowed to identify changes in the mean and the variability of the process, when compared with the data obtained in the pre-production (Cpk_pre-production >1.33).)

For the analysis of the causes of process variability brainstorming sessions were held and cause-effect diagram was designed.

Through the R&R study [1] it was verified that the measuring system is unacceptable for dimensional control and thus improvement suggestions were given.

4. Conclusions

This study allowed to identify the main causes of variability in the production process of a metal part, through the application of quality tools, and to propose measures to improve process and reducing the percentage of defective parts.

Loss of process capability from the pre-production phase to the production phase can be seen as a measure of process degradation.

5. References

[1] J. M. Juran, and A.B. Godfrey, “Juran's Quality Handbook”, 5th ed., McGraw-Hill, 1999.

[2] N. Rodrigues, “Aplicação de ferramentas da Qualidade para melhoria da produção numa empresa de soluções industriais”, MSc thesis in Quality Eng and Mgmt, University of Minho, 2016.

[3] D. Montgomery, “Introduction to Statistical Quality Control”, Arizona, John Wiley & Sons, 2008.

108. Computer Aided Inspection procedures to support Smart Manufacturing of injection molded components

Michele Bici, Giovanni B. Broggiato, Francesca Campana, Alessandro Dughiero

Università degli Studi di Roma "La Sapienza", Italy

Nowadays, Reverse Engineering (RE), Computer Aided Tolerancing & Inspection (CAT&I) procedures and Product Data Management (PDM) systems can help “Smart (or Intelligent) Manufacturing”, through the planning, the automation and the post-processing of component's tolerance and quality inspection. Benefits of their adoption are enhanced predictions of the manufacturing problems and improvements of the product-process final quality. In our previous works, we discussed the integration of RE in CAT&I applied to electromechanical components made by injection molding, reporting algorithms and results that were focused onto procedures of feature recognition and measure from a point cloud.

In this work, we want to focus onto the data treatment after and before virtual measuring operations. For this reason, the paper will be organized into two macro-areas: one referred to procedures and algorithms applied before the measurement campaign, the other referred to data treatments used in “post-processing”.

The scanning and measuring operations are made through a laser scanner set on a CMM machine. In order to obtain a reliable and effective measurement campaign, pieces orientation and their layout on the reference table must be optimized, not only in the respect of the RE parameters but also considering that a large number of single very small size components must be evaluated per acquisition. In addition to the development of algorithms for layout and orientation optimization, also the development of algorithms for the laser scanner paths must be defined with the target of optimization of scan speed, keeping in mind pieces orientation and the obstruction represented by pieces in terms of visibility and scanner safety.

Passing from the CAD model to its convex hull (through a STL model), the developed algorithm researches all possible balance position for the part, and chooses the best three according to criteria as stability and visibility. Then, for each chosen position, the view perspective is reproduced, evaluating how many points are visible. We take into count the amplitude of the measurement range, occlusions and obstacles represented by the pieces themselves and the angle between the local surface’s normal and the scanner (paying attention to the differences between camera and laser).

In the second part of the paper, we analyze and discuss the automatic treatment of data after the virtual measure done with a MATLAB routine. Through these algorithms, we find plane and cylindrical local surfaces. The obtained results are compared with the nominal quotes derived from CAD model and also with ones achieved from a conventional experimental measurement campaign. An automatic procedure has been developed to resume results of the comparison in the draft file of the piece. The output of the whole process is a mix between the CAD model and a PDM, in order to obtain the direct and objective evidence of molds’ quality.

In the paper, procedures and algorithms of both sections are described, after a briefly state of the art. In the final part for each section, case studies are presented and discussed. In the last part, we present future developments and targets according to a "Smart Manufacturing" implementation.

131. Design for Inspection - Evaluating the Inspectability of Aerospace Components in the Early Stages of Design

Roland Stolt1, Fredrik Elgh1, Petter Andersson2

1Jonkoping University, Sweden, Sweden; 2GKN Aerospace AB , Flygmotorvägen 1, SE-46181 Trollhättan, Sweden

One important part of the manufacturing process of aerospace components is making inspections using Fluorescent Penetrant Inspection (FPI). This mandatory inspection represents a non-negligible part of the manufacturing and service cost. It is therefore important to make the geometry of the components suitable for inspection i.e. practicing Design for Inspection (DFI). This has been studied at an aerospace company with the aim of bringing DFI to the early stages of product development process. In this paper, a tool is proposed for the evaluation of inspectability in the early design stages. The tool is applied on CAD-models of the components automatically ranking the inspectability of design proposals using a novel inspectability index. Thus, inspectability can be considered together with other performance and manufacturing aspects forming a powerful decision support. The tool has been run and evaluated together with manufacturing staff at the aerospace company with promising results.

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