Innovative Methods for Small Mixed Batches Production System Improvement: The Case of a Bakery Machine Manufacturer

One of the common problems of organizations with turn-key projects is the high scrap rate. There exist such traditional methods as Lean Six Sigma (LSS) and DMAIC tools that analyze causes and suggest solutions. New emerging intelligent technologies should influence these methods and tools as they affect many areas of our life. The purpose of this paper is to present the innovative Small Mixed Batches (SMB). The standard set of LSS tools is extended by intelligent technologies such as artificial neural networks (ANN) and machine learning. The proposed method uses the data-driven quality strategy to improve the turning process at the bakery machine manufacturer. The case study shows the step-by-step DMAIC procedure of critical to quality (CTQ) characteristics improvement. Findings from the data analysis lead to a change of measurement instrument, training of operators, and lathe machine set-up correction. However, the scrap rate did not decrease significantly. Therefore the advanced mathematical model based on ANN was built. This model predicts the CTQ characteristics from the inspection certificate of the input material. The prediction model is a part of a newly designed process control scheme using machine learning algorithms to reduce the variability even for input material with different properties from new suppliers. Further research will be focused on the validation of the proposed control scheme, and acquired experiences will be used to support business sustainability.

Tiefziehen mittels adaptiver Platinenlage/Control of deep drawing processes by means of adaptive blank positioning

Bei der Serienproduktion von Blechbauteilen können schwankende Blecheigenschaften zu zeitintensiven Anlaufphasen und erhöhten Ausschussraten führen. Diesen Herausforderungen wird heute mit werkzeugbasierten Regelungssystemen begegnet, welche aber eine komplexe und kostenintensive Modifikation der bestehenden Werkzeuge beziehungsweise Pressen erfordern. Vor diesem Hintergrund befasst sich der Beitrag mit einem relativ einfachen und kostengünstigen Regelungsansatz auf Basis einer adaptiven Platinenpositionierung.   In series production of sheet metal components, varying sheet metal properties may lead to an increase of try-out time phase and scrap rate. Today, these challenges are met by tool-based control systems, which, however, require a complex and cost-intensive modification of the existing die or press technology. Against this background, this paper deals with a relatively simple but cost-effective adaptive control concept based on adjustable blank positioning.

An FCM–GABPN Ensemble Approach for Material Feeding Prediction of Printed Circuit Board Template

Accurate prediction of material feeding before production for a printed circuit board (PCB) template can reduce the comprehensive cost caused by surplus and supplemental feeding. In this study, a novel hybrid approach combining fuzzy c-means (FCM), feature selection algorithm, and genetic algorithm (GA) with back-propagation networks (BPN) was developed for the prediction of material feeding of a PCB template. In the proposed FCM–GABPN, input templates were firstly clustered by FCM, and seven feature selection mechanisms were utilized to select critical attributes related to scrap rate for each category of templates before they are fed into the GABPN. Then, templates belonging to different categories were trained with different GABPNs, in which the separately selected attributes were taken as their inputs and the initial parameter for BPNs were optimized by GA. After training, an ensemble predictor formed with all GABPNs can be taken to predict the scrap rate. Finally, another BPN was adopted to conduct nonlinear aggregation of the outputs from the component BPNs and determine the predicted feeding panel of the PCB template with a transformation. To validate the effectiveness and superiority of the proposed approach, the experiment and comparison with other approaches were conducted based on the actual records collected from a PCB template production company. The results indicated that the prediction accuracy of the proposed approach was better than those of the other methods. Besides, the proposed FCM–GABPN exhibited superiority to reduce the surplus and/or supplemental feeding in most of the case in simulation, as compared to other methods. Both contributed to the superiority of the proposed approach.

Scrap Reduction in TMT Reinforced Bar Production by the Application of Lean Techniques

Lean aims to eliminate the non-value-added activity which affects the cost of production along with the scrap rate Lean is often considered as a collection of tools and practices for superior operational and financial performance, through process improvement. Lean is an improvement concept for operational performance in the manufacturing environment. In this work, lean techniques are applied to the manufacturing of random rods at a leading manufacturing company of Thermo Mechanically Treated (TMT) reinforced bars to reduce the scrap. The hot billet coming from the extrusion machine is sent to the rolling mill machine where the TMT bars are produced; this consists of three stages – roughing mill, shear cutting, and programming logical controller. During the shear cut operation performed on the billet, a large amount of scrap was being generated. To reduce this scrap and the non-value-added activities, lean techniques have been applied. Also, the size of the billet was changed to produce maximum yield. With this, the scrap rate at the rolling mill machine has significantly decreased leading to a notable reduction in the cost of production.

Data Mining for Material Feeding Optimization of Printed Circuit Board Template Production

Improving the accuracy of material feeding for printed circuit board (PCB) template orders can reduce the overall cost for factories. In this paper, a data mining approach based on multivariate boxplot, multiple structural change model (MSCM), neighborhood component feature selection (NCFS), and artificial neural networks (ANN) was developed for the prediction of scrap rate and material feeding optimization. Scrap rate related variables were specified and 30,117 samples of the orders were exported from a PCB template production company. Multivariate boxplot was developed for outlier detection. MSCM was employed to explore the structural change of the samples that were finally partitioned into six groups. NCFS and ANN were utilized to select scrap rate related features and construct prediction models for each group of the samples, respectively. Performances of the proposed model were compared to manual feeding, ANN, and the results indicate that the approach exhibits obvious superiority to the other two methods by reducing surplus rate and supplemental feeding rate simultaneously and thereby reduces the comprehensive cost of raw material, production, logistics, inventory, disposal, and delivery tardiness compensation.

Indirect Impacts of Drastic Scrap Rate Reduction on Costs of Production Process in Precision Machining

The Lean Six Sigma (LSS) conception and other solutions in modern manufacturing and management systems have rapidly spread in precision machining in the last decades. Cost components related to quality control of precision parts are analyzed in the paper on the basis of the cutting edge LSS approach. An optimization framework intended to increase the efficiency of production process planning is introduced. The model helps in calculating economic profit, which differs from accounting profit with the extent of opportunity costs. It is found that drastic reduction of the scrap rate, though increasing customer satisfaction, is not always a proper solution due to the extent of quality cost. The applicability of the elaborated framework was tested and some promising results gained during managerial interviews are also summarized in the paper.