Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process

Tool wear detection is a key issue for tool condition monitoring. The maximization of useful tool life is frequently related with the optimization of machining processes. This paper presents two model-based approaches for tool wear monitoring on the basis of neuro-fuzzy techniques. The use of a neuro-fuzzy hybridization to design a tool wear monitoring system is aiming at exploiting the synergy of neural networks and fuzzy logic, by combining human reasoning with learning and connectionist structure. The turning process that is a well-known machining process is selected for this case study. A four-input (i.e., time, cutting forces, vibrations and acoustic emissions signals) single-output (tool wear rate) model is designed and implemented on the basis of three neuro-fuzzy approaches (inductive, transductive and evolving neuro-fuzzy systems). The tool wear model is then used for monitoring the turning process. The comparative study demonstrates that the transductive neuro-fuzzy model provides better error-based performance indices for detecting tool wear than the inductive neuro-fuzzy model and than the evolving neuro-fuzzy model

Dynamic Control of Resource Logistics Quality to Eliminate Process Waste in Rebar Placement Work

Output-oriented resource control in the traditional planning methods is still prevalent in construction industry. It frequently causes unpredictable wastes leading to deterioration of the sequenced supply chains. On the other hand, the use of feed-forward control offers the opportunity for prevention by ensuring the high quality of necessary process resources. This paper, in turn, presents a dynamic control approach that highlights the effectiveness of feed-forward control on minimising process wastes. The field experiment in this paper presents the rebar supply and placement on an actual construction site. It aims to measure the responsiveness of pre-controlled resources to ever-changing process performance. Collected data during this field study provided the basic data for establishing statistical relationships between resource logistics quality and process performance. The research experiments found out two of the critical resource logistics: (1) Available number of workers; and (2) Distance between resource and final place. Finally the proactive control on these entities resulted in a dramatic reduction of process waste, leading to the improvement of productive work rate (31.0 to 53.3%). The main contribution of the research lies on the first-hand investigation from a very probable situation, which would benefit practical engineers and construction managers.</p