Autonomous Machining – Recent Advances in Process Planning and Control

Dittrich, Marc-André; Denkena, Berend; Boujnah, Haythem; Uhlich, Florian

doi:10.5604/01.3001.0013.0444

Cited by 7 publications

(8 citation statements)

References 15 publications

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“…3; − frameworks for development of "machine tool intelligence", or the intelligent machine tool, including the "Hardware-in-the-loop simulation for machine tool" architecture consisting the "physical device level" and the "virtual model", which is actually the main feature the CPS [36]; − concepts for autonomous machines, e.g. [37], requesting the same capabilities similarly to the "autonomous driving", and which could be interpreted as a form towards totally autonomous machine tools and manufacturing systems, and in the limit to the manufacturing singularity. Considering the state-of-the-art of the CPS supporting technologies and the existing deep knowledge on those technologies, the readiness of the present technologies and knowledge to improve manufacturing systems towards I n -CPS, could be evaluated as very high.…”

Section: On Readiness Of the Present Technologies And Knowledge To Upgrade Manufacturing Systems To I N -Cpsmentioning

confidence: 99%

Machine Learning in Cyber-Physical Systems and Manufacturing Singularity – It Does Not Mean Total Automation, Human is Still in the Centre: Part II – In-CPS and a View from Community on Industry 4.0 Impact on Society

Putnik¹,

Shah²,

Putnik³

et al. 2021

Journal of Machine Engineering

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In many discourses, popular as well as scientific, it is suggested that the "massive" use of Artificial Intelligence (AI), including Machine Learning (ML), and reaching the point of "singularity" through so-called Artificial General Intelligence (AGI), and Artificial Super-Intelligence (ASI), will completely exclude humans from decision making, resulting in total dominance of machines over human race. Speaking in terms of manufacturing systems, it would mean that the intelligence and total automation would be achieved (once the humans are excluded). The hypothesis presented in this paper is that there is a limit of AI/ML autonomy capacity, and more concretely, the ML algorithms will be not able to become totally autonomous and, consequently, the human role will be indispensable. In the context of the question, the authors of this paper introduce the notion of the manufacturing singularity and present an intelligent machine architecture towards the manufacturing singularity, arguing that the intelligent machine will always be human dependent. In addition, concerning the manufacturing, the human will remain in the centre of Cyber-Physical Systems (CPS) and in Industry 4.0. The methodology to support this argument is inductive, similarly to the methodology applied in a number of texts found in literature, and based on computational requirements of inductive inference based machine learning. The argumentation is supported by several experiments that demonstrate the role of human within the process of machine learning. Based on the exposed considerations, a generic architecture of intelligent CPS, with embedded ML functional modules in multiple learning loops, is proposed in order to evaluate way of use of ML functionality in the context of CPS. Similar to other papers found in literature, due to the (informal) inductive methodology applied, considering that this methodology does not provide an absolute proof in favour of, or against, the hypothesis defined, the paper represents a kind of position paper. The paper is divided into two parts. In the first part a review of argumentation from literature in favour of and against the thesis on the human role in future was presented, as well as the concept of the manufacturing singularity was introduced. Furthermore, an intelligent machine architecture towards the manufacturing singularity was proposed, arguing that the intelligent machine will be always human dependent _____________

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Section: On Readiness Of the Present Technologies And Knowledge To Upgrade Manufacturing Systems To I N -Cpsmentioning

confidence: 99%

Machine Learning in Cyber-Physical Systems and Manufacturing Singularity – It Does Not Mean Total Automation, Human is Still in the Centre: Part II – In-CPS and a View from Community on Industry 4.0 Impact on Society

Putnik¹,

Shah²,

Putnik³

et al. 2021

Journal of Machine Engineering

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show abstract

“…The restriction of process forces by adapting the feed rate is straightforward and can be implemented in a simple control loop. The disadvantage, however, is that productivity must be sacrificed for accuracy [11,12]. Furthermore, the reduction of displacement is limited since chip formation is always accompanied by forces, and feed variations are only appropriate within certain limits.…”

Section: Adaptive Machining Approaches For High Geometrical Accuracymentioning

confidence: 99%

“…In the case of offline implementation, the NC code is adapted prior to machining during process planning. This can be completed on the basis of process data from previously machined parts (series production) or iterative optimisation algorithms that utilise detailed machining simulations [11,13]. As repair cases are individual (lot size 1) and underlie variances (e.g., material inhomogeneity due to welding), this a priori information is not reliable for recontouring processes.…”

Section: Adaptive Machining Approaches For High Geometrical Accuracymentioning

confidence: 99%

“…However, abrupt changes in the process forces, due to varying feed directions and engagement conditions, are a challenge for the reactive compensation [11]. Brecher et al and Boujnah have shown that control delays can significantly limit the effect of a reactive deflection compensation when the process forces change suddenly [14,15].…”

Section: Adaptive Machining Approaches For High Geometrical Accuracymentioning

confidence: 99%

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Anticipatory Online Compensation of Tool Deflection Using a Priori Information from Process Planning

Denkena

Bergmann

Schumacher

2021

JMMP

Self Cite

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Removing excess material from build-up welding by milling is a critical step in the repair of blades from aircraft engines. This so-called recontouring is a very challenging machining task. Shape deviations often result from the deflection of tool and workpiece due to process forces. Considering the individuality of repair cases, compensation of those deflections by process force measurement and online tool path adaption is a very suitable method. However, there is one caveat to this reactive approach. Due to causality, a corrective movement, following a force variation, is always delayed by a finite reaction time. At this moment, though, the displacement has already manifested itself as a deviation in the machined surface. To overcome those limitations and to improve compensation beyond the reduction of control delays, this study proposes a novel approach of anticipatory online compensation. Flank-milling experiments with abrupt changes in the tool-workpiece engagement conditions are conducted to investigate the limitations of reactive compensation and to explore the potential of the new anticipatory approach.

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“…Fujishima et al have researched sensing interface board to solve thermal displacement compensation [6]. Dittrich et al have worked on autonomous machine tool using adaptive process control [7].…”

Section: Introductionmentioning

confidence: 99%

Positioning Error Correction of Autonomusly Movable Robot Arm

Vaher¹,

Otto²,

Riives³

2020

Journal of Machine Engineering

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Industrial robots are mainly used stationarily in one working position. SMEs often find themselves in situations where robots don't have enough work to do, and because in general, robots cannot be easily moved to another position, the efficiency of robots will decrease. This study provides a solution for this issue. The solution can be found in a robot work cell where a mobile robot deals with robot arm transportation. However, since the mobile robot is not precise enough in positioning, machine vision is used to overcome this problem, which helps the robot to position itself accurately in relation to the work object. The solution has been developed and tested successfully at an Industry 4.0 testbed.

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Autonomous Machining – Recent Advances in Process Planning and Control

Cited by 7 publications

References 15 publications

Machine Learning in Cyber-Physical Systems and Manufacturing Singularity – It Does Not Mean Total Automation, Human is Still in the Centre: Part II – In-CPS and a View from Community on Industry 4.0 Impact on Society

Machine Learning in Cyber-Physical Systems and Manufacturing Singularity – It Does Not Mean Total Automation, Human is Still in the Centre: Part II – In-CPS and a View from Community on Industry 4.0 Impact on Society

Anticipatory Online Compensation of Tool Deflection Using a Priori Information from Process Planning

Positioning Error Correction of Autonomusly Movable Robot Arm

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