'別紙41 (課程博上(英文))Date of submission (month day, year)

Department ofStudenTID Number

D209105

07/03/2023

Naoki uchiyama

Medlanical fngineeringSupe]'ⅥSOYS

Kentaro Takagi

Mathias sebastian Halinga

PP11Canfs name

Abstract (Doctor)
Title ofThesis

Time and Energy optimalTr旦iectory Genel'ation for coverage Motion ofInduS廿ial Machines.

Approx.80o words
In the ma川lfact山ing industry, industrial machines including computer Numerical contr01 (CNC) and industrial
robots are widely used because of their accuracy, aexibility, and high production rates to meet the demands forPrecise products. covel'age motion is one ofthe dominant motions of industrial machines for manufacturing tasks
Such as miⅡing、 polishing, painting, additive manufacturing,1aser cutting, and inspection. To achieve highaccuracy, these tasks l'equire precise motion; on the other hand, the tasks are repetitive and are performed for a
10ng time,、¥hich leads to high energy consumption. Energy saving is needed given the cun'ent 、vorldwide

Situation of high energy costs, environmental effects, and the depletion of energy sources.1n addition, shortel'
Operating times are key for industrial machines to meet high production demands and guarantee pronts. To a杜ain

Shorrer operation times, machines are wpicaⅡy operated at higher velocities, thus leading to high energy
Consumption and low pl'ecision. precise motion is vital for machines to manufacture high・quality pl'oducts,
Therefore, the industrial sector is driven by a hioh need for machines with accurate motion, sh011er operation
times, and energy saving.

Coverage motion optimization is one of the methods that play a major role in reducing time and energy
Consumption and increasins the accul'acy of the machine.1t is a feasible and less cosdy method that does not

req山re replacing existing machine components with new ones or modifying the contr011aw. Motion optimization
is categorized into geometric path optimization and trajectoly generation while considering objective functions
Such as smoothness, time, cost, and energy consumption.1n the literature, many studies focused on motion

Optimization and implemented the No processes separately: simultaneous path optimization and trajectory

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generation are relatively unexplored. By integrating geometric path optimization and trajectory generation,industrialmachines can realize high・quality products with higher machine performance and enS山'e environmental
Sustainability. Due to the h喰h demand for environmental sustainabili智 and reducino production costs, theObjective 6.1nctions of time and energy consumption are considered. Moreover, time and energy are NOConaictino objectives; the trade・off between time a11d enerσy is detennined. This thesis discusses several
approaches of industrial machines covera今e motion optimization for accuracy and performance enhancement
Using trajectory (velocity) opthnization with simultaneouS 3eometric path optimization. The proposed approaches
Can be used for machine operations such as mi11ing,1aser cUせing, inspection,σIuino, and polishino that execute
Pomt・to・pomt motlons.

To enhance machine perf0血ance in tenYls oftime and energy consumption while achievino accurate motion ininduS廿ial machines, the proposed optimization approaches are described as f0110ws in 血is thesis:1ntroductoryr引れal'ks are presented in chapter l f0110wed by a literat山'e review in chaptel' 2 desc"bin0 仙e l'elated studies mmotion optimization, optjmization methods, and industrial feed drive systems. chapter 3 Presents the method forSimultaneous path and trajectory (velocity) optimizatlon used to improve machine efficiency in a coverage motion
for industrial machines by ensurino the smoothness and satisfaction ofthe machine conS廿aints. The multi・objective
Path and trajectory optimization are proposed to obtain a trade・off beNeen time and energy consumption for
Coverage motion.1hejerk limited acceleration pr061e uLAP) describeS 小e trajectory where the velocity pr0丘lesgenerated for each linear segment attain desirable velocities. The energy model ofan industrlaltwo‑axis feed drive
System is used to solve the optimization problem. The non・dominated genetic algm'ithm 11(NSGA ID generates a
Pareto front for trade・offtime and energy consumption solutions、 The simulation results ofthe proposed method
are validated through experiments using the industrial NO・axis feed drive system. Experimental results show theeffectiveness of the proposed approach where time reduction arld energy savino al'e lo.05% and 2,10%,respectively. Furthemore, the optimized path has a lower maximum error of 76.6% compared to the optimized
Path with constant commanded velociw

An energy optimization approach forthe coverage motion ofindustrialmachines, which simultaneously integrates
trajectory generation and geometric path optimization is presented in chapter 4. The modified s・C山've profile゛

describes the trajectory along a linear segment with harmonic motion employed f01' smooth jerk continuiw toenhance the motion accuracy. An energy consumption model ofthe industrialfeed drive system is used to achieve
Optimal energy. A genetic algorithm (GA) is applied f01' the optimization. Experimental validation of theSimulation l'esults is calTied out usin8 the induS廿ial two・axis feed d↑ive system. simulation and experimeηtalresults show that 小e energy saving ofthe feed drive system is achieved under machine kinematic limits that ensure
Smooth motion,、vhich is approximately 14.6% en引'gy saving compal'ed to an unoptin〕ized solution

Chapter 5 describes an approach that proposes optimal motion planning by simultaneous path and velociw
Optimization to achieve the trade・0仟 between time and energy consumption. The multi、objective optimization
model for miηimizing time and enel'gy conS山れPtion is solved by the NSGA 11. The modified s・curve pl'0負ledescribes the trajectory which ensures smooth jerk continuity. To validate the effectiveness of the pl'oposed
appl'oach, simulation and experlments are can'ied out uslng the industl'ial NO・axis feed drive system, and themotion acC山'acy is compared to that of JLAP. Experimental results revealthat the best trade・offtrajectory ofthe
Proposed approach achieves respectively B.9% and 3,5ツ0 oflime reduclion and ener旦y savin3. The mean tracking
en'or is reduced by 162% and 14.9ψ'o for the x and y axes、 respectively, compared to the jLAP. Last, chapter 6
Presents the concluding remarks ofthis thesis and prospective future 、uorks